AI-Powered Knowledge Assistant for Document Retrieval

A leading insurance services firm sought to enhance internal knowledge retrieval by leveraging AI-driven search capabilities. Employees frequently needed to extract information from complex insurance compliance, actuarial, and operational documents, requiring significant manual effort. Traditional document search methods were slow, inefficient, and unable to deliver relevant responses in real time.

To address this challenge, ekSource designed and implemented a Retrieval-Augmented Generation (RAG)-based AI chatbot that enables employees to query both structured and unstructured documents and receive accurate, context-aware responses. The solution integrates Weaviate as the vector database, OpenAI’s GPT-4o mini for intelligent response generation, and a custom-built Angular-based chatbot UI, all deployed within the client’s AWS environment.

The PoC was delivered significantly faster and at a lower cost than competitive solutions due to ekSource’s extensive experience in AI/ML projects, a highly skilled AI engineering team, and our proprietary document extraction pipeline.

Business Challenge

The client’s employees often work with large volumes of regulatory, actuarial, and compliance documents spread across multiple formats, including PDFs, Word documents, and Excel files. Retrieving relevant insights required manually searching through these documents, a time-consuming and inefficient process.

Key Challenges Faced by the Client

Slow and Inefficient Knowledge Retrieval – Employees spent hours manually searching for information, leading to decreased productivity and operational delays.
Lack of a Centralized Knowledge Repository – There was no unified system for retrieving contextual answers across document types.
Inconsistency in Search Results – Employees relied on individual knowledge and ad-hoc keyword searches, resulting in inconsistent responses.
Scalability Concerns – As the volume of documents grew, existing search mechanisms became increasingly ineffective.
Competitive Solutions Were Expensive and Time-Consuming – Competing AI solution providers quoted significantly higher costs and longer delivery timelines due to their lack of streamlined frameworks for document processing and retrieval.

The client needed an AI-powered solution that could understand natural language queries, retrieve relevant content efficiently, and provide accurate, document-backed responses—all while being scalable and future-proof.

Solution

ekSource implemented a RAG-based AI chatbot with a modular, scalable architecture tailored to the client’s needs. The solution included:

Document Processing & Vectorization

Documents were extracted, preprocessed, and indexed using Weaviate as the vector database.
ekSource’s proprietary document extraction pipeline enabled faster text extraction and indexing, reducing implementation time.
Weaviate was chosen over FAISS to support the client’s future multi-storage architecture goals.

AI-Powered Query Resolution

OpenAI’s GPT-4o mini was integrated to generate natural language responses based strictly on retrieved document content.
The chatbot ensured document-grounded responses, minimizing AI hallucinations and improving response accuracy.
The AI model was optimized for low-latency retrieval, ensuring faster response times.

Web-Based Chat Interface (Built with Angular)

AWS-Based Deployment & Security

Rapid Development & Cost Optimization

Benefits

ekSource implemented a RAG-based AI chatbot with a modular, scalable architecture tailored to the client’s needs. The solution included:

Document Processing & Vectorization

Documents were extracted, preprocessed, and indexed using Weaviate as the vector database.
ekSource’s proprietary document extraction pipeline enabled faster text extraction and indexing, reducing implementation time.
Weaviate was chosen over FAISS to support the client’s future multi-storage architecture goals.

AI-Powered Query Resolution

OpenAI’s GPT-4o mini was integrated to generate natural language responses based strictly on retrieved document content.
The chatbot ensured document-grounded responses, minimizing AI hallucinations and improving response accuracy.
The AI model was optimized for low-latency retrieval, ensuring faster response times.

Web-Based Chat Interface (Built with Angular)

This PoC successfully validated the feasibility and business impact of an AI-powered RAG-based chatbot for internal knowledge retrieval. The solution provided an intuitive, efficient, and scalable way for employees to access critical business insights, reducing search time and enhancing productivity.

With this strong foundation, the client can now explore:

By leveraging ekSource’s AI expertise, proven frameworks, and cost-effective delivery model, the client gained a future-ready AI solution at a significantly lower cost and faster timeline than industry alternatives.

ekSource continues to collaborate with clients to build cutting-edge AI solutions that drive business transformation.