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Sybill

AI sales assistant that summarizes calls and updates the CRM

AI AgentSupervised

Last reviewed 2026-06-19

Sybill is an AI sales assistant for B2B revenue teams that records and analyzes sales calls and turns each conversation into structured output. It captures calls across video conferencing platforms, generates summaries with key takeaways and next steps, analyzes conversation and buyer behavior, drafts personalized follow-up emails in the rep's voice, and auto-fills CRM fields such as MEDDPICC and BANT. It is built on a proprietary retrieval system layered over underlying language models. Beyond per-call automation, Sybill provides deal intelligence across full deal history (calls, emails, CRM, Slack), a deal workspace and pipeline view, coaching diagnostics, and an "Ask Sybill" conversational interface for cross-deal queries. It is sold per seat with published pricing and targets AEs, sales reps, and managers who want to cut post-call admin and keep the CRM current.

What it can do

  • Record and summarize calls

    Supervised

    Joins sales calls and produces summaries with key takeaways and next steps after the call; the rep reviews and edits.

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  • Draft personalized follow-up emails

    Copilot

    Writes follow-up emails in the seller's voice for the rep to review and send.

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  • Auto-fill CRM fields

    Supervised

    Populates MEDDPICC, BANT, and custom CRM fields from call and deal data, with humans overseeing and correcting.

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  • Answer cross-deal questions (Ask Sybill)

    Copilot

    Surfaces deal intelligence and answers questions across the deal history when prompted by a user.

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Strengths

  • +Strong automatic per-call output (summaries, email drafts, CRM autofill) that cuts sales admin
  • +Broad integration coverage including conferencing, CRMs, dialers, and an API
  • +Transparent published per-seat pricing

Limitations

  • The richest features (full CRM autofill, cross-deal intelligence) sit on the higher Business tier
  • Underlying language models are undisclosed
  • Agentic marketing overstates reality: outputs need human review before they are sent or saved

Overview

Sybill is an AI sales assistant that records and analyzes sales calls, then turns each one into summaries, follow-up email drafts, and CRM updates. It targets B2B revenue teams that want to cut post-call admin.

What it does

After a call it generates a summary with takeaways and next steps, drafts a follow-up email in the rep's voice, and auto-fills CRM fields like MEDDPICC and BANT. "Ask Sybill" answers cross-deal questions on demand. Summaries and CRM writes run on their own but are reviewed by the rep (supervised); email drafts are copilot-level (the human sends).

Integrations & setup

Connects to Zoom, Google Meet, and Microsoft Teams for capture; Salesforce, HubSpot, and other CRMs for sync; Gmail and Slack; plus dialers. It offers a REST API and webhooks, with MCP access on the enterprise plan.

Pricing

Per seat: a free tier, a Pro plan reportedly around $30/user/mo, a Business tier around $90/user/mo that unlocks full CRM autofill and cross-deal intelligence, and custom enterprise.

Best for / not for

Best for AEs and revenue teams drowning in post-call admin. Less compelling for teams that need a full outbound dialer or that want a single low tier with every feature.

Alternatives

Gong is the enterprise conversation-intelligence incumbent; Fireflies records and summarizes meetings more broadly; Nooks pairs dialing with coaching.

What people are saying

We aggregate real LinkedIn discussion into sentiment for the agents people search most. Sybill isn't tracked yet, want it added? Request tracking.

FAQ

Does Sybill update my CRM automatically?+

Yes. It can auto-fill CRM fields including MEDDPICC and BANT from call and deal data, though humans oversee and correct, so in practice it operates as a supervised agent for CRM updates.

How much does Sybill cost?+

It has a free tier and per-seat paid plans, with the Pro plan reportedly starting around $30/user/mo and a higher Business tier; enterprise is custom.

Sources

Last reviewed 2026-06-19

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