Design an agent that turns sales call recordings into CRM updates for a 500-person sales organisation.

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Ingest recorded calls from the meeting or telephony platform, transcribe with speaker diarisation, and use an LLM to extract structured updates: call summary, next steps, stakeholders, objections, competitor mentions, deal stage signals, close-date changes. Map them to the CRM schema, validate against business rules, and present them as suggested updates the rep approves in one click (auto-apply only low-risk fields). Add manager insights and coaching analytics on top, with consent and privacy controls.

Call recordings are transcribed with diarisation and combined with calendar and CRM context for LLM extraction into a schema, mapped to CRM fields and validated, then shown as suggested updates that the rep approves into a CRM write-back or that auto-apply for low-risk fields, with the write-backs feeding pipeline and coaching analytics.

Key decisions

Matching the call to the right opportunity: use calendar invites, attendee emails and account domains. Ambiguity means asking the rep.
Schema per customer: each sales org has its own fields and methodology (MEDDICC, BANT). Make the mapping configurable.
Trust and control: reps hate wrong CRM data. Default to suggestions with diff views; auto-apply only factual, low-risk data (activity logged, call summary note).
Field validation: picklist values, stage transition rules (can't jump from Discovery to Closed Won), date sanity.
Privacy and consent: call recording laws vary by region; customer data in transcripts; retention policies; access limited by role.
Evaluation: a labelled set of 100 calls with correct field values; measure field-level precision and recall; track the rep acceptance rate in production.

Value metrics: CRM data completeness, time reps spend on admin, forecast accuracy, manager visibility.

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Design an agent that turns sales call recordings into CRM updates for a 500-person sales organisation.

Key decisions

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