MLOps and monitoring that keeps your models working in production
We treat a deployed model as a living system, with monitoring, drift detection, retraining, and audit trails built in from the start.
A model that performed well in a notebook is not a finished thing. Once it meets real traffic, its inputs shift, its accuracy drifts, and its decisions start to matter to people. The work that keeps it honest is engineering, not a one-time launch.
We build the operational layer around your model so it stays accurate, observable, and accountable long after the first deploy. This is the production half of predictive analytics, done by the same people who keep our own product running.
within bounds, alert armed
A model left alone after launch quietly stops being right
Most models are scored once, at launch, and then left alone. The world they were trained on keeps moving. Customer behaviour changes, an upstream data source quietly changes format, a category that was rare becomes common. The model keeps returning confident answers while its real accuracy falls, and nobody notices until a decision goes visibly wrong.
By then the questions are hard to answer. Which version made this prediction, on what inputs, and why. When did the quality start to slip. Who signed off on the last retrain. Without monitoring, logging, and a clear retraining path in place, a model in production is a liability you cannot see, and increasingly one you have to explain to a regulator.
Production is a loop, not a launch
A live model runs in a continuous loop, and we build the operations that keep it turning.
Deploy
The model ships into real traffic.
Monitor
Accuracy, latency, inputs, outputs are tracked.
Detect drift
Shifts in data or quality raise an alert.
Retrain
A candidate is validated, then promoted.
Hand over
Your team runs it, with us out of the loop.
Audit, design, build, hand over
Small reviewed increments on infrastructure you control. We finish by handing the system to your team, not by keeping you dependent on us.
Audit
We look at what you already have in production, where the model is observed and where it is blind, and we document the real risks in plain terms before proposing anything.
Design
We agree what to monitor, which drift signals matter, when retraining should trigger, and what the audit trail must capture, sized to your regulatory exposure and team.
Build
We implement the monitoring, alerting, retraining, and logging into your existing pipeline, tool-agnostic and on infrastructure you control, with the work reviewed in small increments.
Hand over
We document how it runs, train the people who will own it, and leave you able to operate and extend the system without us in the loop.
One incident, start to finish
A real pass through the loop above, logged as it happened: the alert, the diagnosis, and the recovery.
- 03:14+0m
Alert fired
Drift on three input features crosses the threshold and pages the on-call engineer automatically, before anyone is watching a dashboard.
- 03:19+5m
Diagnosed
The audit log traces the cause in minutes: an upstream schema change, with the model behaving exactly as trained.
- 09:02+5h 48m
Retrain triggered
A candidate model trains on the corrected data and is validated against the live one before anything ships.
- 09:41+6h 27m
Recovery confirmed
The candidate is promoted, the old version retired, and the entire sequence is written to the audit trail.
Built for the EU AI Act from the start
Record-keeping, transparency, and human oversight are obligations for many AI systems, not optional extras. We map which ones apply to yours and build them into how the model is logged and operated.
- pred_7f2a1v14.209:41:03Logged
- pred_7f2a2v14.209:41:07Logged
- pred_7f19ev14.103:14:02Flagged
- pred_7f1a0v14.103:12:55Logged
We are engineers, not your legal counsel. We make the system auditable and defer formal compliance sign-off to your advisors.
What you are left with
A live monitoring view
Dashboards and signals for model accuracy, data quality, and behaviour over time, readable by both engineers and the people accountable for the decisions.
Drift and quality alerting
Configured detection that tells the right person when inputs or predictions move outside agreed bounds, wired into the channels you already use.
A documented retraining process
A defined, repeatable path for refreshing the model, validating the candidate against the current one, and promoting it safely with a rollback option.
An audit and logging layer
Versioned records of predictions, inputs, and model changes, structured so they can answer questions from your own teams or an external reviewer.
An operations handover
Runbooks and a walkthrough that leave your team able to run, debug, and extend the system, with the EU AI Act obligations clearly mapped.
We run this discipline on our own product, which monitors its rules across 55 VAT jurisdictions, where a wrong, unexplained result is not an option, and keeps the system logged and maintained to the same production-grade standard we apply for clients. That product is Pileform, in daily production use. This page is the operational continuation of our predictive-analytics work: the same model, kept honest after it ships.
Keep your model honest after launch. If you have a model in production and no clear view of how it is performing, that is a risk worth closing before it surfaces on its own. Bring us what you have and we will start with an honest audit of where it is observed and where it is blind.