Zoom and Google Meet recordings → transcripts → AI summaries + action items → CRM sync. Built for 10–50 rep teams that need the core features, not the enterprise contract.
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monthly retainer
Who this is for
Sales leader at a 10–50 rep org where reps spend 15–30 minutes after every call on manual notes, Gong's per-seat pricing is hard to justify at current headcount, and coaching is limited by how many calls a manager can actually sit through. You want the captures, the summaries, and the CRM sync — not a platform with features your team won't use for two years.
The pain today
- Reps writing notes for 20+ minutes after every call instead of following up
- Gong's per-seat contract pricing out of reach for a 15-rep team
- Sales managers with no time to review full call recordings
- Action items dying in a rep's memory instead of landing in the CRM
- No visibility into talk ratios, competitor mentions, or objection patterns across the team
The outcome you get
- Recording bot auto-joins Zoom and Meet — zero rep action required
- Transcript + summary + action items posted to CRM within 5 minutes of call end
- Speaker-identified transcripts showing exactly who said what
- Coaching dashboard with talk ratios, keyword flags, and timestamp deep-links
- Deal-level summaries consolidating every call on an opportunity into one view
How the recording capture works
AI sales call summarization starts before the call ends. A bot user joins scheduled Zoom or Google Meet meetings automatically — no rep has to press record, share a link, or remember anything. On Zoom, this runs through the Zoom Marketplace App. On Meet, a bot attendee joins via the Meet SDK. On Microsoft Teams, the Graph API handles it. If your team books through Calendly or Chili Piper, the calendar integration triggers the join automatically.
For transcription, I use OpenAI Whisper (open-source, self-hosted or via API) for most teams — it covers 90% of sales use cases at a fraction of what proprietary options charge. For teams running 500+ calls per month, Deepgram or AssemblyAI give better accuracy at scale. I size the stack to your call volume and budget, not the other way around.
Recording bot auto-joins Zoom and Meet — zero rep action required
Transcription and speaker diarization
Raw audio becomes a speaker-labeled transcript. Speaker diarization — who said what, in which order — is handled by Whisper's multi-speaker model or by Deepgram's diarization API, depending on the platform. Zoom meetings with speaker view enabled typically hit 90–95% attribution accuracy. Google Meet is slightly lower, around 85–90%, because the audio mixing is less clean.
This matters for coaching. A transcript that says 'Speaker 1' and 'Speaker 2' is nearly useless when a manager is reviewing ten calls. One that says 'Sarah (rep)' and 'Marcos (prospect)' is the difference between actionable review and noise. I wire speaker attribution to your meeting platform's attendee list so names come through correctly from day one.
+500%: Lead base growth.
Summary and action item extraction
After the call ends, the transcript goes through an LLM pipeline — Claude or GPT-4, depending on the summary structure your team needs. The output is a 3–5 bullet summary covering what was discussed, key objections surfaced, stated budget or timeline, and specific action items with owner assigned. All of this posts to the CRM call record within 5 minutes.
Action items are not buried in the summary. They come out as discrete CRM tasks assigned to the rep with due dates. Keyword flags — competitor mentions, pricing discussions, specific objection phrases you define — get tagged so managers can filter to them without reading every note. Deal-level aggregation pulls every call on an opportunity into a single 'deal summary' visible on the CRM record, so the next person joining a late-stage call doesn't need to listen to four previous recordings.
CRM sync for HubSpot and Salesforce
HubSpot and Salesforce are the two I build for most often. The call record lands with the searchable transcript, the AI summary, extracted action items, attendance list, and call duration. On the opportunity or deal record, the deal summary consolidates all call insights into one view. Custom fields map to whatever your existing CRM structure uses — I don't force a new object schema.
For the Norte Web Digital CRM build, I connected Claude AI to a similar pipeline for outbound prospecting and the customer grew their lead base by +500%. The pattern transfers well to inbound sales motions: the more context your reps and managers have at their fingertips before each call, the shorter the sales cycle.
Coaching dashboard and talk-ratio analytics
The coaching layer is where call summarization pays for itself beyond time savings. Per-rep metrics — talk ratio, question count, competitor mentions, meeting-to-next-step rate — give managers a data-driven view across the whole team without listening to a single full recording.
Timestamp deep-links let a manager jump to a flagged moment — a competitor mention, an objection the rep fumbled, a budget conversation — and hear that 90-second segment. A sampled 30-minute review session covers what would otherwise take five or more hours of full-call listening. For teams with 20+ reps, this is the only way coaching scales without adding headcount.
Custom build vs. buying Gong
Gong's per-user pricing runs $1,200–1,600 per year, with minimums that push a 10-rep team past $15,000 annually before any add-ons. At 80+ reps with a mature sales motion, Gong is probably worth it — the breadth of features and the depth of their conversation intelligence research is genuine value.
For 10–30 rep teams earlier in their sales motion, a custom build covering capture, transcript, AI summary, CRM sync, and coaching dashboard often runs 30–50% of that annual cost. The AI Automation retainer at $3,999/mo covers build and ongoing maintenance. Transcription API costs — Whisper, Deepgram, or AssemblyAI — run $200–2,000/mo depending on call volume and are billed directly to you. I'll say plainly if your scale and maturity make Gong the cleaner call. The custom approach makes most sense in the 10–50 rep range where you want the core workflow without the enterprise contract.
Call recording compliance and privacy
Recording laws vary by state and country. In the US, two-party consent states require both parties to be notified — the bot's joining announcement satisfies this in most jurisdictions, though the exact language is configurable per region. In the EU, GDPR requires explicit consent with a documented legal basis; I configure consent flows per region before any call is recorded.
Data retention is configurable: 90 days is the default, 7 years for regulated industries that need an audit trail. Customer-facing recordings get more care than internal calls by default. I build the consent language and retention policy with your legal team before the system goes live — not after the first complaint.
Recent proof
A comparable engagement, delivered and documented.
A custom CRM that turned Google Maps into a lead machine
Built a purpose-built CRM for a digital agency that captures leads from Google Maps, reaches them via official WhatsApp (Twilio + Meta API), and uses AI to suggest replies and standardize templates. The system scaled the lead base by over 500%, with 250 new leads entering the pipeline every day.
Read the case studyKeep reading
Frequently asked questions
The questions prospects ask before they book.
Whisper large-v3 and Deepgram both run at 90%+ word accuracy on clear calls and around 85% on noisy or multi-accent calls. Post-processing with an LLM corrects common transcription errors — product names, people's names, industry jargon — using the broader context of the transcript. Summaries hold up better than raw transcripts because the LLM works at the meaning level, not the word level.
Yes, and it's a reasonable choice for 10–30 rep teams where Gong's per-seat minimums are hard to justify. A custom build with capture, transcript, AI summary, CRM sync, and a coaching dashboard covers the features most sales teams actually use daily. The missing pieces compared to Gong are usually the breadth of native integrations and the depth of conversation-intelligence research — worth considering if your team has 50+ reps.
The transcript goes through an LLM prompt engineered to identify commitments made by either party: follow-up sends, demo scheduling, pricing approvals, introductions to other stakeholders. Each item is structured with an owner, a due date where stated, and a reference to the call. These post directly to the CRM as tasks on the contact or opportunity record — not as a note the rep has to parse later.
Both are supported. Multi-party calls are transcribed with speaker diarization — each participant is identified and their contributions labeled. Deal review calls, QBRs, and coaching sessions all work the same way. Attribution accuracy is 90–95% on Zoom with speaker view enabled, slightly lower on Meet. Speaker names pull from the meeting invite's attendee list when available.
Whisper handles around 100 languages. For sales teams running bilingual calls — English and Spanish, or English and Portuguese — transcription and summarization both work in the call's primary language. I can configure an optional translated summary for managers reviewing calls in a language they don't speak. More unusual language combinations are scoped on a per-engagement basis.
The bot introduces itself when it joins with a configurable announcement — this satisfies most US two-party consent requirements. Recordings can be paused mid-call for sensitive moments like legal or budget discussions. Customer-requested deletion is honored. Consent language and retention policy are configured with your legal team before any production recording happens.
Four to five weeks from kickoff to the first rep using the bot in live calls. That covers recording capture, transcription, CRM sync, and the AI summary pipeline. The coaching dashboard and keyword tuning come in the following weeks under the retainer. Transcription API costs — Whisper, Deepgram, or AssemblyAI — and LLM costs are billed directly to you, separate from the $3,999/mo retainer.