Secure AI that knows your business
Retrieval-augmented generation systems that let AI answer questions using your private documents and data, securely, with citations.
Part of our AI Development services
The full scope, in one team
- Vector database design & indexing
- Document ingestion pipelines
- Semantic search & retrieval
- Citation & source attribution
- Access control & data privacy
- Accuracy evaluation & tuning
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
From idea to shipped, without the drama
01
Understand
We map the business problem and the workflow before a line of code.
02
Shape
A lean, costed plan, what to build first and how we prove value fast.
03
Build & ship
Working software in weeks, secure and production-ready.
04
Scale
We stay in for performance, growth and new features.
RAG Systems FAQs
What is a RAG system?
RAG (retrieval-augmented generation) connects a language model to your own documents and data, so it answers using your information rather than general knowledge, with citations back to the source. It is the reliable way to build AI over private or company-specific content.
Is our data safe in a RAG system?
Yes, we design RAG systems so your documents stay in your control, with access controls and private or self-hosted deployment options. Your data is not used to train third-party models.
Ready to talk RAG Systems?
Tell us what you’re building, your timeline, and the number you want to move. We’ll come back with a straight answer.