Portfolio

ChatGPT-Based Knowledge Assistant

Concept illustration: An open document binder and laptop with a short answer visibly linked to source cards.
Industry: Knowledge Management / Customer Support
Built for organizations to query massive PDF libraries and extract precise answers via a conversational interface without manual document searching.

Case at a glance · Document search and RAG

From PDF libraries to a conversational knowledge workflow

Teams needed to find information across fragmented PDF libraries without manually searching each document. Indext connected document processing, semantic retrieval and ChatGPT in a conversational interface.

Input
PDF documents containing product, policy or other reference information.
Our work
text cleaning and chunking, vector indexing, contextual retrieval and ChatGPT integration.
Delivered workflow
ask a question, retrieve relevant document sections and use them to produce an answer.
Business use
make existing document knowledge easier for staff and support teams to access.

Problem

The manual retrieval of information from dense document repositories created significant delays in decision-making and support response.
  • Unstructured Data: Difficulty in navigating and searching thousands of pages across fragmented PDF files.
  • Search Limitations: Traditional keyword search failed to capture context or answer complex, multi-part questions.
  • Response Latency: Staff spent excessive time manually cross-referencing documents to find specific policy or product details.

Solution

The system implements a retrieval-augmented architecture to transform static documents into an interactive, searchable knowledge base.
  • NLP Preprocessing: Automated cleaning and chunking of raw PDF text to prepare data for machine understanding.
  • Vector Storage: Indexed document fragments in a vector database to enable high-speed semantic search.
  • Conversational Interface: Integrated ChatGPT to synthesize retrieved data into natural, easy-to-understand answers.
  • Contextual Retrieval: The system identifies the most relevant document sections before generating a response to ensure accuracy.

Tech Stack

  • LLM: ChatGPT (OpenAI API).
  • Processing: NLP libraries for text normalization and chunking.
  • Database: Vector Database for semantic indexing and retrieval.

Results & Impact

  • Information Accessibility: Streamlined the process of finding specific data within massive, unstructured datasets.
  • Support Efficiency: Reduced the time required to resolve complex inquiries by providing instant, cited answers.
  • Operational Scalability: Enabled teams to manage growing document libraries without increasing headcount for data retrieval.
  • Improved UX: Replaced manual document browsing with an intuitive, interactive chat experience.
Generative AI & RAG solutions