Most teams need a partner who ships, not a programme that plans.
AI projects fail in predictable ways. The proof of concept works in a notebook and breaks under real traffic. The eval methodology gets written after launch instead of before. The team that built the prototype is not the team that runs it. The platform partner is happy. The CFO is not.
A focused AI development agency can short-circuit that pattern when one team owns the work from discovery through deployment. Evaluation, monitoring, fallbacks, and the deployment runbook should be part of the agreed build rather than an assumed follow-on hardening project.
That is the model we are built around. Not enterprise consulting. Not staff augmentation. Not a marketplace of vetted developers. A senior team that takes a problem and ships a working AI product against it, with the artifacts and accountability you need to run it in production after we hand it over.
AI capabilities we ship to production
The required architecture, evaluation, monitoring, and handover artifacts are selected for the system risk and written into the engagement scope.
LLM applications
Conversational interfaces, copilots, structured-output systems, and document workflows. Built with prompt versioning, retrieval architecture, latency budgets, and cost ceilings from day one.
RAG and retrieval systems
Production-grade retrieval pipelines over your knowledge base. Chunking strategy, embedding selection, hybrid search, reranking, grounding evaluation, and refresh tooling.
AI agents and tool-use systems
Multi-step agents that call tools, query systems, and act on behalf of users. Bounded autonomy, audit trails, deterministic fallbacks, and human-in-the-loop checkpoints where they matter.
Workflow automation
AI-driven automation across operations, support, finance, and back-office tasks. Built to integrate with your existing systems, not to replace them.
Evaluation and monitoring
Custom eval harnesses tied to your business outcomes. Drift detection, regression alerts, and quality dashboards your team owns after we hand over.
Audit and hardening
AI features built by another team that need governance, eval coverage, security review, or production reliability. We pick up where the previous build stopped.
Four phases with named artifacts at every gate
Discovery produces a scoped build recommendation. Implementation, release, and handover follow only when their acceptance criteria are met. Timing depends on data, integrations, risk, and review requirements.
Published package window: one to two weeks. Use case framing, data and PII mapping, evaluation criteria, model and architecture selection, and a scoped delivery plan. The final schedule is confirmed against access and stakeholder availability.
Published package window: four to eight weeks for a suitably scoped build. The scope defines the working feature, evaluation harness, monitoring, fallback paths, deployment runbook, demos, and acceptance criteria. Complex integrations or assurance requirements can extend it.
Scope-dependent release and stabilisation. Production deployment, observability wiring, alert calibration, runbook walk-through, and measurement against the evaluation baseline proceed through agreed release gates.
Choose your exit. Full handover with documented architecture, eval methodology, and runbooks; or a lightweight retainer where we keep iterating and your team learns the patterns alongside us.
Named artifacts, not a slide deck
The engagement scope selects the artifacts required for your team to operate the system after handover.
Built for AI delivery, not adapted to it
The delivery model is focused on AI builds and the evaluation, monitoring, and operational controls they require.
AI-first from day one
Not a generalist transformation programme. The work is scoped around AI delivery, with evaluation, prompt or retrieval architecture, and production monitoring included where the system requires them.
Senior team, end-to-end
A small senior team owns design, engineering, and delivery together. No layered handoffs between strategists, designers, and engineers. No analyst-led discovery followed by a different team building.
Productized engagements with transparent scope
Discovery Sprint, Build Sprint, Automation Rollout, Audit and Hardening. Named deliverables, week-by-week cadence, exit ramps. Pricing is bespoke per project because every AI build is different, but the scope and shape are never opaque.
Vendor-neutral by design
No cloud partnership quotas. We choose the model, provider, and architecture that fit your product, not what satisfies a partner relationship. If a self-hosted open model is the right call, we do that. If a frontier API fits, we do that.
Where to look next
Deeper detail on the engagements and approach behind this work.
Common questions
What does an AI development agency actually do?
A focused AI development agency designs, builds, and deploys AI products end-to-end. The work spans discovery (problem framing, data and architecture decisions), build (model selection, prompt and retrieval architecture, integration), production (eval, monitoring, fallbacks, deployment runbook), and handover. Done well, the engagement ends with a working AI feature your team can run in production, not a slide deck about what to build.
How is this different from a digital agency that does AI?
Digital agencies typically built their model around web and mobile work and added AI as a service line. Their delivery cadence, team structure, and engagement size reflect that origin. An AI-first agency is calibrated for AI delivery from day one. Eval methodology, prompt and retrieval architecture, production monitoring, and AI risk patterns are part of how we work, not skills we had to recruit for.
What is the typical engagement length?
Our packages publish planning windows: a Discovery Sprint is one to two weeks, a Build Sprint is four to eight weeks for a suitably scoped feature, and Audit and Hardening is two to four weeks. These are package shapes, not a universal delivery promise. Data access, integrations, assurance, and stakeholder review determine the final schedule.
Do you publish pricing?
No. Every AI project is bespoke and the scope drives the cost. Our packages publish week-by-week cadence, named deliverables, and exit ramps so you can compare scope and shape transparently before we quote. Pricing is set per project against the agreed scope.
Where do you work?
We work with mid-market product teams and forward-thinking businesses across the UK and US. Time zones, contract law, and regulatory realities (UK GDPR, EU AI Act, US sector rules) are part of how we deliver. We occasionally work with teams elsewhere when the project fits.
How do I know if my project is a fit?
Book a free 15-minute feasibility triage. We will tell you honestly whether your project benefits from AI-first delivery, whether the timing is right, and what scope makes sense. We turn down projects that are not AI-led because that is what we are built to do well.
Ready to ship an AI product, not a strategy document?
Book a free 15-minute feasibility triage. We will scope what shipping your AI feature actually looks like, give you an honest read on timing, and tell you if we are the wrong fit.