ProductionML,ownedbyseniorengineers
Custom models, evaluation harnesses, and MLOps shipped by the same embedded pod that ships your product. Deployed to your cloud, wired into your stack.
Production ML, not notebooks
Each capability ships with a test suite, a deployment plan, and an on-call owner. Everything else is a prototype.
Custom model development
Classification, regression, ranking, forecasting, recommendation. Trained on your data, evaluated against your business metrics.
Evaluation harnesses
Every model ships with a test suite that catches drift, bias, and regression before production. No black boxes.
Computer vision
Detection, segmentation, OCR, pose estimation. Production-grade pipelines with edge and cloud deployment paths.
Natural language processing
Fine-tuned LLMs, retrieval pipelines, classification, summarisation. Built on your domain data, not generic corpora.
MLOps and deployment
Version-controlled training runs, reproducible pipelines, observability for live models. From notebook to production without the usual chasm.
Data engineering for ML
Feature stores, labelling pipelines, synthetic data generation. The foundation production models actually need.
From problem framing to production
Problem framing
Week 1We translate your business question into a model-shaped problem. Target metric, baseline, success threshold, and failure cost all agreed before anyone trains anything.
Baseline and data audit
Week 1 to 2Simple model, clean evaluation set. We find out whether the problem is tractable in a week, not a quarter.
Model development
Week 2 to 6Iterate on architecture, features, and data. Every run is tracked. Every claim is backed by the harness.
Productionise
Week 4 to 8Deploy to your infrastructure, wire up observability, document the handoff. The team that built it keeps running it.
The problems ML actually solves for you
Search and ranking
Replace rules and heuristics with models that learn from your users. Measurable lift on the metrics you actually report.
Forecasting and planning
Demand, supply, inventory, pricing. Models tuned to the shape of your data and the cost of being wrong.
Classification at scale
Document triage, content moderation, lead scoring, fraud detection. Accuracy you can audit and improve.
Generative and retrieval
LLM-backed workflows with RAG, guardrails, and an evaluation harness that catches hallucination before users do.
Tools we use, picked for your team
Common questions
Whichever fits the problem. We pick the smallest model that hits your target metric, because operational cost matters as much as accuracy.
Your data stays in your infrastructure. We sign NDAs on engagement, assign IP to you contractually, and never use your data to train anything outside your project.
Every model we deploy ships with alerting, rollback plans, and an evaluation harness that runs on live traffic. The pod that built it is on call for it.
Yes. We embed alongside internal teams, share tooling and review practices, and document everything so ownership stays clean after rollout.
A credible baseline in the first two weeks. Production-ready iteration typically follows in four to eight weeks depending on data readiness.
Related capabilities
All capabilitiesReady to ship
faster than you can hire?
30 minutes to scope, stack, and a first-sprint plan. No pitch deck, no pressure.