LLM Integration & RAG
We integrate large language models, retrieval-augmented generation (RAG), and AI agents into your existing product or internal tools — safely and cost-effectively.
Discuss your projectWhat you get
- RAG pipeline over your documents and data
- AI agents and tool-calling workflows
- Prompt engineering and evaluation harness
- Cost, latency, and safety guardrails
- Analytics on AI usage and quality
How we work
- 01
Audit
We map where an LLM genuinely helps and where it adds risk or cost without payoff.
- 02
Ground
We connect the model to your data with retrieval so answers are accurate and attributable.
- 03
Harden
Guardrails, evaluations, and fallbacks make the feature safe to put in front of users.
- 04
Optimise
Caching, model routing, and prompt tuning cut cost and latency without losing quality.
Outcomes
- AI features that answer from your own trusted data
- Lower token cost through caching and routing
- Clear metrics on where AI adds real value
LLM Integration & RAG: FAQs
What is retrieval-augmented generation (RAG)?
RAG is a technique that lets an AI answer using your own documents and data instead of only its training knowledge. Relevant content is retrieved at query time and given to the model, so answers are accurate, current, and traceable to a source you control.
Can you add AI to our existing app without rebuilding it?
Yes. We integrate LLM and RAG features into your current stack through APIs and a thin service layer, so you add AI capabilities without re-architecting the product you already have.
How do you keep LLM costs under control?
We reduce cost with response caching, routing simple requests to smaller cheaper models, trimming context, and setting hard usage limits — typically cutting token spend substantially while keeping answer quality steady.