Service · Machine Learning & Predictive Analytics
A forecast you can act on, or an honest no
Prediction is only worth buying when the history is there and a decision changes because of it. We check both first, then build the smallest model that moves that decision.
- Feasibility checked against your real history before any build
- Baseline first: if a simple rule wins, we say so
- Accuracy reported against the decision, not against a leaderboard
- Retraining and drift monitoring included, not sold later
What you get
Deliverables, not slideware
- 01
Data readiness review
How much history exists, how clean it is, and whether it can support the question you are asking.
- 02
Baseline comparison
The naive rule the model has to beat, measured, so improvement is a number and not a claim.
- 03
Model in production
Scoring or forecasting that runs on a schedule and writes into the system your team already uses.
- 04
Monitoring and retraining plan
What is watched, what triggers a retrain, and who is alerted when the numbers drift.
- 01question: which quotes convert within 30 days
- 02history: 26 months · 18,400 quotes · 3 gaps flagged
- 03baseline rule: 41% precision
- 04model: 63% precision at same recall
- 05retrain: monthly · drift alert on -5pp
How it runs
What actually happens, step by step
No discovery theatre. Each step ends with something you can read or use.
- 01
Frame the decision
Which action changes if the prediction is good.
- 02
Check the history
Volume, gaps, and leakage, before any modelling.
- 03
Beat the baseline
A simple rule is the bar; the model has to clear it.
- 04
Ship and watch
Scores land in your tools; drift alerts a person.
Tangible
What lands in your hands
Named objects with a format and a week attached, so the handover is easy to picture.
System blueprint
Services, queues and data paths, drawn once and kept current.
PDF · 6 pagesWeek 2Source repository
Yours from day one. Commit history, README and setup script included.
Git repo + READMEWeek 1Running environment
The thing itself, deployed, with your data and your users on it.
Live URLPhase oneHandover pack
Access, documentation and a recorded session with your team driving.
Recording + notesFinal week
System Blueprint
Every service, queue and data path, drawn once and kept current.
This is the real template, with client figures replaced by representative ones. Read it before you talk to us — if the format is not useful to you, the engagement will not be either.
Before and after
What the change looks like in the week
Red is the cost you carry today. Green is what the system gives back.
Today
- Forecasts made in a spreadsheet from last year's feel
- Nobody can say which leads are worth calling first
- Stock and staffing decided after the fact
- Past models nobody trusts or retrains
After
- A score attached to each record, in the tool you already use
- A measured lift over the rule you use today
- Monitoring that tells you when it stops working
- A written no when the data cannot support the question
Objections
Answered before you ask
How much data do we need?
It depends on the question, but we check before quoting a build. If the history is too thin, we say so and propose collecting it properly instead.
Do you use off-the-shelf models?
Where they win, yes. The point is the decision, not the technique. We use the simplest thing that beats the baseline.
Who owns the model afterwards?
You do. Code, training data references, and the retraining procedure are handed over documented.
Keep going
Where people look next
Related work, the category this sits in, and the proof behind it.
One next step, and it is a paid one on purpose
The AI Opportunity Diagnostic is a fixed-scope engagement. You leave with a ranked plan you can act on with us or without us.
Still sizing the problem? Write to us in your own words first. A person reads it.