Forecasting, fraud and anomaly detection, geospatial and optimization models — engineered with MLOps discipline so they’re reproducible, monitored, and trusted in production, not just in a notebook.
A model that works once on a data scientist’s laptop isn’t an asset — it’s a risk waiting to drift. We bring software engineering rigor to machine learning: tracked experiments, versioned models, and pipelines that hold up when the real world sends data the demo never saw.
From detecting fraud across millions of transactions to forecasting demand and analyzing fleet movement from live GPS data, we build models that answer real operational questions — and stay accurate as conditions change.
Detection at scale with our SIFT accelerator — schema enforcement, engineered features, and validation controls that surface the outliers that matter without drowning teams in false alarms.
Time-series and demand models for planning, capacity, and risk — built to be explainable to the people who have to act on them.
End-to-end pipelines on real GPS and telemetry data — route, movement, and location intelligence delivered through Fabric with a medallion architecture.
Experiment tracking and model management with MLflow, plus AutoML via FLAML — so every model is versioned, comparable, and reproducible end to end.
Streaming models and near-real-time scoring with Real-Time Intelligence — for decisions that can’t wait for a nightly batch.
Advanced mathematical models that turn analysis into the best next action — scheduling, allocation, and operational optimization.
We map your sources, maturity, and constraints — and separate what’s pragmatic from what’s pie-in-the-sky.
A clear roadmap with quick wins that prove value early and a governed design that lasts.
Production-grade delivery with software rigor — tested, reliable, and ready for the real world.
Training and playbooks so your team can operate and extend it on their own.
We go deep in one ecosystem so your solution is coherent, governed, and enterprise-grade — not stitched together from a dozen vendors.
The anomaly models didn’t just flag fraud — they cut the noise our analysts were drowning in. That signal-to-noise ratio is what made them usable day to day.
Tell us where you are. We’ll map the pragmatic path to where the value is.
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