# Maisa AI > Beyond task automation with Maisa Maisa automates end-to-end processes with accountable Digital Workers Maisa delivers business efficiency by automating end-to-end processes with traceability, hallucination-resistance and governance, in the most regulated --- ## Pages - [Footer Maisa](https://maisa.krai.app/footer-maisa/) - [Home](https://maisa.krai.app/) - [Digital Workers](https://maisa.krai.app/digital-workers/) - [Introducing Vinci KPU](https://maisa.krai.app/research/) - [Manifesto](https://maisa.krai.app/about-us/) - [Careers](https://maisa.krai.app/careers/) - [Agentic Insights](https://maisa.krai.app/agentic-insights/) - [Contact](https://maisa.krai.app/contact/) --- ## Posts - [What is business process automation (BPA)?](https://maisa.krai.app/agentic-insights/business-process-automation/): What is business process automation? Business process automation is the process of using AI technology to automate repetitive manual tasks... - [Agentic AI vs RPA: differences, similarities, and examples](https://maisa.krai.app/agentic-insights/agentic-ai-vs-rpa/): Enterprises are trying to automate everything today, and the technical landscape is being changed by Agentic AI and RPA (Robotic... - [Maisa raises $25M seed investment](https://maisa.krai.app/agentic-insights/maisa-raises-25m-seed-investment/): Less than a year after our $5M pre-seed, we’re back with news that validates our approach to making AI accountable.... - [Rising star of agentic AI delivering trustworthy ‘digital workers’ raises $25M from Creandum and Forgepoint](https://maisa.krai.app/agentic-insights/maisa-raises-25m-from-creandum-and-forgepoint/): Maisa, a dual US/Europe-headquartered company, provides fully auditable ‘digital workers’ to enterprises New Maisa Studio platform, unveiled today, allows non-technical... - [Maisa Studio on AWS Marketplace: removing barriers to AI adoption](https://maisa.krai.app/agentic-insights/maisa-studio-on-aws/): Despite the bottlenecks and challenges, agentic AI is becoming a reality for business processes. As part of this movement, AWS... - [CLATTER: Academic Validation for Our Maisa AI Hallucination Detection Strategy](https://maisa.krai.app/agentic-insights/clatter-hallucination-detection/): The recent publication of “CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection ” couldn’t have come at a better time. This... - [HALP: Maisa’s breakthrough in delivering reliability for enterprise automation](https://maisa.krai.app/agentic-insights/halp/): AI has made headlines for its potential to transform work, but inside most organizations, turning that potential into reliable automation... - [Why we built Maisa this way: scientific proof we're on the right track](https://maisa.krai.app/agentic-insights/science-behind-maisa-architecture/): The architecture behind Maisa is the result of deliberate choices informed by research. A growing body of work has made... - [Advancing our vision for Accountable AI together with Microsoft](https://maisa.krai.app/agentic-insights/microsoft-partnership/): We are excited to announce that Maisa has been selected by Microsoft to become part of the Microsoft for Startups... - [Black Box AI. How can we trust what we can’t see?](https://maisa.krai.app/agentic-insights/black-box-ai/): Artificial Intelligence is transforming critical decisions that affect businesses and people’s lives, from approving loans and hiring candidates to medical... - [Making AI accountable: Maisa raises pre-seed round](https://maisa.krai.app/agentic-insights/maisa-raises-pre-seed-round/): Back in March, we introduced the first version of the KPU, setting new benchmarks that surpassed leading models. Since then,... - [Introducing Vinci KPU](https://maisa.krai.app/agentic-insights/vinci-kpu/): Introduction On March 14, 2024, at Maisa AI, we announced our AI system to the world, enabling users to build... - [Hello world](https://maisa.krai.app/agentic-insights/hello-world/): Hello World In recent periods, the community has observed an almost exponential enhancement in the proficiency of Artificial Intelligence, notably... --- ## Use cases - [Power of Attorney](https://maisa.krai.app/banking-financial-services/power-of-attorney-verification/): After struggling with low automation accuracy and heavy manual review in loan applications, this leading European bank used Maisa to... - [Auto-Loan Processing](https://maisa.krai.app/banking-financial-services/auto-loan-processing/): After struggling with low automation accuracy and heavy manual review in loan applications, this leading European bank used Maisa to... - [Trade Finance](https://maisa.krai.app/banking-financial-services/trade-finance-processing/): A leading global bank’s Corporate and Investment Banking division transformed one of its hardest processes to automate with traditional technology.... - [O2C & P2P](https://maisa.krai.app/insurance/o2c-p2p/): By replacing rigid RPA and unreliable AI tools with Maisa’s hallucination-resistant Digital Workers, this enterprise transformed billing, payments, and financial... - [Equity Research](https://maisa.krai.app/banking-financial-services/equity-research/): A leading investment management group relied on manual equity research workflows that slowed decision-making and limited scalability. Broker reports were... - [New Client Onboarding](https://maisa.krai.app/banking-financial-services/new-client-onboarding/): A global financial institution modernized its client onboarding process using Maisa Digital Workers. The bank previously relied on manual document... - [RFP Response Generation](https://maisa.krai.app/banking-financial-services/rfp-response-generation/): A leading financial institution reimagined its RFP management process with Maisa Digital Workers. Manual response preparation was replaced by an... - [Subpoena Request Intake](https://maisa.krai.app/banking-financial-services/subpoenas-requests-intake/): A leading financial institution restructured how it manages subpoenas and legal information requests. The previous manual process required coordination between... - [Fund Performance Reporting](https://maisa.krai.app/banking-financial-services/fund-performance-reporting/): A global investment bank redesigned its fund performance reporting process with Maisa Digital Workers. What once required analysts to manually... - [Adverse Media Screening Alerts Review](https://maisa.krai.app/banking-financial-services/adverse-media-screening-alerts/): A global corporate and investment bank implemented Maisa Digital Workers to automate the review of adverse media alerts within its... - [Correspondent Account Transaction Reconciliation](https://maisa.krai.app/banking-financial-services/correspondent-account-transaction-reconciliation/): A major financial services provider transformed its reconciliation operations with Maisa Digital Workers. The company’s teams previously spent significant time... - [Deceased Account Processing](https://maisa.krai.app/banking-financial-services/deceased-account-processing/): A leading retail bank transformed the management of deceased client accounts using Maisa Digital Workers. The process of verifying death... - [Tax Withholding Reconciliation](https://maisa.krai.app/banking-financial-services/tax-withholding-reconciliation/): A European investment firm simplified its annual tax withholding reconciliation process with Maisa Digital Workers. The institution previously relied on... --- # # Detailed Content ## Pages - Published: 2025-11-18 - Modified: 2025-12-05 - URL: https://maisa.krai.app/ - Translation Priorities: Optional Maisa AI Maisa AI Password   Remember Me --- - Published: 2025-04-11 - Modified: 2025-12-10 - URL: https://maisa.krai.app/digital-workers/ - Translation Priorities: Optional Maisa AI Maisa AI Password   Remember Me --- - Published: 2024-11-26 - Modified: 2025-04-15 - URL: https://maisa.krai.app/research/ - Translation Priorities: Optional Introducing Vinci KPU - Maisa AI Maisa AIProductMaisa StudioBuild a Digital WorkerManage your Digital WorkforceTrust the outcomeSolutionsAgentic InsightsResearchCareersSchedule a demo November 26, 2024Introducing Vinci KPU SOC 2 type II * ISO 27001 * GDPR & AI EU Act ProductBuild a Digital WorkerManage your Digital WorkforceTrust the outcomeSolutionsBanking & Financial ServicesInsuranceResourcesUse CasesAgentic InsightsCompanyManifestoContactWall of loveCareersLegalPrivacy PolicyCookie PolicyLegal NoticeTerms & Conditions Backed by * Audit in progressIntroductionOn March 14, 2024, at Maisa AI, we announced our AI system to the world, enabling users to build AI/LLM-based solutions without worrying about the inherent issues of these models (such as hallucinations, being up-to-date, or context window constraints) thanks to our innovative architecture known as the Knowledge Processing Unit (KPU).In addition to user feedback, the benchmarks on which we evaluated our system demonstrated its power, achieving state-of-the-art results in several of them, such as MATH, GSM8k, DROP, and BBH— in some cases, clearly surpassing the top LLMs of the time.Vinci KPUSince March, we have been proactively addressing inference-time compute limitations and scalability requirements, paving the way for seamless integration with tools and continuous learning.Today, we are excited to announce that we have evolved the project we launched in March and are pleased to present the second version of our KPU, known as Vinci KPU.This version matches and even surpasses leading LLMs, such as the new Claude Sonnet 3.5 and OpenAI’s o1, on challenging benchmarks like GPQA Diamond, MATH, HumanEval, and ProcBench.What’s new on the Vinci KPU (v2)?Before discussing the updates in v2, let’s do a quick recap of the v1 architecture.KPU OS ArchitectureOur architecture consists of three main components: the Reasoning Engine, which orchestrates the system’s problem-solving capabilities; the Execution Engine, which processes and executes instructions; and the Virtual Context Window, which manages information flow and memory.In this second version, we’ve made significant improvements across all components:Reasoning Engine Improvement: We have enhanced the KPU kernel, furthering our commitment to positioning the LLM as the intelligent core of our OS Architecture. This advancement allows for more sophisticated reasoning and better orchestration of system components.Execution Engine Enhancements: We have successfully integrated cutting-edge test-time compute techniques and made the execution engine more robust, secure, and scalable. This ensures reliable performance while maintaining high-security standards for tool integration and external connections.Virtual Context Window Refinements: We have refined our Virtual Context Window through improved metadata creation and LLM-friendly indexing. This enhancement optimizes how information flows through the system and lays the groundwork for unlimited context and continuous learning capabilities.KPU Architecture BenefitsWhat makes these results particularly significant is that they’re achieved by our KPU OS, acting as a reasoning engine, which focuses on understanding the path to solutions rather than providing answers. As main benefits, we can highlight:Model Agnostic Architecture (Better base models, better performance)Full multi-step traceability:configurable observability: Debug mode, visual representation, et.al.Provides better human-in-the-loop and over-the-loop control.Mitigate, almost fully eliminates, hallucinations:While this approach minimizes AI-generated inaccuracies, it may still encounter issues like errors in tool execution, incorrect data sources, or suboptimal approaches to solving the problem.Lower Latency to resolve problems... --- - Published: 2024-10-25 - Modified: 2025-10-09 - URL: https://maisa.krai.app/about-us/ - Translation Priorities: Optional Manifesto - Maisa AI Maisa AIProductMaisa StudioBuild a Digital WorkerManage your Digital WorkforceTrust the outcomeSolutionsAgentic InsightsResearchCareersSchedule a demoManifestoComputing a better future for AIIt’s time again to make an important decision.Do you flip a coin, roll the dice, or ask the Magic 8-Ball? Or do you return to first principles, consider everything you’ve learned, and reason your way to a solution?We know what we would do. Which is why it feels a little strange that the services we’re increasingly looking to for answers are built around probabilities. Yes, we’re talking about AI: our latest and greatest technological innovation. For acts of creativity, AI’s fuzzy logic can be remarkable at helping us imagine new solutions. But in the complex world of business, we need a lot more accountability. We need airtight, traceable processes for getting to answers… as much as we need the answers themselves. We demand a new kind of common sense made for our new AI era.In short, we need evidence  to justify important decisions like who gets a loan, which insurance claims are denied, what drugs get researched, or deciding who gets laid off. And with today’s AI, this evidence is abstracted into oblivion… and the risk of hallucinations threaten to make it all useless anyway.And so, less than 6% of corporations are actively using AI to do anything more than create question and answer bots. To make AI truly invaluable to businesses, we need a new way of thinking. Throughout history, humans have developed computational systems, formal logic, and scientific methods to verify our thinking. Can we do the same for AI? We believe the solution isn’t training larger models, illuminating their chain of thought, or implementing RAG systems, which are all still based on the same fundamental issues. So, we believe the path forward lies in a new kind of computing system that combines the creative problem-solving capabilities of AI with the determinism of traditional computational systems. An AI computer that shows its chain of truth, not just its chain of thought.In this world, AIs can be trusted to act as intelligent problem solvers executing tasks, not mysterious oracles giving inscrutable answers. To us, this is the key that will take AI into its next age of utility. When AI evolves from a black box… to an open book. Introducing MaisaMaisa is not an AI or an LLM, but a system for making them work better.It’s a new kind of automation tool for enterprises, data teams, builders, and tinkerers who are tired of chasing ghosts and trying to divine the meaning behind AI’s answers. A self-learning intelligence built on fact, not fiction. With Maisa, you can create accountable AI agents you can plug in to any part of your business. Define your desired outcome, set your goals, give them access to company knowledge and tools, and instruct them using natural language. Then, they approach problems by navigating all your knowledge, not just retrieving it, and processing information with a clear, auditable logic that’s easily traceable.Think of Maisa as the next... --- - Published: 2024-10-25 - Modified: 2025-07-16 - URL: https://maisa.krai.app/careers/ - Translation Priorities: Optional Careers - Maisa AI Maisa AIProductMaisa StudioBuild a Digital WorkerManage your Digital WorkforceTrust the outcomeSolutionsAgentic InsightsResearchCareersSchedule a demoCareersIf you’re passionate about Technology, AI, Humans and the Future of Work, we’d love to hear from you.AI Research & DevelopmentClient ServicesEngineeringMarketingOperationsPeopleProductSalesAI Research & DevelopmentClient ServicesEngineeringMarketingOperationsPeopleProductSales SOC 2 type II * ISO 27001 * GDPR & AI EU Act ProductBuild a Digital WorkerManage your Digital WorkforceTrust the outcomeSolutionsBanking & Financial ServicesInsuranceResourcesUse CasesAgentic InsightsCompanyManifestoContactWall of loveCareersLegalPrivacy PolicyCookie PolicyLegal NoticeTerms & Conditions Backed by * Audit in progress --- - Published: 2024-10-25 - Modified: 2025-03-20 - URL: https://maisa.krai.app/contact/ - Translation Priorities: Optional Contact - Maisa AI Maisa AIProductMaisa StudioBuild a Digital WorkerManage your Digital WorkforceTrust the outcomeSolutionsAgentic InsightsResearchCareersSchedule a demoContact SOC 2 type II * ISO 27001 * GDPR & AI EU Act ProductBuild a Digital WorkerManage your Digital WorkforceTrust the outcomeSolutionsBanking & Financial ServicesInsuranceResourcesUse CasesAgentic InsightsCompanyManifestoContactWall of loveCareersLegalPrivacy PolicyCookie PolicyLegal NoticeTerms & Conditions Backed by * Audit in progress SOC 2 type II * ISO 27001 * GDPR & AI EU Act ProductBuild a Digital WorkerManage your Digital WorkforceTrust the outcomeSolutionsBanking & Financial ServicesInsuranceResourcesUse CasesAgentic InsightsCompanyManifestoContactWall of loveCareersLegalPrivacy PolicyCookie PolicyLegal NoticeTerms & Conditions Backed by * Audit in progressNameLast nameCorporate emailWebsiteCompany sizeSize11–50 employees51–200 employees201–500 employees501–1,000 employees1,001–5,000 employees5,001–10,000 employees10,001+ employeesPhoneIndustryI am reaching out toBook a demoLearn about ProductsTechnical SupportDiscuss PartnershipsIn short,How did you hear about us?Select an optionGoogle SearchLinkedInOnline Media (News, Podcast, Articles)Event or ConferenceReferralOtherI have read and accept the privacy policy. --- --- ## Posts - Published: 2025-11-11 - Modified: 2025-11-12 - URL: https://maisa.krai.app/agentic-insights/business-process-automation/ - Categories: AI in the Enterprise - Translation Priorities: Optional What is business process automation? Business process automation is the process of using AI technology to automate repetitive manual tasks and processes while maintaining efficiency, productivity, and accuracy. The goal of BPA is to streamline daily operations and allow businesses to operate smoothly. Automation of business processes can help companies in almost every field to achieve higher effectiveness and improve customer satisfaction and experience. Why is BPA important for the companies? Business process automation is not just the next trending topic. It is the new reality - adaptation to technology that will exist in the world, changing the old manual labor into new digital processes. Automation workflow helps in every step of the BPA by making it more transparent and accountable, reducing errors, improving turnaround times, and cutting costs. The 5 Types of Business Process Automation Task Automation This is the most basic type of business process automation tool. It automates repetitive, straight forward tasks that are time consuming and require no decision making in the process What is task automation? Task automation uses no-code platforms or software like scripts to perform repetitive rule-based tasks. Task automation fields and examples Email management - You can use it to manage your emails by filtering. tagging and organizing them into specific folders Finance - automatic reporting, invoicing and overdue reminders Data management - moving information from a document to a database, or comparing information in different databases and flagging inconsistencies Customer service - instant replay to common questions or assign task based on certain criteria Task automation advantages Reduced errors - tasks are performed following strictly the process removing the manual labor and copy/pasting Increased productivity - tasks are processed 24/7, no coffee breaks or distractions Cost savings - improved performance and less errors mean lower corporate operational costs Improved job satisfaction - employees can finally focus on meaningful tasks and move away from repetitive work Workflow Automation Unlike task automation where individual tasks are executed, workflow automation deals with a chain of tasks to complete a business process based on predefined set of rules. What is workflow automation? The workflow automation makes sure the a set of tasks are completed between human and software with minimal or no human interaction. Each task following the next logical one in the process and only stopping when external approval, consent or additional information is required. Workflow automation fields and examples Sales - user fills out “Contact us” form, the workflow instantly checks for some additional information that is needed (ex. : company size, location) and assigns to “lead” to the correct sales personal and updates the follow-up tasks in the CRM Customer service - a task comes in, it's assigned to the right customer support representative but it's not completed in expected timeframe, a reminder is triggers and send to senior support specialist to ensure no complain is left unsolved IT Help desk - a ticket comes in, based on the category (ex. “Hardware problem”, “Software Issue”) it is assigned to the right specialist... --- - Published: 2025-10-24 - Modified: 2025-11-12 - URL: https://maisa.krai.app/agentic-insights/agentic-ai-vs-rpa/ - Categories: AI in the Enterprise - Translation Priorities: Optional Enterprises are trying to automate everything today, and the technical landscape is being changed by Agentic AI and RPA (Robotic Process Automation). RPA works with exact, predefined instructions. While Agentic AI can aim to reach this goal, it plans and executes a sequence of tasks with minimal human intervention. The potential of both approaches is huge. Many industries, including finance, insurance, healthcare, and customer service. These are already adopting Agentic AI in their internal systems to solve complex problems. Both are trying to automate repetitive work and improve efficiency, but they achieve it in very different ways. What is Agentic AI? Agentic AI refers to an autonomous AI system. It can operate independently to achieve complex, long-term goals without constant human involvement. Agentic AI can understand its environment, reason, plan, act, and learn from its actions to become more efficient. How it works Goal intake: The AI collects the information it needs to understand the objective and explain it in simple language Reasoning: The AI, powered by large language models (LLMs), analyzes data, plans all steps, and chooses the actions. Tool & data access: Connects with external APIs, software, and databases to collect information Orchestration: Breaks down complex tasks into smaller ones for easier execution. And planning their sequence to achieve the desired outcome Action: Execute the task order, working along internal and external systems, tools, and even other AI agents Strengths Agents manage end-to-end workflows. Adjusts constantly when processes shift or data changes. Scales to support complex, multi-step operations. Removing humans from repetitive manual work. Limitations Errors in one step can move through the process. Needs clear guardrails and human oversight to operate safely. Highly dependent on quality data. Requires transparency so organizations can track and understand decisions. What is RPA (Robotic Process Automation)? Robotic Process Automation (RPA) uses software bots to act like a human on a computer. It follows a predefined script-based path to complete tasks when working with digital systems. RPA systems allow enterprises to reduce errors and speed up operations while maintaining quality How it works Process mapping: Every click, action, rule, and sequence itself is programmed in advance by IT teams. Execution: Bots replicate these steps across applications with speed, accuracy, and consistency. Scope: Ideal for large-scale, well-organized, and repetitive processes. For example, data entry, form filling, and transferring information between systems. Maintenance: Any change in process or interface requires updates to the scripts. Strengths Reliable for high-volume, repetitive, structured tasks. Reduces human error and increases speed in routine business processes. Widely adopted in enterprise operations like finance, HR, and back-office functions. Limitations Easily breaks when processes or systems change. Unable to process unstructured or dubious data. Dependent on IT teams for setup and maintenance. Limited scalability for complex or changing workflows. What are the differences between Agentic AI vs RPA? The two approaches reflect different philosophies of automation: Agentic AI on adaptive goal achievement, while RPA focuses on the strict execution of predefined steps. Aspect RPA Agentic AI Primary role Executes predefined tasks... --- - Published: 2025-08-28 - Modified: 2025-08-28 - URL: https://maisa.krai.app/agentic-insights/maisa-raises-25m-seed-investment/ - Categories: News - Translation Priorities: Optional Less than a year after our $5M pre-seed, we're back with news that validates our approach to making AI accountable. Our technology has matured, we've moved from pilots to production deployments, and our team has grown across two continents. Yet the core challenge we set out to solve remains unchanged: enterprises still struggle with AI they can't trust or trace. This new funding proves that the market is ready for a different approach, one where reliability comes first. The AI adoption reality The numbers tell a stark story. Despite the AI hype, 87% of enterprise AI projects never make it past proof-of-concept. Only 4% deliver meaningful value. Companies pour resources into pilots that impress in demos but fail in production, where accuracy and accountability matter. Enterprises need AI they can verify and AI their business teams can build and scale. They need full visibility into how decisions are made and the ability to create agentic automation without technical complexity. Current AI offers neither, keeping it stuck in experiments while real work stays manual. Digital Workers in action Business experts teach Digital Workers through natural language, just like onboarding a new colleague. They describe the process, the decisions to make, and the goals to achieve. The Digital Worker learns from this guidance and real-world feedback, creating automation that mirrors how work actually gets done. Fueling our mission This funding enables us to expand our engineering, sales, and customer support teams across Europe and North America while advancing our R&D efforts. We're building the infrastructure to help more enterprises deploy Digital Workers at scale. Every technical advancement and every new hire serves our core mission: making AI truly accountable for business-critical work. With these resources, we can help more organizations move from AI experiments to AI that executes with full transparency. Our partners in accountable AI We're honored to partner with investors who recognize that AI must be built on accountability. Maisa is solving one of the toughest challenges in AI: making it reliable and safe to use in mission-critical business operations. With a string of top global companies already using the technology, we are extremely confident of seeing significant growth at scale. Peter Specht, General Partner from Creandum Maisa’s platform is purpose-built for compliance-conscious industries like finance, where decisions must be traceable and outcomes consistent. The company’s early success demonstrates there’s real market pull for AI done the right way and Studio, launched today, will make it easy for anyone in an organisation to use digital workers quickly and effectively. Alberto Yepez, from Forgepoint Capital International Come join us If you're interested in building the future of accountable AI and helping enterprises transform how they work, visit our careers page. --- - Published: 2025-08-28 - Modified: 2025-10-09 - URL: https://maisa.krai.app/agentic-insights/maisa-raises-25m-from-creandum-and-forgepoint/ - Categories: News - Translation Priorities: Optional Maisa, a dual US/Europe-headquartered company, provides fully auditable ‘digital workers’ to enterprises New Maisa Studio platform, unveiled today, allows non-technical staff - ‘citizen developers’ - to create AI agents using natural language without developer support Maisa Studio being piloted by global financial institutions and enterprises to automate complex workflows in compliance, finance and operations Creandum, early investor in Spotify, Klarna and Lovable, leads round, joined by Forgepoint Capital International via its European JV with Banco Santander. US funds NFX and Village Global also participated, doubling down on their investment last year Follows Maisa’s recent inclusion and naming in influential Gartner Hype Cycle reports alongside Google, Amazon and Salesforce as a top AI vendor in the world 28 August 2025, Valencia, Spain, and San Francisco, USA – Maisa, a leader in developing hallucination-resistant AI agents, today announces a $25 million seed investment led by Creandum, with participation from Forgepoint Capital, via its European joint venture with Banco Santander. The news comes only a few months after the company raised $5m pre-seed in December 2024 from NFX and Village Global, which is backed by Mark Zuckerberg, Jeff Bezos and Eric Schmidt. Both NFX and Village Global followed on and participated in today’s round. The new funding will support hiring across AI R&D, engineering, sales and customer success, as well as expanding Maisa’s growing footprint in Europe and North America. Alongside the fundraise, the company is launching Maisa Studio, an agentic process automation platform which enables ‘citizen developers’ - users who are experts in their business field but without an IT background - to deploy powerful and fully auditable AI ‘digital workers’ trained through natural language. The platform is being piloted at scale inside global banks, car manufacturers and energy companies, among others, to run multi-step, complex and compliance-sensitive workflows with full traceability and reliability. It allows non-technical staff to onboard digital workers with the ease of onboarding new colleagues. They can easily and quickly install a digital worker to perform complex, knowledge-intensive processes with full transparency, from evaluating risk and reconciling transactions to monitoring supply chain disruptions - any task that would traditionally demand extensive hours of repetitive human effort for poorly equated output. The digital workers require no dataset or developers, and users only need to write a series of natural language commands into the platform. The digital workers learn on the job by doing, through a method Maisa calls ‘HALP’ (human-augmented LLM processing), which is a fast and enterprise ready way to train digital workers. Maisa was founded in 2024 by CEO David Villalón and CSO Manuel Romero, two leaders in applied and foundational AI. Villalón was formerly Chief AI Officer at Clibrain and Director of Product at Voicemod. Romero is one of the top HuggingFace contributors globally, with more than 700 open-source models and 15 million downloads per month. Today’s raise comes less than a year since the company raised $5m pre-seed in December 2024 from NFX and Village Global, which is backed by Mark Zuckerberg, Jeff Bezos and... --- - Published: 2025-07-23 - Modified: 2025-09-26 - URL: https://maisa.krai.app/agentic-insights/maisa-studio-on-aws/ - Categories: News - Translation Priorities: Optional Despite the bottlenecks and challenges, agentic AI is becoming a reality for business processes. As part of this movement, AWS has introduced a new category in their marketplace: AI Agents. We're proud to be among the pioneering agentic AI players selected for this launch This helps enterprises access the technology through trusted channels they already use. The same procurement and security frameworks that govern other business software now apply to AI agents. The AI Agents landscape in enterprise AI agents mark a shift from assistants that respond to prompts to systems that pursue goals autonomously. They decide how to complete tasks, use tools, and adapt based on what they encounter. But enterprises face real barriers. AI agents hallucinate facts, make unexplainable decisions, and operate as black boxes. When you can't trace why an agent did something, you can't trust it with critical processes. This is why most businesses stay limited to basic chatbots. Which brings us to Digital Workers. They're AI agents built for business processes where every action is traceable and every decision explainable. Through Maisa Studio on AWS Marketplace, enterprises can now access AI that works with full visibility: you see the reasoning, the steps taken, and the tools used. AI designed for how business actually operates. Removing the barriers to AI adoption We joined AWS Marketplace as one of the first in this new AI Agents category to make Digital Workers accessible through the channels enterprises already trust. Too many AI projects stall in procurement and security reviews, keeping valuable technology out of reach for teams that need it. AWS Marketplace changes these realities. Security and compliance reviews are already complete. Procurement happens through existing AWS agreements, eliminating new vendor processes. We want enterprises to skip the bureaucracy and compliance marathons and go straight to the business decision: what do we automate first? Making AI accountable This advances our core mission: making AI truly accountable for business-critical work. Through AWS Marketplace, enterprises can access Digital Workers that combine autonomous capabilities with the transparency business demands. We're helping organizations move beyond AI experiments to real implementation, where AI doesn't just assist but executes work with full oversight. The future of business isn't about choosing between powerful AI and trusted systems. It's about having both. Buy with AWS --- - Published: 2025-06-20 - Modified: 2025-07-31 - URL: https://maisa.krai.app/agentic-insights/clatter-hallucination-detection/ - Categories: Research and Tech - Translation Priorities: Optional The recent publication of "CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection " couldn't have come at a better time. This groundbreaking research not only addresses one of AI's most critical challenges but also provides academic backing for the architectural decisions we've made in developing Maisa AI. The Hallucination Problem: More Critical Than Ever Analysts estimated that chatbots hallucinate as much as 27% of the time, with factual errors present in 46% of generated texts, making hallucination detection a make-or-break factor for AI deployment in production environments. The stakes are particularly high in domains like healthcare, legal services, and financial advice, where incorrect information can have severe consequences. Traditional hallucination detection methods have largely relied on simple fact-checking or confidence scoring, but these approaches often miss subtle fabrications or fail when dealing with complex, multi-step reasoning scenarios. CLATTER's Innovative Divide-and-Conquer Approach The CLATTER methodology introduces a systematic four-step process that mirrors what we've independently developed for Maisa AI: 1. Decomposition: Breaking Down Complexity The method decomposes generated text into factual claims, attributes these to source evidence, transforming complex outputs into manageable, verifiable units. This granular approach allows for precise identification of problematic content rather than broad-brush rejection of entire responses. 2. Knowledge Base Verification Each extracted claim undergoes rigorous verification against a trusted knowledge base. The system determines whether there's entailment (support), contradiction, or insufficient evidence for each claim. This step is crucial for maintaining factual accuracy while avoiding over-conservative filtering. 3. Parallel Processing Architecture One of CLATTER's most elegant features is its ability to process multiple claims simultaneously. This parallel verification approach significantly improves efficiency while maintaining accuracy, especially important for real-time applications. 4. Intelligent Reconstruction The final step involves feeding the original input, extracted claims, and their verification status back to the model for response refinement. This creates a self-healing mechanism that improves output quality without requiring complete regeneration. How CLATTER Validates Our Maisa AI Architecture Knowledge Base Integration Our decision to build Maisa AI with an integrated knowledge base consultation system inside the KPU aligns perfectly with CLATTER's approach. When our orchestrator determines it's necessary, we can query our knowledge base to verify claims in real-time, providing the same systematic verification that CLATTER demonstrates is essential. Step-by-Step Verification Pipeline The parallel claim checking that CLATTER employs mirrors our internal verification processes. We've implemented similar step-by-step checking mechanisms that allow us to identify and correct potential hallucinations before they reach the end outcome. Multi-Hop Reasoning Support CLATTER specifically addresses long-form, multi-hop question answering scenarios, exactly the type of complex reasoning tasks that Maisa AI is designed to handle. The research validates our architectural decision to implement comprehensive verification at each reasoning step rather than only at the final output. Self-Healing Capabilities Perhaps most importantly, CLATTER's reconstruction phase aligns with our self-healing approach to AI reliability. Rather than simply flagging problems, the system actively works to improve outputs based on verification feedback. Beyond Academic Theory: Real-World Implementation What makes CLATTER particularly valuable isn't just its theoretical framework, but its practical... --- - Published: 2025-06-04 - Modified: 2025-10-30 - URL: https://maisa.krai.app/agentic-insights/halp/ - Translation Priorities: Optional AI has made headlines for its potential to transform work, but inside most organizations, turning that potential into reliable automation remains a challenge. Business teams aren’t looking for impressive demos or clever assistants. They need AI systems they can trust to follow business logic, respect context, and stay consistent as things evolve. Yet the methods used to build these systems today often work against that goal. What if reliability didn’t depend on perfect data or complex training pipelines? What if AI could learn by doing, through real tasks and real feedback, inside the business itself? The limits of training methods for enterprises Human-in-the-loop (HITL) methods are used to make AI systems more accurate and aligned with human expectations. They rely on human feedback such as labeled examples, corrections, and supervision to teach models how to behave. This approach has been key to training today’s most advanced language models. Systems like GPT and Claude were refined through large-scale HITL processes, helping them perform well across a wide range of generic tasks. But when it comes to enterprise use, this method starts to show its limits. Business processes are specific, tools are unique, and rules change often. Applying HITL in this context means building custom datasets, coordinating technical teams, and retraining models just to keep systems functional. It is slow, expensive, and difficult to scale. For teams that need automation to adapt with the business, this approach becomes a bottleneck. Business logic should not have to wait for model retraining. Human-Augmented LLM Processing (HALP). A new way to teach AI What if AI could learn through real work, just like a new team member? HALP changes how we build reliable systems. Instead of relying on retraining cycles or complex setup, it enables AI to learn by doing. HALP stands for Human-Augmented LLM Processing, and it powers Digital Workers that learn directly from the way work happens. Configuring a Digital Worker through natural language Teams explain the task, walk through the logic, and share the tools they use. The system picks up that knowledge through natural interaction, without prompt engineering or rigid rules. Unlike traditional methods, HALP doesn't require labeled datasets or offline feedback loops. The learning happens in context, during real tasks. The system stays aligned with how the business actually works, even as things evolve. The reliability enterprises have been missing HALP unlocks what enterprise automation has long lacked: reliability in real work. Fast setup with less effort Digital Workers don’t need large datasets or precise prompts. They start from natural interaction and real context. Teams can build and adjust them without relying on IT or external consultants. Lower cost to launch and maintain Less time is spent configuring, correcting, or integrating. Business users can stay involved, reducing handoffs and rework. Scales across teams and processes Digital Workers adapt to different workflows. Logic can be reused, updated, and shared as the business evolves. Trust built into every step Each decision is traceable to a rule or piece of business logic. There... --- - Published: 2025-04-24 - Modified: 2025-10-09 - URL: https://maisa.krai.app/agentic-insights/science-behind-maisa-architecture/ - Translation Priorities: Optional The architecture behind Maisa is the result of deliberate choices informed by research. A growing body of work has made it clear: while large language models offer impressive generative power, they fall short in several critical areas when used in isolation. Maisa’s strategic design responds directly to those gaps. Below is an overview of how each component is supported by scientific insight. Bridging reasoning and execution ReAct: Synergizing Reasoning and Acting in Language Models ReAct remains one of the most important foundations in the evolution of agentic AI. It introduced a core loop: reason, act, observe and repeat. This core helped reframe LLMs as active decision-makers rather than passive responders. This concept triggered the shift toward treating AI systems as agents capable of planning, adapting, and executing tasks in dynamic environments. While it's widely implemented today, its influence remains central to the architecture of AI systems designed for real-world decision-making. Hallucination is Inevitable: An Innate Limitation of Large Language Models LLMs are prone to fluent but inaccurate output. This limitation stems from architecture, not data. Steering LLMs Between Code Execution and Textual Reasoning Executable Code Actions Elicit Better LLM Agents Code to Think, Think to Code Chain of Code: Reasoning with a Language Model-Augmented Code Emulator These studies confirm the advantage of pairing LLMs with code execution: performance improves through verifiable logic, runtime validation, and structured task decomposition. While visible reasoning chains can appear coherent, they often mask logical gaps. Reliability increases when reasoning is grounded in execution, where each step is tested, not just described. Chain-of-Thought Reasoning in the Wild Is Not Always Faithful In fact, this other paper highlights and confirms that exposing reasoning chains through techniques like Chain-of-Thought prompting does not ensure factual accuracy. The presence of a detailed explanation can create a false sense of confidence, even when the underlying logic is flawed or unsupported. The model may appear to reason more deeply, but the steps often serve as post-hoc rationalizations rather than evidence-based logic. This distinction is critical: coherence doesn’t equal truth. Executable validation remains essential for ensuring that each step reflects actual reasoning. How this shapes Maisa: The research outlined in these papers affirms a path we had already taken. Each finding reinforces architectural choices we made early on. Confirming that the principles behind Maisa’s design are supported by emerging scientific consensus and designed to operate under real-world enterprise conditions. At the core is a reasoning engine structured around iterative decision-making loops, where each action is informed by observation and continuously adjusted until a defined goal is met. Instead of following fixed instructions, the system adapts continuously as conditions change and new inputs emerge. To support this, Maisa integrates a live code interpreter within the reasoning process, enabling the system to test assumptions, validate outcomes, and apply logical operations as part of its workflow. Rather than relying on text-based reasoning alone, every step can be executed, verified, and corrected in real time. Code is fundamental, not an add-on. Actions are embedded in executable logic... --- - Published: 2025-04-07 - Modified: 2025-07-03 - URL: https://maisa.krai.app/agentic-insights/microsoft-partnership/ - Translation Priorities: Optional We are excited to announce that Maisa has been selected by Microsoft to become part of the Microsoft for Startups Founders Hub and recognized as a Microsoft Strategic Partner. Pushing forward our vision for Digital Workers This partnership reinforces our commitment to developing Accountable AI and Digital Workers that automate complex business processes. By tapping into Microsoft's extensive Azure infrastructure and specialized resources, Maisa gains powerful new capabilities to enhance the reliability, traceability, and performance of our AI technology. Strengthening Accountable AI This collaboration with Microsoft supports our goal of building trustworthy, transparent AI solutions. We continue working to advance AI systems that companies can rely on to automate processes, delegating to accountable Digital Workers. --- - Published: 2025-04-01 - Modified: 2025-10-28 - URL: https://maisa.krai.app/agentic-insights/black-box-ai/ - Translation Priorities: Optional Artificial Intelligence is transforming critical decisions that affect businesses and people's lives, from approving loans and hiring candidates to medical diagnoses. Yet, many AI systems operate as "black boxes," providing outcomes without revealing how they were reached. This raises a fundamental question: how can we trust decisions made by systems whose reasoning we can't clearly understand? AI models learn from vast amounts of data, predicting outcomes without transparent, step-by-step logic. While their capabilities are impressive, this hidden reasoning creates uncertainty and potential risks. For businesses, relying on AI systems whose decisions are opaque can lead to serious accountability issues. If an AI makes a critical decision, how can companies confidently explain or justify it to employees, customers, or regulators? Addressing this trust gap isn't merely about compliance, it's about confidence and clarity in decision-making processes that shape real lives and business outcomes. Why is AI opaque? AI systems differ fundamentally from traditional software, which relies on clearly defined rules. Instead, AI learns directly from vast datasets. These models don’t have explicit instructions or human-understandable logic guiding their decisions. At their core, AI models use billions of interconnected parameters to convert inputs into outputs through complex mathematical calculations. This method is inherently probabilistic, meaning decisions are based on statistical patterns, not logical reasoning. With billions of these parameters adjusting simultaneously, tracking exactly how or why a specific output was produced becomes practically impossible. Unlike human decision-making, AI doesn't follow structured reasoning steps. It identifies correlations and patterns in data, predicting outcomes without explicit explanations. This absence of clear reasoning pathways means that decisions from AI systems often appear arbitrary, opaque, and difficult to interpret or justify. The risks of black-box AI in business Businesses rely increasingly on AI to automate important tasks, yet the opacity of these systems presents clear practical challenges. False confidence and AI hallucinations A major risk of opaque AI is "hallucinations," where AI produces seemingly accurate but entirely incorrect information. These false positives arise when the AI fills knowledge gaps or handles unclear inputs. For example, customer support chatbots might confidently provide false policy details, leading directly to confusion and complaints. Accountability gaps Opaque AI creates accountability issues. Traditional software clearly logs every decision step, making errors easy to track and correct. Black-box AI systems don't offer this clarity. When decision-making relies on hidden AI processes, identifying the exact point of failure becomes difficult, slowing corrections and process improvements. Legal and compliance risks Businesses must increasingly explain automated decisions clearly due to regulations like GDPR. If an AI-driven system, such as a credit scoring tool, makes decisions without understandable reasoning, businesses risk facing regulatory actions, customer complaints, or legal disputes. Uncertainty working with internal data and knowledge Businesses typically want AI to incorporate their specialized data and internal expertise clearly. However, black-box AI models obscure how proprietary business information is actually used. Without clear visibility, enterprises can't confirm that internal knowledge is applied correctly, risking inaccurate outcomes or impractical recommendations. Explainable AI (XAI) methods Several methods within... --- - Published: 2024-12-25 - Modified: 2025-10-14 - URL: https://maisa.krai.app/agentic-insights/maisa-raises-pre-seed-round/ - Translation Priorities: Optional Back in March, we introduced the first version of the KPU, setting new benchmarks that surpassed leading models. Since then, our technology has advanced with the launch of the Vinci KPU, our team has grown, and we’ve welcomed our first customers. Yet, the core challenge in the AI market remains unchanged: a persistent lack of accountability, reliability and transparency in AI systems. Manu Romero & David Villalón The trust problem in AI Generative AI lacks accountability. Techniques like Chain-of-Thought reasoning, RAGs, and multiagent systems aim to address more sophisticated challenges but still rely on probabilistic predictions, not deterministic computations. AI is unlocking opportunities across countless domains, but the complex world of business demands greater accountability. Mission-critical tasks require not only answers but traceable, evidence-based processes to reach them. Without these, we risk hallucinations—fabricated outputs that render AI results unreliable. This lack of trustworthiness is why fewer than 6% of corporations use AI for anything beyond basic tasks like question-and-answer bots. What we are building We believe the solution isn’t in refining existing approaches but in creating a new kind of computing system—one that combines AI's creative problem-solving with the determinism of traditional computational systems. With Maisa, you can create bulletproof AI Agents. These are a new generation of Digital Workers that follow natural language instructions to achieve specific outcomes and goals, making intelligent and reliable automation a reality. With the best behind us We are fortunate to be backed by visionary investors committed to advancing our mission. Our pre-seed round brought in $5 million, led by NFX and joined by Village Global, the venture fund backed by Mark Zuckerberg, Eric Schmidt, and Jeff Bezos. This funding was further supported by Sequoia's Scout Fund ,and DeepMind PM Lukas Haas, and other angel investors. This funding enables us to continue developing our product and expanding our research initiatives. We’re also genuinely grateful for the recognition our work has received, including a recent feature in Forbes that highlights this important milestone for us. Maisa is going to be a major player in Agentic Process Automation (APA) helping businesses across the world transform their core, business-critical functions through AI. It will allow them to work faster, more efficiently and achieve new and radical ways of operating. Anna Piñol, NFX David and the Maisa team are building a transformative technology to turn AI agents into actual workers that are capable of reasoning through complex workflows. We're super thrilled to be a part of their journey and are very excited to see the new benchmarks and enterprise traction Max Kilberg, Village Global Come join us If you are interested in joining our mission of making AI accountable, visit our careers page --- - Published: 2024-11-26 - Modified: 2025-10-09 - URL: https://maisa.krai.app/agentic-insights/vinci-kpu/ - Translation Priorities: Optional Introduction On March 14, 2024, at Maisa AI, we announced our AI system to the world, enabling users to build AI/LLM-based solutions without worrying about the inherent issues of these models (such as hallucinations, being up-to-date, or context window constraints) thanks to our innovative architecture known as the Knowledge Processing Unit (KPU). In addition to user feedback, the benchmarks on which we evaluated our system demonstrated its power, achieving state-of-the-art results in several of them, such as MATH, GSM8k, DROP, and BBH— in some cases, clearly surpassing the top LLMs of the time. Vinci KPU Since March, we have been proactively addressing inference-time compute limitations and scalability requirements, paving the way for seamless integration with tools and continuous learning. Today, we are excited to announce that we have evolved the project we launched in March and are pleased to present the second version of our KPU, known as Vinci KPU. This version matches and even surpasses leading LLMs, such as the new Claude Sonnet 3. 5 and OpenAI’s o1, on challenging benchmarks like GPQA Diamond, MATH, HumanEval, and ProcBench. What’s new on the Vinci KPU (v2)? Before discussing the updates in v2, let’s do a quick recap of the v1 architecture. KPU OS Architecture Our architecture consists of three main components: the Reasoning Engine, which orchestrates the system's problem-solving capabilities; the Execution Engine, which processes and executes instructions; and the Virtual Context Window, which manages information flow and memory. In this second version, we've made significant improvements across all components: Reasoning Engine Improvement: We have enhanced the KPU kernel, furthering our commitment to positioning the LLM as the intelligent core of our OS Architecture. This advancement allows for more sophisticated reasoning and better orchestration of system components. Execution Engine Enhancements: We have successfully integrated cutting-edge test-time compute techniques and made the execution engine more robust, secure, and scalable. This ensures reliable performance while maintaining high-security standards for tool integration and external connections. Virtual Context Window Refinements: We have refined our Virtual Context Window through improved metadata creation and LLM-friendly indexing. This enhancement optimizes how information flows through the system and lays the groundwork for unlimited context and continuous learning capabilities. KPU Architecture Benefits What makes these results particularly significant is that they're achieved by our KPU OS, acting as a reasoning engine, which focuses on understanding the path to solutions rather than providing answers. As main benefits, we can highlight: Model Agnostic Architecture (Better base models, better performance) Full multi-step traceability: configurable observability: Debug mode, visual representation, et. al. Provides better human-in-the-loop and over-the-loop control. Mitigate, almost fully eliminates, hallucinations: While this approach minimizes AI-generated inaccuracies, it may still encounter issues like errors in tool execution, incorrect data sources, or suboptimal approaches to solving the problem. Lower Latency to resolve problems than other systems in the market. Cost-efficient (up to 40x times cheaper than RAG, reasoning engines and Large Reasoning Models). Fully flexible and customizable with out-of-the-box functionalities: Unstructured data management, tools integrations, data processing... Autonomous execution with self-recovery/self-healing. It... --- - Published: 2024-03-15 - Modified: 2025-10-15 - URL: https://maisa.krai.app/agentic-insights/hello-world/ - Translation Priorities: Optional Hello World In recent periods, the community has observed an almost exponential enhancement in the proficiency of Artificial Intelligence, notably in Large Language Models (LLMs) and Vision-Language Models (VLMs). The application of diverse pre-training methodologies to Transformer-based architectures utilizing extensive and superior quality datasets, followed by meticulous fine-tuning during both Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback/Reinforcement Learning with Augmented Imitation Feedback (RLHF/RLAIF) stages, has culminated in the development of models. These models not only achieve superior performance metrics across various benchmarks but also provide substantial utility in everyday applications for individuals, encompassing both conversational interfaces and API-driven services. These language models, based on that architecture, have several innate/inherent problems that persist no matter how much they advance their reasoning capacity or the number of tokens they can work with. Hallucinations. When a query is given to an LLM, the veracity of the response cannot be 100% guaranteed, no matter how many billions of parameters the model in question has. This is due to the intrinsic token-generating nature of the model, that generates the most likely token, which might not be a factual reason for the response to be trustable . Context limit. Lately, more models are appearing that are capable of handling more tokens, but we must wonder: at what cost? The "Attention" mechanism of the Transformer Architecture has a quadratic spatio-temporal complexity. This implies that as the information sequence we wish to analyze grows, both the processing time and memory demand increase proportionally. Not to mention the problems that arise with this type of model, such as the famous "Lost in the middle" , which means that sometimes the model is unable to retrieve key information if it is "in the middle" within that context. Up-to-date. The pre-training phase of an LLM inherently limits the data up to a certain date. This limitation affects the model's ability to provide current information. Asking the model about events or developments after its pre-training period may lead to inaccurate responses, unless external mechanisms are used to update or supplement the model's knowledge base. Limited capability to interact with “digital world”. LLMs are fundamentally language-based systems, lacking the ability to connect with external services. This limitation can pose challenges in tackling complex problems, as they have restricted abilities to interact with files, APIs, systems, or other external software. Architectural Overview The architecture we have named KPU (Knowledge Processing Unit) has the following main components. Reasoning Engine. It is the "brain" of the KPU, which orchestrates a step-by-step plan to solve the user's task. To design the plan, it relies on an LLM or VLM and available tools. The LLM is plug-and-play, currently extensively tested with GPT-4 Turbo. Execution Engine. Receives commands from the Reasoning Engine which it executes and whose result is sent back to the Reasoning Engine as feedback for re-planning. Virtual Context Window. It manages the input and output of data and information between the Reasoning engine and the Execution engine, ensuring that information arrives at the Reasoning... --- --- ## Use cases - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/power-of-attorney-verification/ - Department/Industry: Banking & Financial Services, Retail & Commercial After struggling with low automation accuracy and heavy manual review in loan applications, this leading European bank used Maisa to revolutionize intelligent document processing and boost customer satisfaction. Using Maisa Studio, the compliance team designed and deployed a Digital Worker capable of managing Power of Attorney verification end to end. The configuration process required no coding. The team simply described the task and rules in natural language, allowing the Digital Worker to learn and replicate their expertise. Document Understanding The Digital Worker reads both digital and scanned documents, even those without embedded text, ensuring complete coverage across formats and quality levels. Embedded Expertise It interprets legal clauses, identifies parties, and verifies authority in context. The system applies the institution’s internal compliance logic and keeps every decision traceable, enabling real-time auditing and transparency. Learning from Every Case With each verification, the Digital Worker strengthens its accuracy. It learns from compliance officer feedback in natural language and continuously adapts to new regulatory requirements without requiring new code or redeployment. Smart Exception Handling Straightforward cases are approved automatically, while complex ones are routed directly to specialists. Using Human Augmented LLM Processing, humans and the Digital Worker collaborate seamlessly, and recurring issues are resolved autonomously through its self-improving capability. Through this collaboration, the bank achieved faster verification cycles, reduced workload for compliance professionals, and built a reliable automation framework that evolves continuously. The Results Massive Time Recovery Automating Power of Attorney verification freed forty thousand hours annually, releasing thousands of days of productive capacity back to compliance teams. Faster Client Onboarding Verifications that once required hours now take only minutes, accelerating customer activation and transaction processing. Continuous Compliance Readiness The Digital Worker evolves with each execution, updating its validation methods automatically to reflect the latest regulations and legal templates. Significant Cost Impact Deploying the solution in a single region generated 1. 2 million euros in savings, demonstrating one of the highest returns on automation in compliance operations. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/auto-loan-processing/ - Department/Industry: Banking & Financial Services, Consumer / Retail Banking After struggling with low automation accuracy and heavy manual review in loan applications, this leading European bank used Maisa to revolutionize intelligent document processing and boost customer satisfaction. The bank’s goal with Maisa was ambitious: achieve 80–90% success rates for fully automated loan document activations, with minimal manual intervention. Their compliance and operations teams onboarded Maisa Digital Workers themselves using natural language instructions — no coding required. Configured to follow the bank’s procedures and rules, the Digital Workers now handle the process end-to-end: Document Intake Receives customer documents from multiple channels, regardless of format or quality, even low-quality scans. Intelligent Classification Analyzes structure, layout, and patterns to classify documents with 98% accuracy, across 40+ languages and formats. Context-Aware Extraction Extracts unstructured data aligned with business rules and compliance needs, with every field fully traceable for audit. Structured Data Output Formats results into standardized JSON with consistent field names and validation rules, flagging anomalies automatically. Quality Assurance Cross-field validation ensures data consistency, with hallucination-resistant Digital Workers logging every step in verifiable code for full auditability. By combining automation with human feedback loops, the system continuously learns and improves, giving teams confidence to scale. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/trade-finance-processing/ - Department/Industry: Banking & Financial Services, Retail & Commercial A leading global bank’s Corporate and Investment Banking division transformed one of its hardest processes to automate with traditional technology. Trade finance verification had resisted automation for years due to the complexity and variability of documents such as contracts, invoices, and certificates. Using Maisa Digital Workers, the bank digitized and streamlined the entire process, accelerating transaction turnaround, reducing manual review, and ensuring regulatory compliance at scale. Using Maisa Studio, the operations team built its first Digital Worker in minutes using simple natural language instructions, without the need for coding or technical expertise. The Maisa Digital Worker now manages the trade finance verification process from start to finish. Using Maisa Studio, the operations team built its first Digital Worker in minutes using simple natural language instructions, without the need for coding or technical expertise. The Maisa Digital Worker now manages the trade finance verification process from start to finish. Document Intake Processes both digital and scanned PDFs, even those without an embedded text layer, ensuring complete coverage regardless of quality or format. Cross Validation of Information Checks every document for key data points, verifies beneficiaries, terms, and compliance requirements, and ensures consistency across multiple sources. Adaptive Reasoning When information is missing or unclear, the Digital Worker identifies gaps, searches related documents for additional context, and applies reasoning to produce accurate conclusions. Executive Reporting Compiles a complete summary of every document analyzed, including findings and references. Every decision is traceable and fully auditable. Institutional Knowledge Capture Each execution enriches Maisa’s Know How system, transforming institutional expertise into a reusable internal asset that evolves with every process run. By combining document intelligence, embedded expertise, and continuous learning, the bank automated one of its most complex and regulated operations with confidence and transparency. The Results Massive Time Savings Trade finance verification time decreased from hours to minutes. Faster Transaction Turnaround End to end automation removed manual bottlenecks and accelerated client service. Continuous Learning and Scalability Each execution captured expert knowledge, creating a self improving automation framework that can be deployed across regions. Consistent and High Quality Decisions Traceable and rule aligned automation ensured accuracy, compliance, and full audit confidence. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/insurance/o2c-p2p/ - Department/Industry: Finance & Accounting, Insurance By replacing rigid RPA and unreliable AI tools with Maisa’s hallucination-resistant Digital Workers, this enterprise transformed billing, payments, and financial operations with scalable, trustworthy automation. Maisa introduced trustworthy, hallucination-resistant Digital Workers, giving the finance team confidence to scale automation with traceability, auditability, and resilience. O2C – Order to Cash Order Intake & Data Capture: Captures orders across channels, extracting customer info, quantities, and pricing even from unstructured or multilingual docs. Validation Against Rules: Cross-checks details against company logic, ensuring compliance and accuracy, all fully auditable. Invoice Generation & Posting: Generates standardized invoices and posts to ERP systems, accelerating revenue recognition. Exception Handling: Handles anomalies via self-healing and HALP (Human-Augmented LLM Processing). P2P – Procure to Pay Invoice Intake & Extraction: Captures invoices from any format, extracting supplier names, dates, amounts, tax references. Classification & Rules Application: Categorizes invoices, applies workflows, and logs all steps for audit. Payment Integration: Posts approved invoices directly into custom payment systems, including legacy platforms. Automated Documentation: Generates standardized PDFs for records. Exceptions handled via HALP, with continuous learning loops. Together, O2C and P2P automation saved 58,000+ hours annually, achieving 98% STP and accelerating both revenue recognition and supplier payments --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/equity-research/ - Department/Industry: Banking & Financial Services, Wealth and Investment Management A leading investment management group relied on manual equity research workflows that slowed decision-making and limited scalability. Broker reports were lengthy, inconsistent, and packed with dense financial data. With Maisa Digital Workers, the firm automated the extraction and structuring of insights from these reports, turning unstructured content into actionable intelligence in minutes and enabling analysts to focus on high-impact investment strategy. Using Maisa Studio, the firm’s Wealth and Investment Management team created a Digital Worker that processes broker research reports end to end. Built without coding and guided by natural language instructions, the worker extracts, interprets, and structures insights with precision and consistency. Data Extraction and Structuring The Digital Worker reads and parses text, tables, and charts from PDF reports regardless of layout or formatting. It identifies relevant sections and converts them into standardized, machine-readable fields ready for analysis. Insight Summarization Summarizes target prices, recommendations, earnings expectations, and qualitative commentary into concise, structured insights, aligned with the firm’s reporting formats and KPIs. Automated Output Generation Creates ready-to-use outputs such as Excel templates and narrative summaries, enabling analysts to review and share insights instantly across teams and systems. Scalability and Adaptability Scales seamlessly across brokers and research teams with minimal setup, allowing rapid expansion of coverage across asset classes and sectors without additional engineering effort. By integrating document intelligence with domain expertise, Maisa Digital Workers turned a manual research workflow into a scalable, automated intelligence system for investment analysis. The Results Faster Coverage Research reports that once required extensive manual review are now processed in less than five minutes. Analyst Enablement Structured insights accelerate decision-making and allow analysts to focus on strategic analysis rather than administrative tasks. Foundation for Scale The automated process enables consistent and rapid processing of large volumes of broker research, creating a foundation for global research scalability. Path to Advanced Intelligence Establishes a roadmap for future AI-driven research synthesis, enabling cross-broker comparisons and multi-document insights at scale. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/new-client-onboarding/ - Department/Industry: Banking & Financial Services, Retail & Commercial A global financial institution modernized its client onboarding process using Maisa Digital Workers. The bank previously relied on manual document handling for customer identification, which caused delays and increased operational costs. With Maisa, onboarding documentation is now automatically classified, analyzed, and processed, dramatically reducing activation time while improving accuracy and scalability across regions. Using Maisa Studio, the bank’s operations and compliance teams configured a Digital Worker to automate onboarding document processing from end to end. Built using natural language instructions, the Digital Worker requires no technical expertise and aligns perfectly with internal procedures. Document Analysis Reviews multi-page PDFs and identifies relevant pages for processing, ignoring irrelevant or duplicate content. Automatic Classification Recognizes and classifies document types such as identity proofs, bank statements, and payslips without human input. Data Extraction Captures essential data fields from each document category — including client details, account identifiers, and verification information — and prepares structured outputs for system integration. Data Structuring Organizes extracted information into standardized formats that feed directly into onboarding systems and compliance checks. This seamless process ensures that every client document is validated, structured, and stored consistently across departments, enabling faster activation and more reliable compliance documentation. The Results Faster Onboarding Client activation time was significantly reduced as document review and validation became fully automated. Automatic Classification Documents are now sorted and categorized without manual intervention, eliminating errors and saving operational time. Accurate Extraction Critical client data is captured systematically, improving the quality of downstream processes like KYC verification and account creation. Scalable Operations The new onboarding workflow handles rising document volumes effortlessly, enabling the institution to scale its client acquisition capacity without adding staff. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/rfp-response-generation/ - Department/Industry: Banking & Financial Services, Corporate and Investment Banking A leading financial institution reimagined its RFP management process with Maisa Digital Workers. Manual response preparation was replaced by an automated, intelligent workflow that searches internal documentation, generates compliant answers, and produces ready-to-submit drafts in hours instead of days. The result is faster collaboration, improved accuracy, and a globally consistent RFP process. Using Maisa Studio, the bank’s commercial operations and procurement teams built a Maisa Digital Worker that now handles the RFP process from start to finish. Configured in natural language, the Digital Worker connects directly with internal libraries, ensuring that every response is accurate, up to date, and aligned with corporate standards. RFP Document Intake Uploads RFP documents, reads each section, and automatically identifies all questions and response requirements. Automated Draft Generation Creates the first draft of RFP responses by analyzing historical data, previous submissions, and approved documentation. Drafts are automatically formatted in the required structure for Word, PowerPoint, or Excel. Unified Content Library Establishes a single, global document library that provides every team with access to the same verified and current content. This ensures consistency and removes dependency on outdated local files. Collaborative Workflow Management Assigns questions and sections to different teams or subject matter experts, tracks progress in real time, and sends reminders for pending items. The Digital Worker maintains version control and visibility across the entire process. Smart Information Retrieval Searches through internal knowledge repositories and documentation libraries to locate the most relevant and compliant information for each response. Analytics and Alerts Provides insights into response progress, pending items, and status alerts for administrators. It also manages document versions and change control to maintain transparency across all submissions. Final Compilation and Delivery Once all sections are validated, Maisa automatically compiles and formats the completed RFP. It generates supporting materials such as executive summaries, value propositions, and presentations ready for leadership review. By centralizing content and automating response generation, Maisa transformed a fragmented process into a fast, auditable, and scalable RFP operation. The Results Automated Generation RFPs are completed automatically using verified content, removing the need for manual assembly and review. Time Savings Response time has been reduced from several days to just hours, allowing teams to handle multiple RFPs simultaneously. Improved Accuracy Systematic search and validation ensure that every response is compliant, complete, and aligned with approved documentation. Resource Optimization Teams now focus on strategic initiatives and vendor management rather than repetitive content retrieval and formatting. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/subpoenas-requests-intake/ - Department/Industry: Banking & Financial Services, Consumer / Retail Banking A leading financial institution restructured how it manages subpoenas and legal information requests. The previous manual process required coordination between multiple departments and systems, creating long turnaround times and potential compliance risks. With Maisa Digital Workers, the bank now receives, classifies, and processes legal requests automatically, ensuring every response is complete, accurate, and fully auditable. Using Maisa Studio, the bank’s legal operations team created a Maisa Digital Worker that now manages subpoenas and legal requests from start to finish. Configured in natural language, the Digital Worker follows institutional rules and compliance standards to ensure accuracy, speed, and transparency at every step. Automated Classification Each incoming request is analyzed and categorized automatically. The Digital Worker identifies its nature and routes it to the correct workflow or department. Data Gathering from Internal Systems Maisa connects securely to relevant internal databases to retrieve the necessary client, product, and transaction details, removing the need for manual data searches. Structured Response Compilation Collected data is organized into a standardized document ready for review. Responses are aligned with internal templates and external regulatory formats. Focused Escalation Routine requests are completed automatically, while sensitive or exceptional cases are forwarded to legal teams for verification. This reduces the number of manual reviews while maintaining oversight. Audit and Compliance Tracking Every request and response is logged with a complete record of actions taken. This creates a verifiable trail for compliance checks and audit reviews. The Digital Worker now acts as a central coordination layer, connecting people, systems, and policies into a single, reliable process that delivers consistent results every time. The Results Faster Turnaround Requests that previously required several days are now completed in minutes, giving legal teams more time to focus on complex matters. Lower Risk Exposure Automated classification and response logic eliminate misrouting and reduce the potential for missing or inaccurate information. Built-in Compliance Each response is traceable, fully documented, and aligned with the institution’s internal standards and external legal requirements. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/fund-performance-reporting/ - Department/Industry: Banking & Financial Services, Wealth and Investment Management A global investment bank redesigned its fund performance reporting process with Maisa Digital Workers. What once required analysts to manually extract, clean, and consolidate data from multiple platforms is now an automated workflow that delivers precise, compliant, and audit-ready reports in minutes. Maisa enables teams to focus on analysis and client insights instead of repetitive data preparation. Using Maisa Studio, the institution developed a Digital Worker that manages the entire reporting cycle, from data ingestion to final report delivery. Configured in natural language by operations and analytics teams, the Digital Worker integrates securely with internal systems and applies business rules consistently across all funds. Automated Data Integration Connects to internal data sources and reporting platforms to extract and transform performance information automatically. Consistent Calculations and Formatting Performs return calculations, generates narrative summaries, and formats outputs for multiple reporting frequencies and fund structures. Structured and Compliant Outputs Produces results in standardized formats such as Excel, JSON, and XML, ensuring alignment with regulatory and client requirements. Audit and Version Control All outputs are versioned and traceable, allowing compliance teams to verify every figure and change through a secure audit trail. Scalable Framework The Digital Worker is adaptable to future reporting expansion, creating a foundation for goal-based investment reporting and other advanced analytics. Maisa transformed a complex reporting pipeline into a secure, reliable, and scalable process that allows the institution to produce insights faster and with greater confidence. The Results Accelerated Cycle Report generation time was reduced from days to minutes, giving teams faster access to accurate performance insights. Scalable Process The automated system adapts to new fund types, frequencies, and reporting formats without reconfiguration. Full Auditability Each report is versioned and reviewable, providing a clear history of calculations and outputs. Secure by Design All data remains within the bank’s infrastructure, maintaining complete control and compliance with internal policies. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/adverse-media-screening-alerts/ - Department/Industry: Banking & Financial Services, Corporate and Investment Banking A global corporate and investment bank implemented Maisa Digital Workers to automate the review of adverse media alerts within its compliance division. Previously, analysts had to manually interpret articles, classify risks, and complete assessment matrices, a process that was slow and inconsistent. Now, every review is automatically structured, documented, and aligned with internal policies, enabling faster analysis, consistent decision-making, and full regulatory traceability. Using Maisa Studio, the bank’s compliance team built a Maisa Digital Worker that automates the entire adverse media review process. Configured through natural language, the Digital Worker applies the institution’s internal frameworks for Financial Crime and ESG screening, ensuring each review follows consistent rules. Automated Data Extraction The Digital Worker identifies relevant information within articles, extracting key facts and mapping them directly to predefined evaluation matrices. Standardized Assessment Logic Each case is processed using uniform logic, ensuring that risk classification and rationale follow consistent patterns that align with internal policies. Structured Reporting and Documentation Maisa produces detailed outputs that include the extracted data, risk category, and rationale, creating structured, audit-ready documentation. System Integration It connects seamlessly with the bank’s compliance platforms and data sources, consolidating information flow and eliminating manual re-entry. Continuous Adaptability The Digital Worker is easily updated to include new evaluation criteria, languages, or alert categories as regulations evolve or new data sources are added. Through this automation, the compliance function gained a dependable process that ensures speed, traceability, and regulatory accuracy across all media screening workflows. The Results Streamlined Evaluation Adverse media reviews that previously required hours of analyst effort are now completed in minutes. Improved Consistency Each decision follows a unified logic, producing structured, high-quality assessments that align with regulatory and internal standards. Audit-Ready Outputs All responses are documented and traceable, providing complete visibility for both internal reviews and external audits. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/correspondent-account-transaction-reconciliation/ - Department/Industry: Banking & Financial Services, Global Operations A major financial services provider transformed its reconciliation operations with Maisa Digital Workers. The company’s teams previously spent significant time reviewing thousands of unmatched transactions, most of which were false positives rather than real discrepancies. With Maisa, reconciliation logic is now automated and adaptive, enabling faster exception handling, fewer manual interventions, and a measurable increase in accuracy and efficiency. Using Maisa Studio, the operations team created a Maisa Digital Worker that learns reconciliation logic directly from standard operating procedures, historical cases, and guided analyst input. Capturing Human Reasoning The Digital Worker observes and records the reasoning steps analysts follow when resolving exceptions, replicating their approach with precision and consistency. System Integration It connects securely to internal platforms through application programming interfaces, enabling data access and matching within the organization’s controlled environment. Adaptive Matching Logic Maisa applies flexible reasoning to detect alternative attributes or fallback values when direct matches fail, ensuring that the system identifies genuine discrepancies more accurately. Self Learning and Continuous Improvement When new exception patterns emerge, the Digital Worker updates its logic automatically, improving its accuracy with every cycle. Rapid Implementation The solution was deployed using sample datasets and a few guided sessions, requiring no coding or major system rebuilds. Through this approach, the institution replaced manual reconciliation with an intelligent and transparent process that continuously refines itself, reducing workload while improving control and reliability. The Results High Precision Ninety nine percent of false positives are now automatically identified and excluded, allowing analysts to focus on true exceptions. Massive Efficiency Gain Reconciliation workload has been reduced by ninety percent, delivering substantial time and cost savings. Adaptive Reasoning Complex reconciliation logic is handled dynamically, ensuring the process remains efficient and scalable without constant technical maintenance. System Awareness Maisa Digital Workers use contextual data across connected systems to resolve mismatches accurately and consistently. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/deceased-account-processing/ - Department/Industry: Banking & Financial Services, Retail & Commercial A leading retail bank transformed the management of deceased client accounts using Maisa Digital Workers. The process of verifying death certificates, closing accounts, and communicating with families once relied entirely on manual work that varied by region and policy. Now, the workflow is automated from document review to family notification, allowing sensitive procedures to be handled quickly, consistently, and with complete regulatory assurance. Through Maisa Studio, the bank developed a Maisa Digital Worker to manage the entire workflow for deceased account processing. Configured in natural language, the Digital Worker follows the institution’s compliance standards, internal procedures, and communication protocols with accuracy and empathy. Document Understanding Extracts and validates information from both scanned and digital death certificates, recognizing variations in format and structure. Automated Account Identification Searches internal databases to identify all active and linked accounts associated with the deceased client, ensuring that none are overlooked. Workflow Execution Carries out account closure and transfer steps in alignment with bank policies, coordinating actions across core systems and compliance checks. Family Communication Management Generates formal letters and notifications using approved templates that maintain appropriate tone and language, ready for review or automatic dispatch. Audit and Traceability Records every action, from document verification to message delivery, creating a transparent audit trail for regulatory and internal reporting. Maisa brought structure, reliability, and compassion to a process that had previously relied entirely on manual attention, ensuring every step is accurate, consistent, and fully compliant. The Results Process Efficiency Account processing time was significantly reduced as automation now handles document validation, account searches, and communications. Improved Reliability Systematic identification ensures that all accounts are found and processed correctly, eliminating oversight and delays. Consistent Communications Family notifications follow pre-approved templates and tone guidelines, ensuring clarity and professionalism across every region. Full Compliance All actions are logged and auditable, providing a complete and verifiable record of each case handled. --- - Published: 2025-11-14 - Modified: 2025-11-17 - URL: https://maisa.krai.app/banking-financial-services/tax-withholding-reconciliation/ - Department/Industry: Corporate and Investment Banking A European investment firm simplified its annual tax withholding reconciliation process with Maisa Digital Workers. The institution previously relied on manual comparison of fund data and counterparty certificates across multiple formats, a task that required significant time and specialized staff. Today, reconciliation is fully automated, with Maisa ensuring faster cycles, complete auditability, and minimal operational intervention. Using Maisa Studio, the firm deployed a Maisa Digital Worker to automate tax withholding reconciliation from data ingestion to final validation. Configured with natural language, the Digital Worker integrates with fund systems and counterparties to streamline every step of the process. Automated Data Ingestion Reads and parses incoming certificates from counterparties, regardless of format, and organizes them into a structured dataset for reconciliation. Smart Matching Logic Applies configurable criteria such as date, amount, and fund ID to match entries across systems with high accuracy. Discrepancy Identification and Consolidation Flags unmatched records, consolidates reconciled entries, and generates dynamic reports showing resolved and pending items. Full Audit Trail Produces structured outputs, including detailed audit logs and traceable Excel files that meet both internal and external reporting standards. Incremental Processing Supports ongoing reconciliation as new certificates are received, reducing bottlenecks and allowing continuous progress throughout the reporting cycle. With Maisa, reconciliation has evolved from a once-a-year, resource-heavy effort into a continuously monitored process that delivers full visibility and control. The Results Accelerated Cycle Reconciliation time dropped from weeks to days, enabling teams to meet deadlines with ease. Full Auditability All transactions are traceable, with clear records for matched and unmatched items across funds and counterparties. Lower Operational Load Automation drastically reduced manual effort, freeing specialized staff to focus on analysis and strategic reporting. Scalable by Design New funds and counterparties can be added quickly, allowing the system to adapt to future growth without additional development. --- ---