AI That Knows Your Business

Generic AI tools don't know your business — and shouldn't see your data.

We build Retrieval-Augmented Generation (RAG) systems that let AI answer questions using your private documents and data, securely.

What Is a RAG System?

A RAG system:

💾

Stores Your Documents

Your documents are stored in a vector database, indexed for semantic search.

🔍

Retrieves Relevant Information

When asked a question, the system finds the most relevant information from your data.

🤖

Generates Accurate Answers

AI uses the retrieved context to generate accurate, grounded answers.

No training on public internet data. No data leakage.

What RAG Is Used For

📚 Internal Knowledge Bases

Let employees find answers from company documentation, policies, and procedures instantly.

👥 HR & Policy Assistants

Answer employee questions about benefits, policies, and processes accurately.

💼 Sales Enablement Tools

Help sales teams find product information, case studies, and competitive intelligence.

📖 Technical Documentation Search

Make technical docs searchable and answerable for support and engineering teams.

✅ Compliance & Audit Support

Quick access to compliance documentation and audit trails when needed.

Why RAG Over Fine-Tuning?

⚡ Faster to Update

Add new documents instantly. No retraining required.

🎯 More Accurate

Answers are grounded in your actual documents, reducing hallucination.

🔒 Lower Risk

Your data stays in your control. No sending sensitive data for model training.

📋 Easier to Govern

Clear visibility into what sources were used. Auditable and explainable.

Perfect for businesses with changing documentation.

Our RAG Approach

1

Proper Document Chunking

Documents are intelligently split to preserve context and meaning.

2

High-Quality Embeddings

We use the best embedding models for accurate semantic search.

3

Secure Access Control

Role-based access ensures users only see documents they're authorised for.

4

Scalable Architecture

Built to handle growing document libraries and user loads.

Built for reliability, not demos.

Technologies We Use

🗄️

Vector Databases

  • Pinecone
  • Weaviate
  • Qdrant
  • pgvector (PostgreSQL)
🔗

Orchestration

  • LangChain
  • LlamaIndex
  • Custom pipelines
🤖

LLM Providers

  • OpenAI GPT-4
  • Anthropic Claude
  • Azure OpenAI
  • Self-hosted models

Build a Secure AI Knowledge Assistant

Ready to let your team query your business knowledge with AI? Let's build a RAG system that works for you.