Forecasts and scores,
running in production
Forecasting, scoring and recommendation models built on your data, deployed behind an API and monitored so you know when they drift.
- Typical
- 8–16 weeks
- Pricing
- Fixed price per phase
- Group
- Product engineering
What you get
Machine learning engineering
Forecasting, scoring and recommendation models, deployed behind an API and monitored in production.
Data review
What you have, what's missing and whether it can answer the question, before any modelling.
The model
Tested against a simple baseline, so you can see what the extra complexity buys.
Deployment
Served behind an API your systems can call, with versioning and rollback.
Monitoring
Accuracy and input data watched over time, with alerts when either drifts.
Where it fits
Good places
to start
- 01Demand forecasting
Stock and staffing planned from forecasts instead of last year's numbers.
- 02Risk and lead scoring
Cases ranked so your team looks at the important ones first.
- 03Recommendations
Products or next actions suggested from what similar customers did.
How it runs
Small steps,
shown every week
- 1–2 weeksDiscover
We map how the work happens today and agree what done looks like.
- 2–4 weeksPrototype
The riskiest flow first, on your real data, while change is cheap.
- 2-week sprintsBuild
Weekly demos and a shared backlog you can reorder at any time.
- OngoingRun
Monitoring, agreed response times and a monthly review of usage.
Questions
Before you start
What if a simple rule works as well?
How do we know it keeps working?
Who owns what you build?
Machine learning engineering
Tell us what
you need.
A 30-minute call with the people who would do the work, then a written proposal.