Visibility into activity, pipeline, and execution fuels better decisions, faster corrections, and more predictable outcomes - without requiring reps to change their workflow.

arrives, unasked
The answer reaches your team before they think to ask.
Most AI waits. It hands a rep a blank box and a blinking cursor and asks them to know the right question. Most never do, and the tool goes unused. Flip it.
- The deal summary lands in the inbox Monday morning.
- The alert finds the manager the moment a deal starts to slip.
- The account answer shows up inside the chat tool already open on the screen.
No portal. No prompt. No new habit.
one source of truth
One answer everyone can run a forecast on.
On its own, a model answers the same question differently every time you ask it. A foundation underneath gives everyone the same one.
- See the same account the same way — whether you're the rep, the manager, or the CRO.
- One source of truth behind every pipeline review and deal review.
- A record that holds from this quarter to the next — not a fresh guess each time.
03 — answers
Clarity delivered where you need it
You don't have a "checking dashboards" problem. You have a "straight answers at the right moment" problem — in Slack during deal reviews, in your CRM before a call, when the board asks a direct question.
No context-switching. No hunting through dashboards. The right answer, in the right place, at the right time.
04 — decisions
Know what to do next
Revenue leadership is about making the right call with incomplete information. You can see the pipeline. You can read the notes. But you can't easily answer: "Where should I intervene today?" or "How confident should I really be in this forecast?"
Backstory completes the picture by turning activity and reasoning into specific, prioritized actions.
Data
Context
Action
No context-switching. No hunting through dashboards. The right answer, in the right place, at the right time.
from the blog
Your LLM is only as good as the data you feed it.
A smart model with nothing real behind it answers with a shrug. See why the data, not the model, decides whether the answer holds up.
See it in action