Machine learning development
Models that runinside business software
Forecasting, anomaly detection, OCR, and risk scoring — deployed inside the ERP or platform where somebody actually acts on the number, not sitting in a notebook nobody opens.
500+
Projects delivered
most under NDA
100+
Clients served
5 regions
5
Systems live
in production now
—
Clutch rating
awaiting verified score
What we build
The work itself,component by component
Demand and price forecasting
Sequence models trained on your history, with the confidence band shown next to the number.
Anomaly detection
Baselines per entity so an alert means something unusual for that account, not merely a large value.
OCR and document parsing
Invoices, forms, and scans converted to typed records with a confidence-driven review queue.
Risk and quality scoring
Ranked outputs with the contributing factors exposed, so a score can be argued with.
Training pipelines
Reproducible runs, versioned datasets, and metrics tracked across every retrain.
In-product delivery
The prediction appears in the screen where the decision is made — not in a separate dashboard.
Why it matters
What changesonce it is running
Three things a buyer of this work should be able to hold us to.
Used, not admired — A forecast inside the purchasing screen changes an order. One in a report does not.
Retrainable — Pipelines are scheduled and versioned, so drift is caught by a metric rather than by a complaint.
Explainable enough to trust — Contributing factors ship alongside the prediction so the user can sanity-check it.
Stack we use for this
- Python
- PyTorch
- scikit-learn
- pandas
- Airflow
- PostgreSQL
- Docker
How it runs
Four stages,and you can leave after any of them
Scope
We agree what the system does and what shipping means, in writing.
Build
Short cycles in your repository, running before it is finished.
Ship
Deployed into your cloud account, reachable by a real user.
Stay
Maintenance and roadmap by the same engineers who built it.
Proof
The systemsthat back this page
FAQ
Questionswe get asked
How much data do we need?
It depends on the target, and we say so in the assessment rather than after a failed build.
Who retrains it?
The pipeline does, on a schedule. Your team sees the metrics; we handle the plumbing.
Where does the prediction show up?
Inside your existing software, on the screen where someone acts on it.
Machine learning development