Sales reps carry an invisible admin tax: after every call they manually write notes, update stage fields, log next steps, and sometimes remember to create the follow-up task. In practice the CRM reflects what reps had time to enter, not what actually happened. Managers forecast against incomplete data, sequences fire off stale signals, and the gap between the real conversation and the CRM record widens with every call.
We connect your call recording tool to the CRM and build an extraction pipeline that pulls structured outputs — summary, stage update, next-step task, follow-up trigger — and writes them back without rep intervention. We decompose what “a good call record” actually means for your process before configuring extraction rules, so the output matches your stage definitions rather than a generic template. A quality dashboard lets ops monitor extraction accuracy and flag edge cases; the system improves as exceptions get reviewed. Typical savings run two to five rep-hours per week once the pipeline is tuned.
The extraction pipeline is built around your specific stage definitions and your specific field requirements, not a generic sales call summary. We run a scoping session with sales leadership before configuration to agree on what structured outputs are actually needed: which CRM fields get populated, which stage transitions are inferred from call content versus requiring rep confirmation, and what the follow-up trigger conditions are. The agent’s system instruction is written against your process documentation — if that documentation doesn’t exist yet, writing it is the first step in the build, not a precondition that blocks it.
On the integration side, the pipeline connects to whichever call recording platform you’re already using — Gong, Chorus, Fireflies, or a direct VoIP integration depending on your stack. The extraction runs asynchronously after each call; rep workflow is not disrupted. CRM write-backs carry a confidence score for each extracted field: high-confidence fields update automatically, lower-confidence fields are flagged for rep review rather than written blindly. The quality dashboard surfaces flagged records, extraction accuracy rates by field, and the exception log so the ops owner can monitor and improve the rules over time.
This system processes recorded calls; it does not work without call recordings, and it does not replace the sales conversation itself. If your team makes calls but doesn’t record them, recording consent and configuration is a prerequisite. The extraction accuracy depends on audio quality and on how consistently reps follow a discoverable call structure — calls where the rep is doing three things simultaneously and the conversation is unstructured will produce lower-confidence outputs. The system also does not make deal decisions or provide coaching; it surfaces structured data so those conversations happen with better information.
Handover includes the extraction configuration, the field mapping documentation, the CRM update rules, the follow-up trigger logic, and the quality dashboard. The ops owner can add new CRM fields to the extraction pipeline, adjust confidence thresholds, and tune exception routing without a developer. We document the decision logic behind each extraction rule in plain language so the team understands why a field is set to auto-update versus flag-for-review, and can change that threshold as the system matures and accuracy improves.