PRD-08 · AI
AI & RAG
Self-hosted retrieval-augmented generation
Overview
Your models, your data, your network.
The reason most organisations can't use AI is not capability, it's custody: the useful answer needs the sensitive document and the sensitive document can't leave the building. We build retrieval-augmented generation that runs where the data already lives - on your servers, on your network, air-gapped if that's the requirement - so the model reasons over your knowledge base without a byte of it reaching a third-party API. It is the same sovereignty discipline behind our defence work, applied to language models.
What it does
Inside the system.
RAG
Retrieval-augmented generation
A pipeline over your own documents - vector search, grounded generation and citations back to the source - so answers are traceable, not hallucinated.
LLM
LLM integration & fine-tuning
Open-weight models run locally, fine-tuned on your domain where it earns its keep. No per-token bill, no data egress.
DOC
Document intelligence
Extraction, classification and summarisation over the unstructured pile - contracts, reports, correspondence - that no one has time to read.
FLW
Workflow automation
Human-in-the-loop automation: the model drafts and routes, a person decides. Judgement stays where it belongs.
DSP
Signal & sensor AI
The same practice behind our AI adaptive noise cancellation - machine learning on radar, sonar and sensor data, at the edge.
Deployment
Where it runs.
On-premise, private cloud, or fully air-gapped. Nothing calls out: the models, the vector store and the documents all sit inside your perimeter.
Start here
See AI & RAG
against your problem.
Tell us what you're trying to train, track or communicate through and we'll tell you honestly whether AI & RAG fits - or whether you need something else built.
Start the conversation