Production ML, owned by senior engineers
AI and ML engineering is the work of building, evaluating, and running machine learning in production. We ship it as an embedded senior pod, deployed to your cloud. Custom models, evaluation harnesses, and MLOps from the same team that ships your product.
30-minute call · no pitch deck · no obligation
What AI / ML Engineering includes
Senior-owned, AI-accelerated, and wired into your stack. Not a deck of recommendations.
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.
How we run AI / ML Engineering
Problem framing
We 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
Simple model, clean evaluation set. We find out whether the problem is tractable in a week, not a quarter.
Model development
Iterate on architecture, features, and data. Every run is tracked. Every claim is backed by the harness.
Productionise
Deploy to your infrastructure, wire up observability, document the handoff. The team that built it keeps running it.
When AI / ML Engineering is the right call
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 for AI / ML Engineering
Frameworks
- PyTorch
- TensorFlow
- JAX
- Hugging Face
- scikit-learn
MLOps
- Weights & Biases
- MLflow
- DVC
- BentoML
- Ray
Inference
- Triton
- vLLM
- TGI
- ONNX
- TensorRT
Cloud
- AWS SageMaker
- GCP Vertex
- Azure ML
- Modal
- RunPod
AI / ML Engineering questions, answered
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.
Let’s build it together.
One senior team, one flat monthly subscription, no lock-in. Book a call and we’ll map the fastest path to shipped.