AI that shows its working
Trained on your conversions, not a generic model. Every score names the factors behind it, every forecast carries its measured error, and every alert says what was observed, what was expected, and how unusual the gap actually is.
What it actually does
Conversion probability
A calibrated percentage per lead, learned from what actually closed in your workspace. When there is not enough history to be honest about, it says so instead of guessing.
Call-prep briefs
Three lines before the telecaller dials: who this is, what has already happened, and the single best next action — generated from the lead’s own history.
Ask your pipeline
Plain-English questions answered from your data. “Which source converted best last quarter for the Pune team?” — with the query it ran, so you can check it.
Anomaly detection
Every metric compared against the same weekday before it. A quiet Sunday is never reported as a crisis, and a genuine collapse always is — with a recommendation attached.
Forecasting
Holt exponential smoothing with a weekday index, scored against a held-out tail of your own series. Every projection ships with its measured error rate.
Coaching signals
Each telecaller’s strengths and weaknesses as percentiles against their own team — not against an invented industry benchmark that fits nobody.
Three things we will not do
The honest boundaries around AI in a CRM — stated up front rather than discovered in month three.
We will not invent a number
When a score cannot be computed — too little history, too small a cohort — it is shown as “not enough data”, never as a confident zero. A fabricated metric is worse than a missing one because people act on it.
We will not train on your data for anyone else
Your workspace trains your model. Nothing crosses a tenant boundary, and your customer data never becomes training material for another company’s predictions.
We will not hide the method
Every insight names its method, its window and its confidence. If a forecast has a 34% error rate, the chart says 34% rather than drawing a clean line and hoping.
Point it at your pipeline
Scoring starts working as soon as you have history to learn from — usually within the first fortnight.