AI products that survive contact with production.
Most AI work dies between the demo and the deployment. We run generation, marking and evaluation pipelines in production on our own platform, at a scale where quality has to be measured rather than assumed. We build the unglamorous half: evaluation, guardrails, cost control and the human review step that keeps output defensible.
The problems we are normally called in to fix.
The demo works and the product does not
A prompt that impresses once behaves unpredictably across ten thousand real inputs, and you find out from customers.
Nobody knows what it costs to run
Token spend scales with usage and quietly becomes the largest line in the bill. It must be designed for, not discovered.
You cannot tell if a change helped
Without an evaluation set, every prompt change is a guess and every regression is invisible until somebody complains.
What ai product development means here.
The whole product
Not just the model call. Auth, billing, admin, usage limits and the interface people actually work in.
Agents and workflow automation
Multi-step processes that read, decide and act across your systems, with failure paths designed rather than hoped for.
Retrieval over your own content
Answers grounded in your documents and data, with citations, so output can be checked rather than trusted blindly.
Evaluation pipelines
Scored test sets, independent verification and sampled review, so a model or prompt change is a measurement rather than a gamble.
Cost and latency control
Model routing, caching and batching, so the unit economics work at ten thousand users and not only at ten.
The outcome we aim at.
- Quality that holds across real inputs, not curated ones
- A known cost per run, and a plan for when usage multiplies
- Changes shipped against an evaluation set instead of a hunch
- Output a regulated buyer will accept, with a human in the loop
What actually moves the price.
- Whether an evaluation set exists or has to be built
- How much of your own data must be ingested and kept current
- Accuracy requirements and the cost of being wrong
- Expected volume, which drives the model and infrastructure choice
We quote a fixed shape of work with a price and a date on it, rather than an hourly rate that grows.
Sectors that need this most.
AI & SaaS
AI products, agents and automation platforms, and the B2B SaaS they are built into.
Education
LMS platforms, school management systems and student portals for schools, colleges and training providers.
Health & Healthcare Technology
Patient portals, clinic software and digital health products, built for UK data rules.
Straight answers.
Which models do you build on?
Whichever fits, and usually more than one. Routing a cheap model for easy cases and an expensive one for hard cases is often the difference between viable and unaffordable unit economics.
Can you work on an existing AI product?
Yes, and it is often where we add most. Teams frequently have a working prototype with no evaluation, no cost visibility and no guardrails, which is exactly what stops it shipping.
How do you stop it producing wrong answers?
You cannot entirely, so you design for it: grounding in your own data, evaluation sets that catch regressions, confidence thresholds, and a human review step wherever being wrong is expensive.
Do we own the prompts and the data?
You own the code, the prompts, the evaluation sets and the data. We do not hold your product hostage.
Tell us what you need building.
A couple of lines is enough to start. You will get a straight answer on scope, cost and timeline within one working day.