Solution · ML & AI Ops
Keep AI working in the real world.
Most AI programmes launch one model and stall on the second. ML & AI Ops is the difference between AI that keeps earning after launch and a shelf of impressive demos.
Most AI programmes launch one model and stall on the second. ML & AI Ops is the difference between AI that keeps earning after launch and a shelf of impressive demos.
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.
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.
AI that stays live and keeps paying back. Faster from experiment to production. Failures caught by monitoring, not by the business. A team that can grow the model portfolio without growing the headcount.
Mid to late maturity. The right time is the second model — when you realise the first one's deployment process won't scale.
Where this lands in practice. Each row opens with the detail — and what it buys you.
Training, evaluation, promotion, deployment and rollback. Shadow runs and A/B as standard. One pattern across the portfolio.
The payoff. Shipping a model becomes routine, not an event.
Statistical drift detection, recall / precision tracking, data-quality SLAs. Alerts that mean something.
The payoff. You find out the model degraded before your customers do.
Model cards, lineage, decision logs, regulator-ready packs. Defensible in writing, not just in conversation.
The payoff. The regulator's visit is a walkthrough, not an excavation.
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.
The payoff. Innovation keeps its speed; shadow IT loses its reason.
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.
The payoff. The assistant your business relies on stays reliable.
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.
The payoff. Incidents found before the morning stand-up.
Through the Moberg Delivery Framework — the same five stages, governance and engineering standards we run on every engagement, from business case to long-term run. Microsoft and Databricks have both independently validated the practice underneath — but judge us on the second, fifth and twentieth model still alive in production.