Classical machine learning earns its keep on numbers, not chat. We build forecasting, risk scoring, and predictive maintenance models that run in production, not just in a notebook.
Forecasting runs on historical spreadsheets, causing stockouts or capital trapped in excess inventory.
Models that ingest real-time data and automate replenishment on probabilistic demand, not static minimums.
Static rule engines flag an unmanageable volume of false positives in risk and fraud scoring.
Dynamic risk scoring that reads transaction context and routes only high-risk anomalies to human investigators.
Equipment fails unexpectedly despite strict adherence to scheduled service calendars.
Models that watch vibration and temperature streams to predict mechanical degradation before failure.
Historical data becomes a monitored, scored prediction .
For teams making the same forecast, risk call, or maintenance decision on a guess, over and over.
We extract features from your history, train and validate a model, then run it in production with drift monitoring so a stable score, not a stale one, reaches the decision.
Cloud-based monitoring is scaling to offset rising labor and material costs.
Most manufacturers now combine AI with IoT sensors for predictive applications.
Legacy predictive models lose up to 40 percent accuracy on dirty data, so readiness comes first.
Predictive analytics look straightforward in a roadmap deck. Then unplanned equipment failure, fraud false positives, and models trained on dirty data erode the savings the pilot promised. That production layer is where most ML programs stall—and where validated forecasting earns its place.
Share of total production expense that machinery maintenance can reach
NCBI, 2023
Reduction in unplanned downtime predictive analytics can deliver, up to 90 percent
Mordor Intelligence, 2026
Erroneous Medicaid disbursements at a 6.12 percent improper-payment rate
CMS, 2026
Accuracy lost by predictive models trained on poor-quality data
MIT Sloan, 2021
Feature engineering, training, and inference built to run continuously, not once.
Automated alerts the moment live data deviates from the training baseline.
Low-certainty scores route to a human operator, never an automated mistake.
Models sit beside SAP, Oracle, or SQL via secure APIs, leaving the core untouched.
For SLED scope under NAICS 541512, we forecast agency budgets and score eligibility or fraud risk as your subcontractor, never facing the agency.
NDA-first, subcontract-only. We work behind the prime, under your brand. We do not pursue prime contracts and we never face the agency.
Start small, verify first. We begin with a narrow use case on a small, verified dataset and stand up a validation pipeline before scaling.
Models you can audit. Backtested against your own historical data, with documented MLOps pipelines and drift monitoring.
Automated drift monitoring alerts our engineers the moment live data deviates from the original training baseline.
Yes. Models deploy inside your existing virtual private cloud to maintain strict data sovereignty.
We build a parallel system that communicates over secure APIs and leaves your fragile core untouched.
Configurable confidence thresholds route any score below a strict certainty level to a human operator for review.
A common and valid concern. We start with a narrow use case on a small, highly verified dataset and build a validation pipeline before scaling across the enterprise.
Tell us the decision you keep making on a guess. That is usually where the first model goes.
Scope a prediction model
Python 
PostgreSQL