Data Engineering & Analytics

Scoring and decisioning.

Which accounts to review first. Which transactions look unusual. Which customers are likely to lapse. Model development, segmentation and scoring have been part of this team's work for a long time. It is also the area a buyer's auditor will look at hardest — so this page leads with the limits, not the capability.

evidence status · no named scoring engagements published

What this is

Some data engineering work leads naturally to models that score or rank things — prioritising a collections queue, flagging transactions that look unusual, ranking which accounts need review first. We build these only where the problem is genuinely a prioritisation problem and the decision stays with a person.

company delivery history · founder-asserted ·

The same discipline extends to forecasting. Demand, revenue and customer-segment forecasting work the same way here — a model estimates what is likely to happen, and that estimate becomes an input to prioritisation, not a decision. It might rank which segment to review sooner or which demand shift needs a response first; a person still decides what to do with the ranking. The requirements below apply to a forecasting model exactly as they do to a scoring model.

company delivery history · founder-asserted ·

What we require before we build one

Where we stand today

We do not have published, named engagements in scoring or decisioning, and we are not going to imply otherwise. If it matters to your decision, ask us directly on the call and we will tell you exactly what we have done and what we have not.

Start with a call about what you are trying to fix.

Book an assessment call

30 minutes · with the engineer who would scope the work

one-page recap within 2 business days of a call that progresses