AI-Powered Entity Data Retrieval and Insights: Turning a Knowledge Base Into an Intelligent, Queryable Asset

From a locked, inaccessible knowledge base and hours of manual questionnaire entry to an AI-driven retrieval engine that surfaces accurate answers in minutes.

Customer Overview

The client is a specialist entity management platform provider whose flagship product, Kube, serves organizations managing complex corporate structures, compliance obligations, and governance workflows. At the heart of Kube sits an extensive repository of entity data, built up over time across thousands of records.

As client expectations around compliance automation grew, it became clear that the platform's existing approach to surfacing that data was no longer fit for purpose. The client partnered with Cyann, Cnovate, and Microsoft to reimagine how entity data could be retrieved, interpreted, and delivered to end users at scale.

The Challenge

Despite housing a rich and comprehensive entity knowledge base, the platform had no intelligent mechanism to surface that information when it was needed most. Compliance questionnaires, a core part of the client's onboarding and governance workflows, were being completed almost entirely through manual effort, with dedicated employees spending hours on each submission.

Key challenges included:

• Trapped Knowledge: An extensive knowledge base of entity data existed within the platform, but there was no structured or scalable way to surface it in response to client queries or compliance questions.
• Manual Questionnaire Completion: Multiple employees were dedicated to manually inputting questionnaire responses for clients, a process that consumed significant time on every submission and could not scale with demand.
• High Error Exposure: The manual, rudimentary approach introduced consistent risk of inaccuracy across questionnaire responses, creating downstream compliance and governance risk.
• Onboarding Complexity: Complex organization security and governance questionnaires required nuanced, entity-specific answers that were time-consuming to research and compile manually for every new client.
• No Path to Scale: With no automation in the workflow, growth in client volume translated directly into growth in manual workload, with no ceiling on operational cost.

The Breaking Point: When the average questionnaire was taking approximately 5 hours to complete per submission, the business had a compounding capacity problem. Every new client and every new compliance cycle added to a manual burden that was already unsustainable. The knowledge existed to answer these questions accurately. The gap was in retrieving and applying it intelligently.

The Cyann Approach

Cyann, in partnership with Cnovate and Microsoft, designed and deployed an AI-powered extraction and normalization solution built directly on top of the client's existing entity knowledge base, transforming it from a static repository into a live, queryable intelligence layer.

Solution Overview

• AI-Powered Extraction and Normalization: Introduced an intelligent extraction layer using Azure OpenAI Service and Azure Cognitive Search to automatically identify, retrieve, and normalize relevant entity data in response to incoming questions.
• Automated Questionnaire Response: Built workflows using Microsoft Power Automate and Copilot Studio that allow clients to submit large volumes of questions to Kube and receive accurate, AI-generated responses efficiently and at scale.
• Streamlined Onboarding and Compliance Workflows: Entity data is now surfaced directly into complex onboarding and compliance processes, dramatically reducing manual effort and the risk of errors at every stage.
• Azure ML Integration: Leveraged Azure ML to continuously improve extraction quality and answer accuracy as the model learns from new question types and entity structures.
• Enterprise-Grade Architecture: Built on a scalable Azure foundation with security and global expansion built into the architecture from day one, positioning the client for growth without re-platforming.
• M365 Ecosystem Integration: Connected the solution into the client's M365 environment, embedding AI-powered data retrieval within the tools teams already use daily.

Tech Stack

Microsoft Azure: Azure OpenAI Service · Azure Cognitive Search · Azure ML · Microsoft Power Automate · Copilot Studio · M365

Engineering Insights

• The Data Already Exists. The Problem Is Retrieval: In many enterprise AI projects, the barrier is not a lack of data but a lack of structured access to it. The client already held the knowledge needed to answer compliance questions accurately. Combining Azure Cognitive Search with OpenAI created the retrieval and reasoning layer that turned static records into dynamic, queryable answers.
• Copilot Studio Bridges the Gap Between AI and Business Users: Rather than requiring compliance teams to interact with raw AI outputs, Copilot Studio provided a business-friendly interface that felt intuitive and required no technical expertise. Adoption was higher precisely because the experience matched how users already worked.
• Normalization Is Not Optional at Scale: Entity data drawn from multiple sources, formats, and corporate structures requires normalization before it can be reliably used in automated responses. Building this step into the pipeline with Azure ML was what made the accuracy levels achievable and consistent.

Results

The solution delivered transformational improvements across the client's compliance, onboarding, and operational workflows:

• 5x Faster Questionnaire Completion: The end-to-end questionnaire process is now five times faster, fundamentally changing what the client can deliver to their customers.
• 80 to 85% Reduction in Completion Time: Average questionnaire time dropped from approximately 5 hours to under 1 hour per submission, freeing up significant capacity across the team.
• Compliance Risk Substantially Reduced: Filing and regulatory adherence has been de-risked and streamlined, with accurate AI-generated responses replacing error-prone manual inputs.
• Entity Data Now Instantly Accessible: The previously locked knowledge base is now a live intelligence layer that surfaces the right data at the right moment across onboarding and compliance workflows.
• Operational Efficiency Transformed: With manual questionnaire effort removed from the critical path, the team can focus on complex exceptions and strategic client work rather than repetitive data entry.

Conclusion

A knowledge base is only as valuable as your ability to access it. For this entity management platform, years of carefully maintained entity data was sitting largely untapped while employees spent hours manually answering the same categories of compliance questions over and over again. By deploying an Azure-native AI retrieval and normalization solution, Cyann and Cnovate turned that knowledge base into an active, intelligent asset. The result is a platform that can now answer complex governance and compliance questions accurately, instantly, and at scale, without the manual overhead that was limiting growth.