RAG & LLM Engineering
Retrieval and reasoning over your data, including on-premise.
Retrieval-augmented generation, fine-tuning, and on-premise model engineering. Strong angle for regulated and sovereign workloads where data cannot leave the estate.
Where this goes wrong
The patterns we are most often called in to fix.
RAG demos that collapse the moment they meet a real corpus.
Sensitive data that legally cannot be sent to a hosted LLM provider.
No way to measure whether a retrieval system is getting better or worse.
Our process
How we deliver: Discover, Architect, Build, Run
- 01
Discover
Frame the problem with the people closest to it. Map the system. Surface the assumptions and the unknowns.
- 02
Architect
Design the target state and the path to it. Make the trade-offs explicit. Earn buy-in across engineering, security, and the business.
- 03
Build
Cross-functional delivery with a definition of done that includes observability, security, and runbooks, not just feature acceptance.
- 04
Run
Operate with you. Continuous improvement against business outcomes, with FinOps and AI-augmented DevOps baked in.
What’s in scope
The capabilities you draw on across an engagement.
- Corpus engineering: extraction, chunking, embedding strategy
- Vector database selection, hybrid search, re-ranking
- On-premise and air-gapped deployments of open-source LLMs
- Evaluation harnesses, golden sets, and continuous regression testing
- Fine-tuning and parameter-efficient adaptation (LoRA / QLoRA)
- Model-agnostic orchestration across hosted and local providers
Technology footprint
We are pragmatic about technology, and AI-agnostic by default.
On-premise compliance AI on open-source LLMs
Fully air-gapped retrieval and reasoning system over a financial institution's policy and case corpus. No data leaves the estate; every decision is auditable.
Related services
Have a programme that needs rag & llm engineering?
Tell us where you are. We will tell you whether we are the right partner, and how we would shape the engagement.