ML & AI Ops, quality and governance
The unglamorous half that decides whether a model is still trusted a year after launch.
The challenge
AI stalls after the pilot
The first model ships on heroics: one team, one launch, everyone watching. Then the data drifts, the author leaves, and the second model never makes it out — the programme quietly becomes a shelf of demos. Meanwhile every function is adopting AI tools on its own — with no shared access control and no record of what acted on what.
Our approach
Operate AI like the software it is
We put the operational machinery around your models and AI tools — deployment patterns, monitoring, retraining, versioning, audit trails — so reliability is a property of the platform, not of the person who built the model. The same discipline covers the AI tools your teams use every day: approved, governed, logged — innovation with the shadow IT taken out.
Results
AI that stays live and keeps paying back
Faster from experiment to production, failures caught by monitoring rather than by the business, and a team that can grow the model portfolio without growing the headcount.
What we do
Training, evaluation, promotion, deployment and rollback. Shadow runs and A/B as standard. One pattern across the portfolio.
- Training, evaluation, promotion, rollback — one pattern across the portfolio
- Shadow runs and A/B as standard before anything touches customers
- Every model versioned with its data and code
Statistical drift detection, recall / precision tracking, data-quality SLAs. Alerts that mean something.
- Statistical drift detection on inputs and outputs
- Recall and precision tracked against business thresholds
- Data-quality SLAs with alerts that mean something
Model cards, lineage, decision logs, regulator-ready packs. Defensible in writing, not just in conversation.
- Model cards, lineage and decision logs maintained as artefacts, not archaeology
- Regulator-ready packs assembled from what already exists
- Approvals and sign-offs recorded where auditors look
Finance, legal, marketing and ops all want AI — and will get it with or without you. We roll out approved AI tools per function on your data, under one umbrella: access control, usage policy, audit log. Innovation with the shadow IT taken out.
- Approved AI tools per function, on your data, under one umbrella
- Access control, usage policy and audit log shared across all of them
- New tools onboarded through a path, not through a workaround
Assistants and agents need running, not just launching — quality evaluations, guardrails, prompt and version management, and a clear record of what acted on what. The operational discipline of software, applied to AI.
- Quality evaluations and guardrails run continuously, not just at launch
- Prompt and version management, with a record of what acted on what
- Incidents handled like software incidents — detected, triaged, learned from
AI watching your systems: anomaly detection on pipeline and platform metrics, agent-assisted root-cause, predictive scaling. Quieter alerts, faster recovery, incidents found before the morning stand-up.
- Anomaly detection on pipeline and platform metrics
- Agent-assisted root cause and predictive scaling
- Alerting tuned down to signals, not noise
Proven in production
AI & Data
Kvika — Five generations of a credit decision engine
Netgíró's credit decisions moved from a bureau score and human judgement to models that learn — halving risk losses, then improving through five model generations without a single rewrite.
Data
Arnarlax — Iceland's largest farmer and producer of Atlantic salmon
Iceland's largest Atlantic salmon producer replaced its source systems and its data platform at once — migrated to Microsoft Fabric without the business losing a reporting week.
AI & Data
Sýn — An AI-native data platform
Iceland's telco and media group replaced a legacy warehouse estate with a self-running Databricks lakehouse — AI on both sides of the platform, and roughly half the run-rate.