How it works
Geronigo is an AI sales manager. It plugs into the CRM, listens to every sales call, scores each one against your own standard, and coaches every rep daily, citing the exact moment on the recording for every claim it makes. It is already running on live data, on a fifteen-minute clock. Nothing on this page is a mock-up.
Exhibit A
Before reading how any of this works, read the output that matters most: a sales playbook for this team. The business, the method, a phase-by-phase call structure, a never-do list, and every claim quoted back to a real call. It was written with zero consultation, zero interviews, zero meetings, zero contact with staff. It was derived entirely from the numbers: recorded calls, won against lost, and the CRM record around them. It reads like it knows the business, which is remarkable for a document nobody briefed. It will still need refining by the people who actually run the floor.
The worker wakes on the quarter hour and runs the same sequence every time. Analysis runs first so a rep’s coaching never waits on a bulk CRM sync.
Every finished sales call longer than a minute is fetched from the CRM, recording and all, and filed away before anything else happens.
Each recording becomes a transcript with the speakers separated and every line timestamped to the second, so a coaching point can later prove itself against the tape.
Talk time and talk ratio per call: who held the floor, for how long, and how that call compares to the rep's usual shape.
Calls over two minutes are scored one to five per dimension against a rubric for that type of call (a booking call isn't judged like a closing call), with key moments quoted at their real timestamps and a one-line coaching note.
Once a period settles, each rep gets a digest: a headline, wins, watch items, coaching points that cite the tape, and a game plan for today's booked meetings. Dailies and weeklies arrive as a short audio episode.
The rep digests roll up into a team brief: highlights, concerns, and for every rep a one-line read plus one concrete coaching move. It gets an episode too.
Contacts, deals, stage history, conversations and calendars are walked into the warehouse, so every number above has a paper trail. This runs last on purpose: insight is never stuck queuing behind housekeeping.
On the hour, an analytics build rolls everything synced so far into seventeen reporting tables. The dashboard, the stats pages and Ask all read from those same tables. One set of numbers, no side arithmetic, nothing that can quietly disagree with itself.
No person could have compiled this by hand. The raw material is every recorded minute of every call, read in full.
A human manager reviews a handful of calls a week. Geronigo scores every call over two minutes, every day, against the same standard, so the picture is the whole floor rather than the calls someone happened to sit in on.
Every coaching point carries a citation: the call, the second, the quote. Click it and hear the moment. A point that can't prove itself against the transcript is dropped, and the drop is recorded.
Daily and weekly digests are spoken as short audio episodes a rep can play on the drive in: what worked yesterday, what to watch, and lines to use on today's booked meetings.
Not a stat sheet. A read on each rep's state, the single most useful coaching move to make next, and what the funnel did and why.
A model reads your actual won-versus-lost transcripts and drafts the playbook from what demonstrably works here, with quotes cited to real calls. A lead approves it before anyone is scored against it.
“Who had the best connect rate last week?” gets answered from the same tables the dashboard reads, with links to the underlying calls, so the answer and the chart can never disagree.
AI that coaches people has one job above all others: never make things up. Every digest passes hard gates before anyone sees it.
Everything above is built from three feeds: the CRM, the calendar, and call recordings and messaging. That shapes what the numbers can say. Wins are identified from what was said on the call and from how the opportunity was managed in the CRM, and neither is a perfect account of what’s going on. The platform treats both as evidence and cites its sources, but the picture can only be as complete as the data underneath it.
Up-funnel · not yet connected
Which creative started this conversation, what the click cost, which page set the expectation. Unknown today.
Connected today
The slice this whole page is built from: the middle of the funnel, in full.
Down-funnel · not yet connected
What a won deal actually banked, and when. Unknown today.
Connect up-funnel and the stories get specific: cost and quality per campaign, per creative, per landing page. This ad starts conversations that close; that one books meetings that never show.
Connect down-funnel and the numbers become dollars. Cash and gross revenue per rep, per call, per opportunity, and full ROI traced from a specific ad creative through to banked revenue.
There has been zero consultation behind any of this. No sit-down on how the pipelines are meant to work, what the marketing promises, or how a call is supposed to be structured. Everything here was deduced from the raw data alone. That is useful proof that the platform can find the structure by itself, but it cuts both ways. The playbook will need correcting by the people who actually run the floor, and a layer of general business intelligence (the things only you know) still needs to be overlaid on top.
What you see here is what the narrowest slice of data, with no briefing, can already do. It’s the starting point. Wire in both ends of the funnel, fold in your knowledge of the business, and this becomes the sharpest view of it available: how it’s actually going, and where to push to scale it.