Six ML capabilities, one delivery standard.
Each pillar below is a pattern we've built and hardened on real engagements — not a slide-ware category. "Built on" points to the case study it came from.
Fraud, Risk & Intent Scoring
Slot-based composite scoring engines that reconcile many partial, sometimes-contradictory data sources into one ranked, confidence-aware score — for credit risk, transaction fraud, AML pattern flags, or lead/deal intent.
Revenue & Pricing Intelligence
Forecasting and elasticity models that surface revenue leakage, recommend price and promotion adjustments, and quantify the impact before a change goes live.
Inventory, Demand & Capacity Planning
Planning and forecasting models that blend real production mix, lead times and demand signals — from shelf-level inventory down to per-station line-balancing on a factory floor.
Computer-Vision Quality & Inspection
Golden-baseline matching and defect-segmentation pipelines that turn subjective visual inspection into a measured, spec-referenced pass/fail with full audit trail.
AI Inferencing Layer & Edge Deployment
The infrastructure that makes "no subscription LLM" real: containerized inference services, on-prem GPU sizing, FastAPI/Oracle/Postgres integration, and model versioning with rollback.
Data & Decision Infrastructure
Audit ledgers, KPI engines and portfolio-level dashboards that sit on top of the models — so a score or a rebalance is never a black box to the people who have to act on it.
What "AI inferencing layer" actually means here.
Data ingestion
Multi-source parsers with structural validation — CSV/XLSX exports, ERP tables, DB snapshots.
Model & scoring core
Purpose-trained models or deterministic scoring logic — whichever the decision actually needs.
Serving & integration
FastAPI/REST services in front of the model, wired into existing dashboards and databases.
Audit & governance
Every run logged to Postgres/Oracle with full lineage — inputs, model version, output, timestamp.