Machine Learning & Computer Vision
A model in a notebook is a hypothesis. We deliver models that hold up under real traffic, drift, and scale.
Models trained on your domain, deployed for production.
We cover the full ML lifecycle — problem framing, data pipelines, feature engineering, training, and evaluation — with equal weight on the unglamorous parts: monitoring, drift detection, retraining, and rollback.
Our computer vision work spans detection, segmentation, OCR, and video understanding, and our deep learning practice tunes architectures to the constraints of your latency and cost budgets.
Why teams choose this
Right-sized models
We pick the simplest model that meets the bar, not the flashiest.
Production durability
Monitoring, drift detection, and retraining built in from day one.
Honest evaluation
Metrics tied to business outcomes, validated on held-out reality.
Cost-aware inference
Quantisation, batching, and distillation to control spend.
What we build into every system
Custom model training
Classical, deep, and fine-tuned architectures.
Computer vision
Detection, segmentation, OCR, and video understanding.
Feature pipelines
Reproducible feature stores and data versioning.
Model evaluation
Offline + online metrics tied to outcomes.
Drift monitoring
Detect data and concept drift automatically.
Inference optimisation
Quantisation, distillation, and batching.
ML lifecycle
A reproducible path from raw data to monitored production inference.
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Where it creates leverage
Defect detection
Vision models flag manufacturing defects in real time.
Demand forecasting
Time-series models cut inventory waste.
Document OCR
Extract structured data from scanned forms.
Tools we reach for
Let's build something that ships.
Bring us a problem. We'll tell you honestly whether AI is the right tool — and exactly how we'd build it.