AI / ML Engineering.
Production AI workloads built on top of an engineered context layer. Credit decisioning, claims triage, demand forecasting, agentic workflows. We don't ship a model without the governance to defend it.
— Models that survive audit, scale, and CTO turnover.
Three workload families.
Decision automation
Credit underwriting, claims triage, fraud, AML, collections strategy. Bounded decisions, audit-required, regulator-watching. The bread-and-butter of grounded enterprise AI.
- Credit & underwriting
- Claims & collections
- Fraud & AML triage
- Model-risk attestations
Forecasting & optimization
Demand, supply, working capital, network optimization. Where 1–2% accuracy gains compound into eight-figure outcomes — provided the context is right.
- Demand & revenue forecasting
- Inventory & supply optimization
- Workforce & capacity planning
- Treasury & FinOps
Agentic workflows
LLM-powered agents that read, summarize, reconcile, and route. Built on the FlexiContext layer so they answer with your KPI definitions, not the model's training-data guesses.
- Document intelligence
- Internal Q&A & analyst-assist
- Reconciliation & exception triage
- Multi-step grounded agents
Four habits that move models to production.
Production AI, defendable.
Production model + grounded inference path
The model, the prompt / feature pipeline, the inference service, the monitoring. End to end inside your platform.
Eval + monitoring suite
Offline regression set, shadow traffic, drift detectors, alert thresholds. Owned by your team, runnable on every change.
Model-risk & governance pack
The artefact bundle for your model-risk committee, your auditor, your regulator. Lineage, bias, override map.
Decommission & refresh plan
The conditions under which the model is retired or retrained — written before launch, not after the incident.