AI-Powered Development
Ship LLM features, copilots and agents into production without guesswork.
Usually enters at the grow stage.
- Artificial Intelligence
- Machine Learning
- AI Integration
What it covers.
We start from a task that costs your team hours and measure the model against it before anything goes live. Features run on a provider-neutral layer, so changing models later is a configuration change.
Most AI projects stall in the same place: an impressive demo that never becomes something people use daily. We work the other way round. Before any model is chosen we find a task your team already does by hand — reading invoices, answering the same twenty support questions, summarising calls — and we measure what it currently costs in hours.
Then we write the test set. Thirty to a hundred real cases from your own history, each with the answer a competent person gave. That set becomes the definition of working, and every prompt, model and retrieval change is measured against it. It turns the project from a matter of taste into engineering: not “this feels better” but “it went from 71% to 88%, and here are the cases it still fails”.
The last decision is what happens when the model is wrong. Low-stakes and easy to spot, the feature can act and show its work. High-stakes — anything touching money, health or legal detail — the model proposes and a person approves, with the approval recorded. Irreversible actions we do not automate at all; we automate the preparation and leave the action to a human.
Who it’s for
- Teams with a repetitive, document-heavy task that already consumes measurable hours every week.
- Product companies who need an assistant or copilot inside an existing application, not a separate chatbot beside it.
- Businesses whose earlier AI pilot produced a demo and then stopped, and who want to know why.
What you get.
Every engagement ends with these in your hands, in your accounts, under your ownership.
- 01
An evaluation set from your own cases
Real examples with correct answers, versioned in your repository. It is the artefact that lets you change models later without gambling.
- 02
The feature, inside your product
Built into the application your users already use, with the interface designed around how wrong answers get caught.
- 03
A provider-neutral model layer
Every model call behind one interface, so moving between hosted providers or to a model you run yourself is configuration plus a re-run of the test set.
- 04
A written data-flow note
Exactly what is sent where, which providers hold what under which retention terms, and which fields never leave your systems.
Built with
- OpenAI
- Anthropic Claude
- Google Gemini
- Self-hosted open models
- Vector search and retrieval
- Python
- TypeScript
- Evaluation harnesses
How it runs from brief to launch
AI-Powered Development follows the same four steps as every discipline here, so adding another one later changes nothing about how you work with us.
- 01
Tell us the outcome
One conversation covers every discipline involved, so you explain the business once, to the people who would do the work.
- 02
Get a written plan
The plan names who does what, in what order and what each part costs, before any of the work starts.
- 03
One team delivers it
Designers, engineers and QA work from the same plan in the same channel, so handoffs happen inside the team.
- 04
We stay after launch
The people who built it keep it running, report on how it performs and keep improving it.
Work in this discipline
Projects where ai-powered development was one of the disciplines on the plan.
Questions we get asked
The ones that come up in the first call. Anything else, ask us directly.
What kind of AI features do you build?
Assistants and copilots inside your product, agents that carry out multi-step tasks, document and email processing, search over your own data. Each one starts from a task your team already does by hand.
Which models do you use?
The one that passes your test set at the lowest cost. We build against a provider-neutral layer, so moving between hosted models, or to one you run yourself, is a configuration change.
How do you know it works before launch?
We write an evaluation set from your real cases first, then measure every prompt and model change against it. A feature ships when it clears the bar you agreed, not when a demo looks good.
What happens to our data?
It stays in your accounts. We use providers with zero-retention terms, keep personal data out of prompts where the task allows, and document exactly what is sent where.
How long before we see something working?
A first working feature, measured against your own cases, usually lands in four to eight weeks. The evaluation set comes first, in the opening week or two, because everything after it is measured against it.
What does it cost to run once it is live?
Model usage is billed by the provider to your account, so you see it directly rather than through us. We size it against your real volumes before building and design the feature to keep it predictable — caching repeated work, and using a smaller model where the test set shows it is good enough.