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Smart Document Search with Gen AI

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Building a Smarter Document Search with Generative AI

Many institutions, such as Local Governments, generate vast amounts of crucial information every day. From detailed meeting minutes and white papers to policy documents and public consultations.

But there's a problem. How do you find a specific piece of information buried in thousands of documents? This was the challenge we set out to solve in a recent project focussed on accessing data from Local Government.

The Challenge: Trapped in a PDF Prison

The vast majority of official documentation from local authorities is published as PDF files. While great for preserving document format, PDFs are notoriously difficult for conventional search engines to handle. Their text isn't always machine-readable, and their complex structure means standard keyword searches often fail to find the most relevant information, or worse, miss it entirely.

Furthermore, even if the documents are indexed, a standard search engine like Google or Bing isn't the right tool for the job. Their purpose is to search the entire World Wide Web. When a resident or council officer needs to know about a specific local planning decision, they don't want results from a council halfway across the country. They need a precise answer, drawn only from their own authority's official documents.

The Solution: Advanced AI for Precise Answers

To solve this, we turned to the latest advancements in generative AI, specifically focusing on two powerful techniques: Retrieval-Augmented Generation (RAG) and Agentic AI.

Retrieval-Augmented Generation (RAG): Think of RAG as a smart, two-step research process. When a user asks a question (e.g., "What was decided about the high street regeneration in the last council meeting?"), the system first performs the 'Retrieval' step. It intelligently scans the entire library of council PDFs to find the most relevant paragraphs and pages related to the query. Then, in the 'Generation' step, a powerful language model uses only this retrieved information to construct a clear, accurate, and concise answer in natural language. This ensures the answers are always grounded in the source documents, preventing the AI from making things up.

Agentic AI: We took this a step further by building an "AI Agent." This agent acts like an autonomous digital researcher. It can understand a user's query, break it down into logical steps, determine the best way to search the document database using the RAG system, and then synthesize the findings into a comprehensive response. It’s an intelligent workflow that provides a level of efficiency and accuracy far beyond a simple search bar.

The Result: Fast, Efficient, and Trustworthy Search

The outcome is a highly effective search tool that transforms how users interact with local government documents. Instead of manually sifting through hundreds of pages, a user can now simply ask a question and receive a direct answer in seconds, complete with citations pointing to the source document.

This system empowers both council employees and the public to access information effortlessly, fostering transparency and dramatically improving efficiency. The days of frustrating, fruitless searches are over.

Is your organisation sitting on a wealth of untapped information locked away in documents? Get in touch with Trust Worthy AI to explore how our expertise can help you build a solution.