Discovery
A short, structured look at the systems, people and constraints already in play.
Service
Machine learning
Demand forecasting, churn and credit scoring, anomaly detection and predictive maintenance, scoped one decision at a time.
Scoped to one decision at a time
We do not start with a model. We start with the decision somebody makes today, how they make it, and what a better version would be worth. If that number is small, we say so before anyone writes code.
What we build
Demand forecasting, churn and credit scoring, anomaly detection, predictive maintenance. The techniques are not exotic; the value comes from wiring them into a decision someone already owns.
Every model ships with an evaluation set, a monitoring plan, a named owner and a rollback path.
Pilots that are designed to reach production
The most common failure in this field is a model that works in a notebook and never leaves it. We design the production path in week one, even when it will not be walked until month four.
Saying no
Four times we have run a pilot, reached the agreed threshold, and still recommended against scaling because running cost outweighed the gain. Writing that down is cheaper for you than discovering it in year two.
What we do
The activities this covers
Design
An approach written in plain language, reviewed with your team before anyone writes code.
Delivery
Built in your cloud and your repository, demonstrated weekly against working software.
Validation
Tests and measurement against the success criteria agreed at the start.
Documentation
Runbooks and decision records written during the work, not reconstructed after.
Handover
Training for the people who will own it, scoped from the first week.
What you get
Deliverables, not slideware
- A repository you ownTransformations, infrastructure and CI configuration, commented and reviewed.
- Test suiteTests running on schedule, with alerts routed to a channel your team reads.
- Cost baselineWhat the work costs to run per team, with a monthly budget alert.
- Architecture diagramCurrent state and target state, kept up to date through the engagement.
- RunbooksWhat to do when something fails at 3am, written for someone who did not build it.
- Two weeks of trainingHands-on sessions with the analysts and engineers who take it over.
Related work
Where this service led the engagement
Ridgeline Bank · Financial services
A month-end close that took eleven days
Finance rebuilt the same figures by hand every cycle. We modelled the ledger in the warehouse, defined forty metrics once, and automated the reconciliation.
11 → 3 days to close
Hollis Logistics · Logistics
A warehouse bill that doubled in a year
Nobody could see which queries cost what. We added cost attribution per team, rewrote the twelve worst models, and set a monthly budget alert.
-46% platform cost
Questions
Questions about machine learning
Still unsure? Call (216) 123 45678 and ask for whoever is on the desk.
Ask a questionNo. Most engagements improve what you already run. We only recommend migration when the current platform cannot meet a requirement you have already committed to.
You do. Everything is written in your repository, under your cloud account, on open tooling. There is nothing to transfer at the end because it was never ours.
That is the normal arrangement. Your engineers review our pull requests and we review theirs. Teams that stay out of the work rarely take it over successfully.
First working increment usually lands inside three weeks. We demo weekly against running software, not plans.
Tell us which number you cannot trust
Send one paragraph about the report that always starts an argument. A data lead, not a salesperson, replies within one business day.