Custom models for problems off the shelf can't solve.
End-to-end machine learning engineering — data preparation, model development, MLOps — for the problems where your data and your domain are the moat.
Why this matters now
Generic APIs plateau fast on specialized problems: your defect types, your acoustic signatures, your fraud patterns. Winning on those requires custom models and the discipline to keep them accurate in production.
Service pillars
Problem framing & data engineering
The unglamorous 60%: labeling strategy, leakage prevention, and datasets your models can trust.
Model development
Classical ML through deep learning, chosen for the problem — with accuracy targets in the SOW.
MLOps
Versioned pipelines, drift detection, retraining triggers, and rollback — models as operated software.
What changes for your operation
- Proprietary advantage — Models trained on your data are ones competitors can't buy.
- Production reliability — Monitoring catches drift before customers do.
- Compounding accuracy — Feedback loops improve models with every prediction.
Stack we deploy with
- PyTorch / TensorFlow
- Kubeflow / MLflow
- Feature stores
- Airflow
- Kubernetes
Where this service earns its keep
- Defect classification
- Acoustic and vibration analysis
- Yield prediction
- Risk scoring
Where we deploy it
Questions we hear most
Yes — labeling strategy, tooling, QA workflows, and where useful, model-assisted labeling to cut annotation cost by half or more.
Ready to put machine learning to work?
Book a demo, or start with the AI Readiness Assessment — a 30-minute working session that maps your highest-value first deployment.
- AI that sees, predicts, and acts — not just a dashboard.
- Pilot to fleet rollout in weeks, with a go/no-go you can defend.
- Enterprise-grade security, procurement, and support from day one.
Schedule a demo
See Invexal on your own cameras and data.