Models that ship, not just notebooks that run
We build production Python systems for machine learning, NLP, and computer vision — trained on your data, deployed on your infrastructure, and maintained after launch.
Where Python AI work earns its keep
Each engagement starts from a specific business problem, not a framework we want to use.
Model development
Classification, forecasting, and recommendation models built in PyTorch and scikit-learn, validated against your baseline.
Language pipelines
Document extraction, classification, and retrieval systems, including fine-tuned and LLM-backed approaches.
Computer vision
Detection, segmentation, and quality-inspection models for image and video pipelines.
MLOps & deployment
Training pipelines, versioning, and monitoring so a model keeps performing after the demo.
Serving & integration
FastAPI services and batch jobs that plug your models into existing products and workflows.
Data engineering
Cleaning, feature pipelines, and storage that keep a model fed with data it can trust.
How an engagement runs
Scope the problem
We look at your data and the decision it needs to support before proposing a model at all.
Prototype fast
A working baseline in days, tested against your real data rather than a public benchmark.
Build for production
Hardened training pipeline, versioned models, and a serving layer built for your traffic and uptime needs.
Monitor and retrain
Drift and performance tracking once it’s live, with a retraining path already in place.