Transforming Enterprise Knowledge Retrieval with Intelligent Document Processing and RAG Chatbot

Client

under NDA

Website

under NDA

Industry

Retail

Service Line

Generative AI

Overview

The client had a repository of more than 1,000 complex, unstructured documents, including technical manuals, legal contracts, and historical reports. Employees relied on manual PDF searches to locate specific information, creating operational delays and leaving valuable institutional knowledge isolated across separate files.

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HBM developed an end-to-end Retrieval-Augmented Generation system that digitized, processed, and indexed the document repository. Through a conversational interface, employees could ask questions in natural language and receive fast, relevant, source-cited answers generated from the organization’s documents.

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Client Needs and Challenges:

Large unstructured repository: The organization needed to make information accessible across more than 1,000 complex documents and tens of thousands of pages.
‍Slow and ineffective search: Employees spent considerable time searching PDFs manually, while conventional keyword search struggled with the volume, complexity, and varied terminology of the content.
‍Siloed institutional knowledge: Important information remained distributed across technical manuals, legal contracts, and historical reports, contributing to repetitive internal support requests.
‍Answer accuracy and traceability: Employees needed relevant answers grounded in the organization’s knowledge base and supported by citations to the original sources.

Services Delivered

Document digitization and processing: Applied advanced optical character recognition to digitize the repository and divide its content into searchable chunks.
‍Semantic search architecture: Converted document chunks into dense vector embeddings and stored them in a vector database for semantic retrieval.
‍RAG system development: Created a Retrieval-Augmented Generation pipeline that retrieved relevant context and generated accurate, source-cited answers using a large language model.
‍Solution implementation & Integration: Developed a natural-language chatbot using Python, LangChain, GPT-4, Pinecone, and vector database technology, and integrated into the company’s existing infrastructure and working tools.

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Outcomes

95% reduction in time spent searching for information: Search time fell from approximately 20 minutes to under 30 seconds per query.
Over 1,200 documents spanning 50,000+ pages indexed: The content became available for instant semantic retrieval.
80% decrease in repetitive internal support tickets: Fewer tickets were submitted for knowledgebase-related questions.

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