AI & LLM Solutions
Language models are genuinely good at a narrow set of things and unreliable at others, and the difference decides whether a project ships or quietly gets shelved. We work out which of your problems are in the first category, then build those properly — measured against a real accuracy target before anyone depends on it.
The problem
You might recognise some of these
Your team spends hours each week reading documents and copying the useful parts somewhere else.
Years of tickets, contracts, and email hold answers nobody can find when they need them.
A pilot demoed impressively, then turned out to be wrong often enough that no one trusted it.
Leadership wants an AI strategy and you need to know which parts of it are real.
You are not sure what happens to your data once it leaves your network.
What's included
What you receive
- A written assessment of which tasks suit a language model and which are cheaper solved another way
- An evaluation set built from your real cases, with a measured accuracy figure before launch
- A working system integrated with your existing data, authentication, and permissions
- Retrieval over your own content, with answers that cite the source they came from
- Guardrails, human review for consequential actions, and a log of every model call
- Cost-per-request modelling, so the running bill is known before you commit
- Source code, documentation, and handover — the same as any other engagement
How it runs
Our process
- 1
Qualify the task
We look for work that is high-volume, language-shaped, and tolerant of review. If your problem is better solved by a search index or a fixed set of rules, that is what we will tell you.
- 2
Build the evaluation first
Before any feature work, we assemble real examples with known-correct answers. Without it "it seems better" is the only available measure, and that is how unreliable systems reach production.
- 3
Prototype against it
Prompting, retrieval, and model choice get tuned against that scored set. Most of the gain comes from how your data is retrieved and framed, not from which model is fashionable.
- 4
Ship with the failure case designed
Every model is wrong sometimes. We decide in advance what the system does when it is unsure, what a human sees, and what it is never permitted to do unsupervised.
Typical tools
- Anthropic Claude
- OpenAI
- Retrieval-augmented generation
- Vector search (pgvector)
- Evaluation harnesses
- Python
- TypeScript
Questions