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Case Study
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Conversational Chatbot Powered by Document Intelligence

Client

A mid-sized SaaS company specializing in HR and payroll management faced a growing barrier: their support and operations teams struggled to quickly extract key information from a maze of internal documents: policy PDFs, compliance guides, and technical manuals. Manual searching and frequent escalations to subject matter experts slowed down resolutions and increased workloads.

TechTez was brought in to develop an AI-powered chatbot that could extract context-aware answers from unstructured documents using natural language. The solution now powers internal support workflows across multiple departments.

The Challenge

Enterprise teams are often buried under static, unstructured documents in a variety of formats (PDF, DOCX, etc.). Retrieving accurate, contextual answers is slow, labor-intensive, and not scalable, especially when answers are scattered across numerous files.

Key obstacles included:

  • No Contextual Search: Existing document systems lacked the intelligence for natural, context-driven queries.
  • Manual Reviews: Employees wasted valuable hours scanning lengthy documents.
  • Disconnected Answers: Traditional tools made it difficult to link user questions with the right portions of content.
  • Limited Accessibility: No intuitive interface for asking plain-language questions; only tech-savvy users could dig deep.

Our Strategy

TechTez developed an advanced Conversational AI Chatbot using Retrieval-Augmented Generation (RAG) to transform how teams interact with company knowledge.

Architecture & Workflow

Solution Highlights:

  • Document Ingestion & Structuring: All internal documents (PDF, DOCX) are automatically divided into logical, searchable blocks for optimal retrieval.
  • Vectorization & Storage: Each content block is embedded and stored in a lightweight vector database (SQLite) for high-speed, accurate semantic search.
  • Semantic Matching: When users ask a question, the chatbot semantically matches the query to the most relevant sections of content—regardless of wording.
  • AI-Powered Answer Generation: Relevant sections are passed to a Large Language Model (ChatGPT or Ollama Mistral) to generate clear, human-like responses.

Technology Architecture

  • LLMs: ChatGPT, Ollama Mistral
  • Vector Storage: SQLite
  • Programming: Python, LangChain, Retrieval-Augmented Generation (RAG)

Results & Impact

  • Instant, conversational access to any internal document
  • Search time reduced from minutes to seconds
  • No need for manual tagging or indexing
  • User-friendly: usable by non-technical staff
  • Multi-format support (PDF, DOCX, etc.)
  • Scalable, model-agnostic deployment

Outcomes:

  • 40–50% reduction in average document lookup time within the first two weeks
  • 30% decrease in support case resolution times
  • Significant drop in escalations to SMEs, freeing experts for higher-value work
  • Improved response accuracy, reducing follow-up queries

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