From model development to production operations — where AI decisions are audited, not just acknowledged.
If you found this page without reading the business context first — here's where AI/ML Engineering fits.
If your organisation can't move AI pilots to production, AI/ML Engineering is the practice that closes the infrastructure, governance, and pipeline gaps standing in the way.
Explore AI ReadinessFor organisations ready to scale beyond pilots, this practice provides the production architecture, team structure, and operating model for enterprise-wide AI deployment.
Explore AI EnablementWhen automation evolves from rules-based RPA to AI-powered decisions — anomaly detection, document intelligence, predictive routing — this is the engineering layer that makes it reliable.
Explore AutomationModels built for your specific business problem — not generic reference architectures. Every engagement starts with a feasibility validation: data quality, feature availability, and business metric alignment before model architecture is decided.
The gap between a validated model and a production AI system is almost entirely an MLOps problem. We design and build the deployment, monitoring, and retraining infrastructure that keeps AI systems performing — not just launching.
In Banking, Insurance, and Healthcare, model decisions have regulatory consequences. Our explainability practice — grounded in EazyML (Gartner-recognised) and the HALO Framework — ensures every model meets the auditability standards that regulated industries require.
Enterprise LLM deployment requires governed infrastructure — not just prompt engineering. Celsior's CAFE platform provides the agent orchestration, RAG pipelines, and compliance controls that regulated enterprises require before LLM systems interact with sensitive data.
Our deepest production experience is in the verticals where model decisions carry the highest stakes.
A structured, time-boxed delivery model built for regulated enterprise programmes. Not open-ended workstreams.
Data quality, feature availability, and business metric alignment checked before build begins. Output: go/no-go report.
Model development, MLOps pipeline, model registry, deployment automation, and monitoring instrumentation.
SHAP/EazyML explainability, model documentation, bias assessment, regulatory artefacts. UAT against defined acceptance criteria.
CI/CD deployment, PACE-powered observability, retraining cadence, drift thresholds, operational runbooks. SLA-governed.
Drift detection, retraining, governance audits, and incident response are core engineering functions in every Celsior engagement. They determine whether an AI investment keeps returning value — or quietly degrades into a liability.
For AI/ML engagements that start with an architecture or build-vs-buy question — a time-boxed consulting engagement producing a decision-grade recommendation before development investment begins.
Explore Delivery ModelFor organisations with a defined architecture and an active programme — a production-configured AI/ML team assembled from Celsior's skills database and deployed within 2–4 weeks.
Explore Engineering PodsThe three verticals where explainability and governance are regulatory requirements — and where our production deployments are deepest.
See Industry PracticesHere are the other three chapters that complete the picture.
The business problem this capability is the technical answer to.
Model development, MLOps, explainability, and agentic AI — in production, in regulated industries.
Pre-configured AI/ML engineering teams deployed in 2–4 weeks.
The Celsior platform ecosystem that accelerates and governs every AI/ML engagement.
Can't find the answer? Talk to our practice lead — 30-minute architecture review, no deck required.
Request an Architecture ReviewWe integrate alongside your team — not above it. Scope boundaries between Celsior-managed components (MLOps pipeline, model registry, monitoring) and client-managed components are documented at engagement design stage, before delivery begins.
Hyperscaler PS defaults to their own cloud, ML services, and tooling. We're platform-agnostic — and we bring proprietary IP (CAFE, PACE, HALO, EazyML) that directly addresses governance and explainability requirements that hyperscaler engagements do not.
Drift management is architecturally embedded — not left to the client post-engagement. Automated drift detection thresholds, retraining triggers, and PACE-based observability dashboards are configured during delivery and handed over with full operational runbooks.
Our model documentation and explainability practices are aligned to OCC SR 11-7 for banking model risk management, EU AI Act risk tier requirements, and HIPAA AI provisions for healthcare. Governance artefacts are designed to be examination-ready from day one.
No deck. No sales pitch. A focused conversation with a Celsior AI/ML engineering lead about your specific delivery constraints.