Generative AI that knows your business. And shows its sources.
We build enterprise LLM solutions that work on your content, not just the public internet. Knowledge assistants, retrieval-augmented generation (RAG) applications and document workflows, designed with access control, evaluation and cost in mind from day one.
You're in the right place if…
- Teams whose people lose hours searching policies, manuals, contracts or tickets for answers
- Product companies adding generative AI features to an existing application
- Organisations with a promising chatbot pilot that now needs to be accurate, secure and supportable
- Leaders who want generative AI adopted without sensitive data leaking into public tools
What gets in the way.
Confident but wrong answers
Models without grounding in your content invent answers, and users stop trusting the tool quickly.
Messy, scattered knowledge
The content that matters lives across file shares, wikis, ticketing tools and PDFs, in different formats and states of repair.
Permissions that must still apply
An assistant must not show someone a document they could not open themselves.
Unpredictable running costs
Token usage grows with adoption, and without design choices around models and caching the bill grows with it.
Generative AI, end to end.
Knowledge assistants
Internal assistants that answer questions over your documents and systems, with citations back to the source.
RAG applications
Retrieval pipelines covering ingestion, chunking, embeddings, vector search and ranking, tuned against an evaluation set.
LLM features in your product
Summaries, drafting, search and conversational interfaces built into your existing application.
Document understanding
Extracting, classifying and summarising content from contracts, forms, reports and correspondence.
Model selection and optimisation
Choosing the right model for each task, and tuning prompts, context and caching for quality and cost.
A delivery approach you can plan around.
Define the questions
Agree the users, the questions they need answered and a test set of real examples to measure against.
Prepare the knowledge
Connect sources, clean and structure content, and carry permissions through to retrieval.
Build and evaluate
Build the retrieval and generation pipeline, then measure accuracy, grounding and cost against the test set.
Launch and improve
Roll out to users, capture feedback, monitor answer quality and keep the knowledge base current.
Ways to work with us.
Focused prototype
A short build on a narrow use case and your real content, to prove value before committing further.
Production project
A scoped build that takes an assistant or feature into production with integrations and monitoring.
Ongoing improvement
A small team that keeps quality, content and costs in shape as usage grows.
Technologies we work with
- OpenAI and Anthropic models
- Open-weight models where data must stay in your environment
- Vector search and embeddings
- LLM orchestration frameworks
- Python and TypeScript
- AWS and Azure cloud services
Generative AI: common questions.
What is RAG and why does it matter?
Retrieval-augmented generation finds the relevant passages in your own content and gives them to the model alongside the question. Answers are grounded in your documents and can cite them, which makes them far easier to trust and check.
Will our data be used to train public models?
We use enterprise model offerings and deployment options that keep your data out of provider training, and where data must not leave your environment we can use models hosted there. We agree this with you before building.
Can the assistant respect existing document permissions?
Yes. We carry access rules from the source systems into retrieval, so people only get answers drawn from content they are allowed to see.
How do you measure whether answers are good?
We build an evaluation set of real questions with expected answers and score each change against it, alongside user feedback once the tool is live.
Let's talk about your generative ai needs.
Someone from our Data & AI practice will reply within one business day to talk through the use case.