AI development: assistants and chatbots grounded in your data, not the internet's guesses.
Proven on Manifold, shipping AI compliance features in production
A working slice on your real data early
Quality measured with evaluation sets, not vibes
Running costs estimated before you commit
You have a use case in mind (an assistant for your team or customers, drafting, search, extraction) and you need it built properly: accurate, safe with your data, and affordable to run.
- Chatbots, assistants and LLM-powered features built into your existing site, app or internal tools
- Retrieval over your real documents and data, so answers cite your facts
- Guardrails, evaluation and monitoring, so quality is measured rather than hoped for
- Model and provider selection with running costs estimated up front
- Human-in-the-loop workflows where the stakes demand review
Grounded, guarded, watched.
This is the shape of every AI feature we build: retrieval keeps answers grounded in your documents, guardrails and evaluation keep quality measured, and a human signs off wherever the stakes demand it.
Answers cite your own facts rather than the internet's guesses, and running costs are estimated and capped before you commit.
How an AI build takes shape
Shape
We define what good output looks like and what failure costs, before any model is chosen.
Build
A working slice on your real data early, evaluated honestly, then hardened.
Operate
Monitoring, cost tracking and iteration once real usage starts teaching us.
How quality gets measured.
Every assistant we build goes round this loop until it earns its launch. Click through the five steps.
Step 1: Write the evaluation set with you
We collect real questions from your business and agree, in writing, what a good answer to each one looks like. This happens before any model is chosen.
The evaluation set is yours to keep: it is how you hold us, or anyone after us, to a standard.
AI builds typically from £6,000, with running costs estimated before you commit.
One scoping call with the engineer who would build it, then a fixed written figure with model running costs estimated alongside. Nothing is billed by the hour.
Manifold, our field-service SaaS, ships AI compliance features we designed, built and run in production. That matters because production AI is mostly unglamorous engineering: evaluation sets, fallbacks, cost ceilings and monitoring. We bring that discipline to every build rather than stopping at the demo.
The AI build questions everyone asks
Which AI providers do you build on?+
Whichever fits the job: we are provider-agnostic across the major model APIs and self-hosted options, and we design so you can switch later without a rebuild.
How do you stop it making things up?+
Grounding in your documents, tight scopes, evaluation sets that measure accuracy before launch, and human sign-off wherever a wrong answer costs money.
Who owns what you build?+
You do. The feature is built into your existing site, app or internal tools, designed so you can switch model providers later without a rebuild, and handed over with the evaluation and monitoring in place so your team can see how it is performing.
Ready to see what AI Development looks like on your data?
- Show us the use case. A few sentences about the job and the data behind it; we reply within one working day.
- Scope it with the engineer. The call is with the person who would design and build the feature, not a salesperson.
- See the numbers first. A fixed build estimate in writing, running costs alongside, and a plain 'this is not an AI problem' if that is the truth.