How we think about decision problems.
Worked examples, architecture writeups, and methodology pieces that show how Neptari approaches the problems we’re asked to solve — not as a library of finished deliverables, but as a window into the thinking.
Multi-source property risk scoring.
Seven independent data signals, a configurable weighting layer, and a deterministic BUY / HOLD / PASS recommendation a decision-maker can defend in any review. A worked example of how we design decision intelligence systems.
Social-footprint risk, with governance baked in.
The same multi-source scoring pattern, pointed at a problem most organizations don’t actively measure — cyber-risk exposure inside an executive team’s public social footprint, with consent, redaction, retention, and audit built into the pipeline as first-class stages.
Predictive churn for subscription products
Sequencing cohort analysis, feature engineering, and a calibrated probability model into a workflow customer success can actually use.
Data quality scorecards for operational reporting
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.