How we think about
decision problems.
Most decision intelligence work is invisible — it lives inside dashboards, models, and policy documents that nobody outside the team sees. This section is a window in: worked examples, architecture writeups, and frameworks that show how Neptari approaches the problems we’re asked to solve.
Multi-Source Property Risk Scoring
A walkthrough of how we’d design a property investment scoring system — seven independent data signals, a configurable weighting layer, and a deterministic BUY / HOLD / PASS recommendation that a decision-maker can defend.
Predictive churn modeling for subscription products
A walkthrough of how we sequence cohort analysis, feature engineering, and a calibrated probability model into a workflow that customer success teams can actually use.
Data quality scorecards for operational reporting
How we approach the gap between “data exists” and “data is usable” — and what a lightweight, owner-accountable scorecard looks like in practice.
Let’s talk about your decision problem.
If you’re combining multiple data signals into a recommendation — or you should be, and it’s living in spreadsheets and judgment calls — we’d like to hear about it.