Enterprise Client | 2026 – Present
Key Technologies: AI Engineering, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Document Intelligence, Prompt Engineering, .NET, C#, React, Microsoft Azure, Azure Logic Apps, Azure Cosmos DB
Overview: Keyhole Software partnered with an enterprise client to develop an AI-powered document intelligence and regulatory compliance platform for organizations operating in highly regulated industries. Built using .NET, React, Microsoft Azure, and Retrieval-Augmented Generation (RAG), the enterprise AI platform enables users to analyze complex regulatory and internal documentation while receiving accurate, context-aware guidance grounded in their own content.
Delivered by a four-person Agile team consisting of three software engineers and one project manager, the engagement focused on building a scalable AI foundation that accelerated product development while creating a long-term architecture for intelligent document processing, AI-powered knowledge management, and future enterprise AI capabilities.
The Challenge
Organizations operating in highly regulated industries manage thousands of pages of policies, technical specifications, standards, and evolving regulatory documentation. Finding relevant information quickly is difficult, and relying on traditional keyword search often results in incomplete answers, inconsistent interpretation, and time-consuming manual research.
The client wanted to deliver an AI-powered experience that could provide accurate, context-aware regulatory guidance while ensuring responses were grounded in customer-specific documentation rather than relying solely on a large language model’s general training. The platform also needed to support rapid feature delivery while establishing a scalable architecture capable of evolving alongside changing business requirements and regulatory standards.
Why Keyhole
Keyhole Software assembled a small Agile delivery team capable of designing and building both the enterprise AI architecture and the full-stack application. By combining expertise in AI engineering, cloud-native application development, document intelligence, and modern Microsoft Azure technologies, the team was able to establish the platform’s technical foundation while continuing to deliver new functionality throughout the engagement.
The Solution
Rather than treating artificial intelligence as a standalone feature, the team focused first on building a scalable document intelligence foundation that could support future AI capabilities across the platform.
The solution centered around a robust document ingestion pipeline capable of processing both customer-uploaded documents and externally sourced regulatory content. Once processed, this information became the foundation for Retrieval-Augmented Generation (RAG), allowing large language models to generate responses grounded in each customer’s unique document collection instead of relying exclusively on pretrained model knowledge.
By combining document intelligence, prompt engineering, and modern AI workflows, the platform delivers accurate, explainable, and context-aware regulatory guidance tailored to each organization’s documentation.
Beyond the AI foundation, the team expanded the platform with additional AI-powered functionality, document management capabilities, and full-stack application enhancements using .NET, React, Azure Logic Apps, Azure Cosmos DB, and other Microsoft Azure services.
Solution Architecture
The platform combines document ingestion, intelligent document processing, Retrieval-Augmented Generation (RAG), prompt engineering, and cloud-native application development to deliver grounded AI experiences. Customer documentation is transformed into an AI-ready knowledge base that enables semantic retrieval and context-aware responses, improving both the relevance and reliability of AI-generated guidance.
The architecture was intentionally designed for extensibility, allowing additional AI capabilities and business functionality to be introduced without redesigning the underlying document intelligence pipeline.
Technical Highlights
- Developed a scalable document intelligence pipeline supporting AI-powered regulatory compliance workflows.
- Designed ingestion workflows for customer-uploaded documents and externally sourced regulatory content.
- Implemented Retrieval-Augmented Generation (RAG) workflows that deliver grounded, context-aware responses using customer-specific documentation.
- Applied prompt engineering techniques to improve large language model accuracy, consistency, and response quality.
- Developed full-stack application features using .NET, C#, React, Azure Cosmos DB, Azure Logic Apps, and Microsoft Azure cloud services.
- Expanded the platform’s document management capabilities alongside new AI-powered application functionality.
- Collaborated within a four-person Agile delivery team consisting of three software engineers and one project manager using Azure DevOps, Azure Repos, Microsoft Teams, peer code reviews, and automated quality practices.
- Leveraged Claude throughout development to accelerate implementation, evaluate technical approaches, and support AI-assisted software development.
Business Results
- Established the platform’s AI foundation early in the engagement, enabling additional AI capabilities and product features to be developed in parallel.
- Created a scalable enterprise AI architecture supporting future document intelligence, Retrieval-Augmented Generation (RAG), and intelligent document processing initiatives.
- Improved AI reliability by grounding responses in customer-specific documentation, increasing relevance while reducing the likelihood of hallucinated responses.
- Accelerated delivery of new AI-powered functionality while supporting an aggressive product roadmap.
- Provided a flexible architecture that allows the platform to evolve alongside changing regulatory requirements and future business needs.
Business Impact
The resulting platform provides the client with a scalable enterprise AI foundation for document intelligence, regulatory compliance, and AI-powered knowledge management. By investing in the underlying architecture first, the team enabled the platform to support future AI enhancements without requiring significant changes to the document processing pipeline.
This engagement demonstrates how thoughtful AI engineering, modern cloud architecture, and Retrieval-Augmented Generation can be combined to build enterprise AI solutions that are accurate, scalable, and designed for long-term growth.
