Diagnosis first
We start by finding out why the pilot fails outside the demo. We read the prompts, the retrieval setup, and the data, then reproduce the failures on your real cases. You get a written diagnosis with the actual causes, not a proposal to rebuild everything from scratch.
Evaluation from real cases
Demos pass because they test happy paths. We build an evaluation set from your real tickets, documents and edge cases, so accuracy becomes a number instead of an opinion. Every change afterwards is measured against it, and you keep the set when we leave.
Cost and latency control
Pilots often burn tokens because nobody watched the bill. We set cost and latency budgets per request, add caching and model routing where it makes sense, and make spend visible per feature. You will know what a month of production actually costs before you commit to it.
Monitoring and guardrails
Production AI needs eyes on it. We add tracing on every request, alerts on failure patterns and drift, and guardrails for the inputs your users will actually send. When quality drops, you find out from a dashboard, not from an angry customer.
An honest verdict
Sometimes the right call is to stop. If the use case cannot reach the accuracy your process needs at a cost that makes sense, we tell you after diagnosis, in writing, with the evidence. That answer costs you one fixed fee, not a year of drift.
Common questions
What is an AI rescue and do we need one?
An AI rescue takes a pilot that works in demos but fails in real use and either gets it into production or gives you an evidence-based reason to stop. You need one if your chatbot, copilot or agent has been in proof-of-concept for months, accuracy is a matter of opinion, and nobody owns a plan to ship it. If it already runs reliably in production, you do not need this service.
Why does our AI work in the demo but fail with real users?
Demos run on curated inputs and happy paths. Real users send messy questions, ambiguous phrasing and data the pilot never saw. Common causes we find: retrieval returning the wrong context, prompts tuned to the test cases, no evaluation set, and no handling of edge cases. The fix starts with measuring failure on your real cases, because until then every debate about quality is guesswork.
Can you take over an AI pilot another team or agency built?
Yes, that is the normal case. We start from the running system and the code, not from tribal knowledge: we map the pipeline, reproduce the failures, and instrument it so behaviour becomes visible. We do not need the original builders in the room, though access to whoever owns the data helps. If a full rebuild is genuinely cheaper than a rescue, we tell you that after diagnosis rather than quietly rebuilding on your budget.
What does an AI rescue cost?
We quote a fixed price after one scoping call and a look at the pilot. The diagnosis phase is its own fixed fee, so you are never committing to the full rescue blind. There is no hourly billing and no open-ended retainer: the price covers an agreed outcome, and if scope genuinely changes we re-quote in writing before continuing. What we will not do is name a number before we have seen the system.
What happens if you conclude our use case is not ready for AI?
We tell you, in writing, with the evaluation results that led to the verdict. Some use cases cannot reach the accuracy the process requires at a cost that makes sense, and pretending otherwise wastes your budget and our reputation. You keep the diagnosis, the evaluation set and a list of what would need to change for it to become viable, so you can revisit when models or your data improve.
Who owns the code and what happens to our data?
You own everything: code, prompts, evaluation sets and documentation transfer to you in full, and we work under an EU contract with a signed DPA. Your data stays in your infrastructure, and where the pilot sends data to a model provider we review that flow for GDPR, use EU hosting where available, and make sure nothing is used for model training without your explicit decision.
How long does it take to get a stuck AI pilot into production?
Diagnosis is short and time-boxed: you get the written verdict and quote quickly, not after weeks of workshops. The rescue itself depends on what is broken, and we put a real deadline in the quote and plan the work backwards from it. What we do not do is open-ended iteration: if the evaluation numbers stop improving, we say so and adjust the plan instead of grinding on quietly.
How we build.
Unit and feature tests with PHPUnit / Pest - standard, not an add-on.
Automated tests and deploys on every push. No manual releases.
Every line reviewed by a senior engineer. No juniors on your budget.
Full source code, infrastructure and documentation transfer on handoff.
Book a free consultation to discuss your project and see how we can help.