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.
Challenges
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