How accurate are the responses?

Accuracy depends entirely on the quality and currency of the documents you provide, so we do not quote a fixed figure. The design controls for it instead: the assistant answers only from retrieved passages, cites each source, and says it does not know when the material is not there — so a wrong answer is traceable rather than authoritative.

What documents can the AI learn from?

PDFs, Word documents, Confluence pages, web pages, and structured data files. The system supports most common document formats.

How long does implementation take?

Timeline depends on the size of the document set and how complex the knowledge base needs to be. We estimate it properly once we have seen the documents.

Artificial IntelligenceBuilt for internal toolPortfolio build

AI Business Assistant

A retrieval-augmented assistant that answers questions from a business's own documents, with links back to the source.

Project Overview

Developed a retrieval-augmented assistant that answers questions from an organisation's own documents. Documents are ingested, chunked and indexed in a vector database; queries retrieve the most relevant passages, and an LLM generates an answer that cites its sources.

Business Challenge

Answering routine internal questions means searching across PDFs, wiki pages and shared drives by hand. The person who knows a particular answer is often not available, and staff default to asking a colleague rather than looking.

Proposed Solution

Developed a retrieval-augmented assistant that answers questions from an organisation's own documents. Documents are ingested, chunked and indexed in a vector database; queries retrieve the most relevant passages, and an LLM generates an answer that cites its sources.

Technical Challenge

Making sure the assistant answers only from verified documents. Implemented a similarity threshold — below it, the assistant states it does not know rather than inventing an answer, and every response links back to its sources so a user can verify it.

Key Features

✓Natural language query interface

✓RAG pipeline with source attribution

✓Supports PDF, Word, Confluence, and web page ingestion

✓Conversation history & context awareness

✓Admin panel for document management

✓Usage analytics & query tracking

✓Role-based access control

✓API endpoint for third-party integration

Development Process

1Document inventory and knowledge base audit
2RAG pipeline architecture design
3Vector database setup and document ingestion
4LangChain integration with OpenAI
5Frontend chat interface development
6Confidence threshold tuning and testing
7Deployment and team training

Technologies Used

Frontend

React.jsTailwind CSS

Backend

PythonFastAPILangChain

AI / ML

OpenAI GPT-4EmbeddingsRAG Pipeline

Database

Pinecone (Vector DB)MongoDB

What It Does

▸Answers questions using the organisation's own documents rather than general model knowledge
▸Cites the source passage for every answer, so a user can check it
▸Declines to answer when the documents do not support one, instead of guessing
▸Lets staff find policy, process and reference material without searching five systems
▸Gives an admin view of which documents were added and which questions are asked

Business Problems It Addresses

✓Removes the need for one person to be the answer to every internal question
✓Points new starters to the same documented source as long-standing staff
✓Surfaces policy and process answers in seconds rather than by email
✓Shows which questions recur, which is where the real documentation gaps are

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Future Enhancements

Multi-language support for global teamsVoice-based query interfaceIntegration with Slack and Microsoft TeamsAutomated document freshness scoring and alerts