Our three practices map to Gartner's four analytics maturity levels — from understanding what happened to deciding what to do next.
Knowledge Layer — Descriptive + Diagnostic
The knowledge layer of Intelligence Engineering. Retrieval, grounding, and governance over your enterprise knowledge — documents, databases, audit trails — so every answer is traceable to a source.
Plugs into
Predictive Layer
The predictive layer. Automated feature engineering, AutoML, and explainable models — trained on your data, deployed on your infrastructure. Your data science team, packaged as agents.
Plugs into
Prescriptive Layer
The prescriptive layer. Scenario generation, simulation, optimization, and policy agents — answering what to do under uncertainty. Runs standalone on assumptions, or calibrated with real data.
Plugs into
The three practices compose to any industry or problem. The four below are domains we've already packaged into deployable intelligences — examples of the framework at work, not the limit of it.
Location · Routing · Allocation
Track assets, analyze location patterns, and optimize routing, placement, and allocation — in real time.
Sensors · Anomalies · Maintenance
Monitor sensors, detect anomalies with AI, and optimize maintenance before equipment fails.
Forecasting · Logistics · Re-planning
End-to-end visibility, demand forecasting, and logistics optimization — from supplier to customer, with real-time re-planning.
Assess · Train · Ready
AI assesses skills, generates personalized training roadmaps, and optimizes your workforce readiness for AI transformation.
Every intelligence above is the same three practices, composed differently. Tell us your challenge — we compose them to fit it.
We host, operate, and optimize it for you. Monthly subscription. Go live in days.
We engineer it, deploy on your cloud or on-premise hardware. You operate. Full control.
Every engagement starts with consulting. We recommend the best fit.
