Our first purpose-built model reads enterprise sales, consulting, and implementation conversations — transcripts and the client email threads around them — and extracts the knowledge inside: customer questions, concerns, blockers, and the workarounds your best people used to get customers live. Cross-referenced across every conversation and conditioned on your product's context. Every call your strongest employee handles becomes intelligence the whole team keeps.
What it extracts
“Does the bulk import dedupe rows that differ only by whitespace?” The edge-case questions customers actually ask, captured with who asked and how often.
The hesitations voiced but rarely written down — the objections that quietly decide whether a deal or a renewal moves forward.
Where the product stopped an implementation cold — surfaced while it's still fixable, not in the churn postmortem.
The clever thing your engineer did to get the customer live anyway — reusable knowledge, today trapped in one person's head.
Then it cross-references. A blocker from Tuesday's call links to the feature request from last month's email thread and the workaround from another account — demand counted across conversations, evidence pinned to every claim.
How it's built
Notetakers summarize a meeting and forget it. Meeting Intelligence is trained on the structure of enterprise implementation conversations themselves — so it generalizes across companies. Point it at your product context and it reads every call relative to what your product actually does.
Every extraction is grounded in your product's capability map. Signal is relative to the company — the same sentence is noise for one and a feature request for another, and the model knows the difference.
Meeting transcripts and client email exchanges from implementation work — the messy, structured reality of enterprise collaboration, not clean benchmark text.
FDEs, implementation consultants, solutions engineers — the people whose conversations hold the richest product data a company generates.