Artificial Intelligence
AI Development
AI Development for organizations that need it done right the first time. Senior engineers, artificial intelligence delivery, and a team that stays on the outcome.
Overview
Most AI projects die in the gap between a convincing demo and a system a business can depend on. The model is rarely the hard part; the hard part is everything around it — retrieval that fetches the right context, evaluation that catches regressions before users do, latency and cost that survive real traffic, and access controls that survive a procurement review. That surrounding engineering is what we build.
We work across the full range: retrieval-augmented applications grounded in your own data, agentic workflows that take real actions against your systems with an audit trail and a rollback path, and custom model pipelines for training, fine-tuning and serving. Where a hosted API is the right call we use one; where data residency or cost rules it out, we run open-weight models on your own infrastructure. The decision is made on your constraints, not on what is fashionable this quarter.
Who it’s for — Teams putting AI in front of customers or into core operations, where a wrong or slow answer has a real cost.
What you get
- An evaluation harness so model changes are measured, not guessed
- Retrieval and guardrails designed as first-class architecture, not a prompt
- Cost and latency budgeted and monitored against real traffic
- Human-in-the-loop and rollback on any consequential action
- Everything in your accounts — no proprietary layer you must keep paying for
How we approach AI Development
We start from the failure you are trying to avoid, not the model you want to use. That means defining what "good" looks like as a measurable evaluation set before we build, so every later change — a new model, a cheaper one, a different retrieval strategy — is judged against real cases rather than a gut feel that it seems better.
From there the model is treated as one replaceable component in a system, behind retrieval, guardrails, caching and a fallback path. That is what lets you swap the model when a better or cheaper one ships without rewriting the product around it.
Signs it’s time
- A promising AI demo has stalled on its way to production
- Answers are confident but wrong, and you have no way to measure how often
- Cost per request or latency makes the feature unviable at real volume
- You need AI to act on your systems, not just chat, with an audit trail
Technologies we build it with
Chosen per problem, not per fashion — this is the stack we most often reach for on this work.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
What changes
Measurable quality
An evaluation harness that catches regressions before your users do.
Predictable cost
Token cost and latency budgeted, monitored and kept viable at scale.
Swappable models
A system where the model is a component you can upgrade, not a rewrite.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How to engage us
Three ways to work with us on this — chosen to fit the problem, not our margin.
- Dedicated teamA standing team that works only on your product, in your rituals and your tooling. Best when the roadmap outlives the project.Ongoing product development
- Staff augmentationNamed senior engineers embedded into your existing team, reporting into your leads. Best when you know what to build and need capacity.Filling a capability gap
- Software outsourcingA defined outcome delivered end-to-end by an accountable team. Best when you want the result owned, not just the hours filled.Outcome-owned delivery
Services in this practice
The specific services that make up this practice.
Related terms
Common questions
How much does ai development cost?
We price ai development by the shape of the work, not a rate card. Most engagements begin with a paid discovery phase so the estimate reflects your real system rather than a guess — you get a range with named cost drivers, and we tell you which decisions move it.
How long does it take?
It depends on scope, which we establish in discovery before quoting a timeline. What we will not do is promise a date and then staff it against whoever is free — you get a real schedule and the senior engineers who will keep it.
Who actually does the work?
Senior engineers, working directly with you. The people in your kickoff are the people on your commits — there is no bait-and-switch onto juniors once the contract is signed.
Can you work with our existing system?
Yes — much of our work is exactly that. We start by reading the system as it is rather than proposing a rewrite; most platforms need a roadmap and a safety net, not a demolition.
Who owns the code and IP?
You do, completely, from the first commit. Code lives in your repositories and infrastructure in your accounts. There is no proprietary layer you need us to keep operating.
Let’s talk about AI Development.
Tell us what you’re building or fixing. A senior engineer reads every enquiry and replies within a business day.