Prediction, scoring, and vision models tuned on your operational data
TankAware's predictive maintenance model cut inspection errors 40% and lifted monitoring accuracy 30% for a multi-site petroleum operator — still running in production today.
What model engineering means
Model engineering means training a machine learning model on your own operational data. The model learns to predict an outcome, score a risk, or read an image — not to hold a conversation.
Hashlogics builds these models for prediction, scoring, and computer vision, tuned on your data instead of a generic API. That's a different service from generative AI, which produces text and chat.
What we build
Prediction
A prediction model studies patterns in your data and forecasts what happens next — a machine failure, a missed deadline, a customer about to leave. TankAware's model reads sensor and inspection data from petroleum sites. It flags a tank problem before the tank fails. Think of a smoke detector that warns you early, but tuned to your equipment instead of a generic threshold.
Scoring and classification
A scoring model weighs several signals into one number that ranks or grades something — a risk score, a quality grade, a fit score. Greenlight's model scores a company's ESG performance. It combines AI-read evidence (66% of the weight) with hard data (33%) across more than 50 topics. That turns weeks of manual research into a report in under 10 minutes.
Computer vision
A computer vision model reads an image or video feed and detects, classifies, or verifies something automatically — a defect on a line, an identity at a door, a hazard on a site. Ask us on a call about our current computer vision work.
How a model gets to production
We treat every build as a series of checkpoints, not a straight line to a demo.
Our AI consulting diagnostic tells you whether a custom model is worth building before we start.
- Data audit — we check what data you actually have, and whether it's enough to train on.
- Model selection and training — we pick the simplest model that solves the problem, then train it on your data.
- Accuracy gate — you set the error threshold the model has to clear before it ships. We don't lower the bar to hit a date.
- Deployment — the model goes live inside your existing product or workflow, not a standalone sandbox.
- Monitoring — we track its accuracy after launch and retrain it as real-world data shifts.
Kept accurate after launch
A model that drifts quietly is worse than no model at all. Real-world data changes — equipment ages, behavior shifts, new patterns appear — and a model trained once goes stale.
Every model we ship comes with your choice: an ongoing monitoring agreement, or a documented handover so your team can retrain it.
Built for enterprise and startup teams
Enterprise
Compliance-grade accuracy, audit trails, and integrations built for regulated operations — the way we built TankAware's SOC 2-ready platform for a multi-site operator.
Startup
One production model, shipped into the product you already have — not an open-ended research engagement.
Built and running for real clients.
Oil & Gas · Canada
TankAware
AI + IoT petroleum site management for Sutherland Excavating Ltd.
Read the case study →
ESG / Sustainability · Global
Greenlight
AI ESG and sustainability research platform.
Read the case study →
Trading / FinTech · United Kingdom
Trading CoPilot
Real-time AI trading alerts and execution companion for forex traders.
Read the case study →
