Revenue operations / commercial intelligence
AI · Revenue Intelligence · Automation
Revenue Operations Intelligence Command Center
An interactive revenue-operations product on a synthetic-but-coherent commercial dataset: it scores opportunities with an interpretable model, forecasts weighted pipeline, retrieves grounded account intelligence locally, and recommends next-best actions traceable to the data. It runs live on this page.
Data modeling, analytics engine, retrieval, product engineering
TypeScript · React · deterministic analytics · local retrieval
What you can try
Commercial teams sit on scattered CRM data, accounts, opportunities, activities, and notes, that rarely combine into a prioritized view of what to do next.
The goal was to move from raw commercial data to prioritized action: score opportunities transparently, forecast the pipeline, surface grounded account context, and recommend next steps a rep can trust because each cites its evidence.
Architecture
Synthetic CRM model
Accounts, opportunities, activities, notes (seeded, reproducible)
Feature engineering
Stage, recency, engagement trend, deal age and size
Opportunity scoring
Interpretable win-likelihood with visible contributing factors
Pipeline forecast
Value weighted by stage win-probability
Account intelligence
Local retrieval and a deterministic grounded summary
Next-best-action engine
Rules with cited evidence, priority-ranked
- Seeded, reproducible synthetic dataset (accounts, opportunities, activities), clearly labeled demonstration data, not real customers
- Interpretable opportunity scoring that exposes every contributing factor, no black-box score
- Weighted pipeline forecast using per-stage win probabilities
- Local retrieval plus deterministic grounded account summaries; no external LLM is claimed or required
- Rules-based next-best-action engine where every recommendation cites the data that triggered it
- A live, interactive command-center UI running entirely client-side
A working command center you can use on this page: select an account for grounded intelligence, inspect scored opportunities, read the weighted forecast, and act on prioritized recommendations. It shows the path from commercial data to decision, honestly, without pretending an LLM is running.
Want your CRM to tell you what to do next?
Try the console above, then let's wire the same thing into your real CRM.