Sales · scenario 4 of 5
Learning from every call and every lost deal
I build analysis that turns each sales call into a short recap and each lost deal into a clear account of where it stalled.
How it works
- 01 · Event
Call ends, deal lost
A sales call finishes, or a deal is marked lost in the CRM.
- 02 · Data
Only the evidence
Speech is processed temporarily; a lost deal brings its history and call recaps, not the customer file.
- 03 · AI
Facts and turning points
The AI extracts need, objections and next step, and decides where a lost deal stalled.
- 04 · Action
Recap in the CRM
The recap goes to the deal; lost-deal findings reach the sales manager as a short list.
- 05 · Check
Reason compared
The recorded loss reason is compared with the deal history, and gaps are flagged.
- 06 · Human
Manager decides
Which deals to reopen is the manager's call; the system recommends.
What you get
- Managers read a one-page summary instead of listening to recordings.
- Deals worth a second attempt surface the next morning.
- Recurring objections become visible across the team.
Example
A team of six reps loses a third of its offers. The system tags the reason behind every lost deal and call (price, timing, competitor, no decision) and shows the pattern monthly. "Too expensive" turns out to mean the offer came too late, and the sequence changes.
Privacy by design
Store the outcome, not the conversation: audio is processed temporarily. The AI sees one deal at a time, not your whole CRM.
Typical stack
Streaming speech-to-text, EU-hosted · LLM with structured output · CRM API · n8n or Python workflows
Built into what you already run: your CRM, telephony, n8n instance or cloud stay in place. I can also take over an existing workflow or join a running project.
Often combined with
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