Production AI
AI treated like any other production system, because that's what it becomes the day your operation depends on it.
The day it stops being an experiment
The gap between a prototype that works and an AI system a business can run is not the model. It is everything around it: what happens when the answer is wrong, when the provider is down, when the data shifts and nobody notices for three weeks.
That gap is ordinary engineering, and it is the part most AI projects skip. We build it: evaluation you can re-run on every change, monitoring that reaches us before it reaches you, fallbacks so a failure degrades instead of stopping, and support from the people who wrote the code.
This is the last step in our AI services. It follows a proof of concept and applies to any agentic AI system we build.
Four things a live AI system needs
Evaluation, not only tests
An AI system does not answer the same way twice. We score behaviour against the cases you care about.
Monitoring that reaches us first
Uptime says the service is running, not that it is right. We watch behaviour, and the alert comes to us.
Somewhere to fall back to
When the model is unsure, slow or unavailable: a rule, a queue, or a person, decided in advance.
Support from the people who built it
The team that wrote it keeps it running. No handover to a desk that has never seen the system.
We already run AI data in production
Our most recent build takes real-time capture from AI detection equipment on refuse trucks into a national operator's systems.
| Item | Result | Notes |
|---|---|---|
| Records exported weekly | 1.1M+ | an automated weekly export into the operator's systems |
| Automated tests | 300+ | running continuously, with monitoring that alerts our team before the client notices |
| Application errors under load | Zero | 3,480 requests at 5 a second - about 17x the expected busy-hour peak, measured by the detection provider connecting to it rather than by us |
| Kick-off to live | Under 4 weeks | at a fixed scope and fixed price - a relatively simple installation |
How an engagement runs
Whether you bring a prototype or a blank page, the work is shaped around what production will demand of it.
Start from where you are
Discovery with your stakeholders reviews the processes, datasets and infrastructure you have now, and tests whether AI is both technically viable and operationally ready. If a proof of concept already exists, we assess what it needs to survive production: model performance, pipeline reliability, integration. Starting from scratch, we build with production constraints in view from day one.
Engineer it for live conditions
Data pipelines are built to hold up in production, not tuned to a controlled prototype. Deployment follows a structured MLOps approach - model packaging, CI/CD, infrastructure and integration - inside your existing technology environment and your data governance, with controls on data access, model outputs and audit trails. Scale is designed in, so performance holds as data volumes and demand grow.
Yours to keep running
After go-live we track behaviour against agreed metrics, catch degradation early and manage retraining as data and conditions change. Everything is documented and handed over as we go, so your team can run and extend the system without depending on us. We stay on support for as long as you want us.
Common questions
What happens when the model gets it wrong?
Something sensible, because we decided in advance what that is. Depending on the job, a low-confidence answer goes to a person, falls back to a rule, or queues for review. What never happens is the process silently carrying on with a wrong answer.
How do you know it is still working in six months?
Evaluation runs on every change, and monitoring watches how the live system behaves on real work. Data moves and models are updated, so accuracy is something you keep measuring, not something you established once at go-live.
Who supports it once it is live?
We do, and it is the same people who built it. Alerts come to us the moment they fire.
Do we have to use a particular AI provider?
No. We will recommend what fits the workload and your constraints, including running a model on your own hardware where that suits the data or the budget better.