We build AI systems that reach production and stay there. Not demos. So far that is $2.5M of work taken off client payrolls.
Thirty-minute audit call with the architect who would run the work. No deck, no commitment.
A pilot stalls because one of these six is missing, not because somebody picked the wrong model. All six come from one team that has been doing full-stack, cloud, and DevOps work together for more than fourteen years, so nothing gets thrown over a wall and nothing arrives half owned.
Retrieval over your own documents, agents that call real tools, and models tuned to your domain. Every build ships with an evaluation harness, so quality is a number your reviewers can check rather than a claim you have to defend in a meeting.
The application around the model. Typed APIs, a real design system, and a front end people are willing to open every day, on web and on mobile.
Ingestion, feature stores, training pipelines, and deployments you can reproduce six months later. The unglamorous layer that decides whether any of this is still standing.
Built for the load spike, not for demo day. Capacity planning, failover, and a cost line that does not ambush your finance team the month usage triples.
Systems that cannot go down, moved forward in increments. We work alongside the estate you already have instead of asking for a rewrite.
Access control, audit trails, and red-team passes, plus the evidence pack your security reviewer or your regulator asks for before anything goes live.
By 2026 almost anyone can build something that works on stage. Far fewer can build something that survives a load spike, a security review, and half a year of ordinary Tuesdays. That second half is the actual work, and it is the half we are built for.
The demo is the deliverable
Production is the definition of done
Partners sell the work, juniors deliver it
The engineers on the first call are the engineers on the code
Strategy decks and maturity models
Working software, measured against your own data
Handover is a slide deck and a Slack channel
Runbooks, alerting, and a named owner, so it still works in six months
Lock-in designed into the architecture
Your repository, your cloud, your weights, handed over in full
You can stop after any phase and keep everything produced up to that point. The gates keep your spend tied to evidence, and they keep us honest about what is actually working rather than what demos well.
We will tell you if you do not need us. That happens more often than you would expect.
These are the sentences that open most first conversations. Nearly all of them come from a pilot that already works and still cannot ship.
The demo works in a notebook and nowhere else.
We take the thing your team already proved and put a service around it, with an owner, a service objective, and a deployment pipeline behind it.
We have years of documents and nobody can find anything.
Retrieval across your archives that is permissioned, cited, and accurate enough that people stop keeping private copies and start getting the hours back.
The core system is old and nobody will touch it.
Incremental modernisation on a strangler-fig path. We work alongside what you have instead of demanding a rewrite.
We cannot tell whether quality is getting worse.
Evaluation suites, drift detection, and regression gates in CI, so quality becomes a number on a dashboard instead of a complaint from a user.
Our AI spend is growing faster than the value it returns.
Model routing, caching, quantisation, and an honest build or buy call. Often the answer is a smaller model doing a narrower job.
Legal will not let us put this in front of customers.
Guardrails, audit trails, human review where it matters, and the evidence pack your reviewers need in order to sign off.
Selected work
Publicly referenceable work, all of it live and carrying real traffic rather than sitting in a sandbox waiting for a decision. Engagements under NDA are available as named references on a call.
Agentic automation
An authoring environment for browser-operating agents. Runs are recorded and replayed deterministically, so a failing automation can be stepped through rather than guessed at, and a non-engineer can build one without opening a terminal.
Market intelligence
A research engine that ingests noisy public signals and resolves them into ranked trend reports you can trace back to a source.
Education
A language-learning app with real-time pronunciation scoring and feedback that adapts to the mistakes each learner keeps making.
Engagements
There is no rate card, because scope drives cost and every stalled build is stuck for its own reason. We do commit to a number before work starts, and it holds.
Fixed scope, 1 to 2 weeks
A senior architect and an engineer stress-test the problem and hand you an implementable plan, whether or not you build it with us.
Named team, 3 to 9 months
Where most stalled pilots startFor a pilot that works and still cannot ship. A named architect and engineers take what your team already proved and build the half nobody scoped: deployment, evaluation, access control, audit trail, and the on-call rota that keeps it alive after we leave.
Retained, rolling
Our engineers work inside your team and your process, to raise the bar on a platform you already run rather than to build a new one.
Bring whatever you have, however rough. We will tell you which of these three fits and what it is likely to cost.
If yours is not here, ask it on the call. We would rather answer it before you commit than after.
Usually because the demo was scoped and the production system was not. Deployment, evaluation, access control, audit logging, cost ceilings, and a named on-call owner are a separate body of work, and it is normally larger than the pilot itself. Closing that gap is what Production Rescue exists to do.
Thirty-minute audit call with an architect and an engineer. You leave with a straight answer on what stands between your build and production, what it costs to close that gap, and where it is most likely to go wrong. No deck, no discovery fee.
