AI & Machine Learning Implementation
Machine learning that earns its place in production.
Most AI projects stall between a promising notebook and a dependable system. We start by agreeing on the business metric and a baseline to beat, then build the data pipeline, the model and the monitoring together, so the model keeps working after the demo.
Schedule 1
What is included
- B-1.1
Feasibility and baseline
A short, paid study that tells you whether the data can support the goal, and what a simple baseline already achieves.
- B-1.2
Model development
Forecasting, classification, anomaly detection, computer vision and recommendation models, evaluated on held-out data that resembles reality.
- B-1.3
MLOps
Versioned data and models, reproducible training, automated evaluation and safe rollout behind feature flags.
- B-1.4
Monitoring and drift
Alerts when inputs or accuracy drift, with a retraining path that does not need us.
- B-1.5
Integration
Models served behind an API your product already knows how to call, with latency and cost budgets.
Schedule 2
Typical projects
- Demand and inventory forecasting
- Document and image classification
- Fraud and anomaly detection
- Quality inspection with computer vision
- Recommendations and personalisation
- Churn and lead scoring
Materials
- Python
- PyTorch
- scikit-learn
- XGBoost
- pandas
- MLflow
- ONNX
- Ray
- Airflow
Schedule 3
How it runs
- 01
Discovery · 1–2 weeks
We map the problem, the users, the data and the constraints, and agree what success will be measured by.
- 02
Blueprint · end of discovery
Architecture, scope, milestones and a fixed price for the first build phase, all in writing. You can stop here and keep the blueprint.
- 03
Build · weekly demos
Short iterations with a working demo each week, automated tests from day one and a shared board you can always see.
- 04
Launch & handover · planned cut-over
Staged rollout, monitoring switched on, documentation and a recorded handover session with your team.
- 05
Run & improve · optional
A monthly support plan, or nothing: the handover is written so that you never need us to keep the lights on.
Schedule 4
Common questions
We are not sure AI can solve our problem. Where do we start?
With a two-to-three-week feasibility study. You get a written answer, a baseline and a recommendation, including "do not build this" when that is the honest result.
How much data do we need?
It depends on the task more than on a number. Some problems work with a few thousand labelled examples or a pre-trained model; others need years of history. The feasibility study measures it on your data.
Will the model keep working after you leave?
That is the point of the MLOps work: monitoring, documented retraining and a handover session with your team.