Generative AI
Most generative AI projects die somewhere between the demo and the deadline. We build the version that survives the gap.
The demo problem
Generative AI demos beautifully. That's the trap.
A model that produces one impressive answer in a meeting will produce a hundred answers a day in production, and roughly three of them will be confidently, fluently wrong. In a demo that's a curiosity. In a customer-facing product it's a refund, a complaint, or a regulator.
The engineering that separates those two states — evaluation sets, guardrails, fallbacks, retrieval grounding, human review on the paths that matter — is invisible in a demo and is most of the actual work.
That's the work we do.
What we build
- Content and drafting features
- Generation embedded inside your product, tuned to your voice, your format, and your constraints. Email drafts, report summaries, product descriptions, first-pass documentation.
- Structured extraction and transformation
- Turning unstructured input into clean, validated data your systems can act on. The output is JSON your database accepts, not prose someone has to re-read.
- Internal copilots
- Assistants that know your product, your policies, and your history, sitting inside the tools your team already uses.
- Multimodal features
- Systems that read images, documents, and audio alongside text, where the input isn't tidy.
How we build it
- We define "good" before we start
- Every project begins with an evaluation set drawn from your real data — the actual inputs, including the awkward ones. We report measured accuracy against it, and we show you the failures, not just the average.
- We ground the model in your material
- Retrieval over your own documents, with citations back to source. A model answering from your policy library is a different risk profile from a model answering from memory.
- We design for the wrong answer
- Every generative system fails sometimes. The design question is what happens next, and the answer is never "it fails silently." Confidence thresholds, escalation paths, and human review where the stakes justify it.
- Your data stays yours
- Enterprise API tiers with retention disabled, or open-weight models hosted in your own infrastructure where compliance requires it. We're specific about where data goes and what's kept.
A sensible way to start
One feature. Fixed scope, fixed fee, `[X weeks]`, with a measured accuracy number at the end. If the numbers work, we scale it. If they don't, you've learned that for the price of a pilot instead of the price of a programme.
Before you ask.
Whichever fits. Commercial models from OpenAI, Anthropic, or Google, or open-weight models you host yourself. That decision follows from your privacy, cost, and latency requirements — not from what we prefer.
AI Agents
A chatbot answers a question. An agent does something about it. That difference is the entire engineering problem.
Intelligent SystemsKnowledge Search (RAG)
Your organisation already knows the answer. It's in a PDF, in a folder, that someone left in 2022.
Intelligent SystemsDocument Processing
Somewhere in your business, a person is reading a PDF and typing what it says into a form. Possibly right now.
Tell us what you're trying to build.
A 30-minute call, no charge and no pitch deck. Describe the problem and we'll tell you how we'd approach it, roughly what it costs, and whether we're the right team for it. If we're not, we'll say so.
30 minutes · No charge · No deck