Sybill

Sybill connects to four CRMs (Salesforce, HubSpot, Zoho and Dynamics), Slack for delivery, email and calendar, conferencing across Zoom, Google Meet and Teams, dialers, and import of recordings from other tools including Gong. It acts in those systems as well as reading them, writing field-level updates and deal records into the CRM, pushing summaries into Slack channels and drafting into the rep's own mailbox.

Sourcesybill.ai integrations and CRM autofill pagesread 2026-09-01

Sybill runs a documented post-call chain end to end: join and record the meeting, transcribe, structure the summary against the chosen framework, write field-level updates into the CRM, push the summary into Slack, draft a follow-up email in the rep's voice and attach relevant collateral, then brief the rep before the next call. The control flow is not documented; no branching or conditional paths, no retry or error handling and no routing surface the customer can inspect or configure is described, so what is shown is a fixed pipeline rather than a runtime carrying decisions.

Sourcesybill.ai post-call automation and pre-call briefing descriptionsread 2026-09-01

Ask Sybill answers natural language questions grounded across the full corpus of meetings, dialer calls, emails, CRM, and Slack, and the context graph grounds every recommendation in how deals actually evolved, a comprehensive grounding capability that is a headline of the product.

Sourcesybill.ai/pricingread 2026-10-01

The product is a copilot by construction: follow-up emails are drafted in the rep's own voice for them to review, edit and send, CRM updates are proposed for confirmation, and the rep remains the one who acts on flagged risks. That is review by design rather than a configurable gate; nothing lets a buyer mark particular actions as requiring sign-off, name who signs off, or hold an action pending approval, and no mode switch between attended and unattended operation is documented.

Sourcesybill.ai follow-up email and CRM autofill descriptionsread 2026-09-01

On compliance and access control, the home page states SOC 2, GDPR, ISO 27001 and PCI and links a trust center at trust.sybill.ai, and the same section says a team controls what Sybill can access, what gets shared and who can see it; Google sign in is supported, and single sign on is listed on the Enterprise plan. Neither a named role model nor an audit log is documented (sybill.ai home and pricing pages).

Sourcesybill.airead 2026-10-01

Sybill gives deep visibility into deals and conversations: deal inspection flags at-risk opportunities and predicts outcomes, behavioral analysis maps each participant's engagement across a call, slides and demo screens are ranked by measured buyer resonance, and weekly reports and highlight reels summarize the motion. All of that observes the buyer and the rep rather than the agent; nothing traces what the assistant did on a given call, which sources it drew on, or which systems it wrote to and in what order, and no execution log or audit surface is documented.

Sourcesybill.ai deal inspection, behavioral analysis and reporting pagesread 2026-09-01

The context graph, which Sybill calls shared memory, is the corpus Ask Sybill queries across meetings, calls, emails, CRM and Slack to answer questions and ground recommendations, which makes it a knowledge store rather than agent memory. No state an agent carries between runs apart from that graph is documented, and no scope or lifetime for agent memory is stated as distinct from data retention.

Sourcesybill.ai context graph and Ask Sybill descriptionsread 2026-09-01

Delivered only as a cloud SaaS platform with no self host, on premises, or data residency deployment option documented.

Sourcesybill.airead 2026-09-01

Ready-to-use assets ship with the product: summary templates structured against MEDDPICC, BANT and SPICED, out-of-the-box prompts for deal strategy, multi-threading and coaching, and agents connected to the shared context. No library is browsable; the frameworks are formats a summary is cast into rather than a catalog a buyer selects from, the prompts are starting points inside the assistant rather than published assets, and no gallery of named agents or adoptable templates is documented.

Sourcesybill.ai summary framework and prompt descriptionsread 2026-09-01

Work starts on events rather than on a person typing: a scheduled meeting starting triggers joining, recording and transcription, the call ending triggers the summary, CRM autofill and follow-up draft, an automated pre-call briefing is sent ahead of the next meeting drawing on prior conversations and similar closed deals, and deal inspection fires risk flags when a next step is missing or an economic buyer is absent. The agent reaches people where they already work rather than in a console of its own, pushing summaries into Slack, writing field-level updates into Salesforce, HubSpot, Zoho or Dynamics, drafting into the rep's email, and operating across Zoom, Meet and dialers.

Sourcesybill.ai pre-call briefing, CRM autofill and Slack delivery descriptionsread 2026-09-01

Different tasks run on different models: the platform combines large language models with entity detection and other natural language models, and behavioral analysis over video adds its own models. That routing is internal to the vendor, with no customer selection, no per-task choice exposed, no bring-your-own-key and no routing policy the buyer configures. The model mix is described in press reporting rather than on a vendor page, and the providers used are not named.

Sourcesybill.ai product descriptions and press reporting on the model stackread 2026-09-01

REST API documentation for Sybill's own platform is published at api.sybill.ai with an OpenAPI specification covering conversations, deals, accounts, messages, rows, documents, sources and object types, with API keys and rate limits, and an MCP server at mcp.sybill.ai/mcp offers OAuth sign in and eight read tools (Ask Sybill, conversations, deals, accounts). The MCP server is read only, and API and MCP access ship on the Business plan (api.sybill.ai docs and sybill.ai/pricing).

Sourceapi.sybill.ai/docs/mcp.htmlread 2026-10-01

Sybill measures results but offers no testing. Slides and demo screens are ranked by measured buyer resonance, winning behaviors are surfaced for coaching, and deal patterns are analyzed across closed opportunities, all of which read results after the fact. Nothing lets a customer evaluate a change before it reaches anyone; no preview or dry run, no scored comparison between versions of a summary, prompt or agent configuration, no holdout and no quality gate in a release path is documented.

Sourcesybill.ai slide resonance and coaching descriptionsread 2026-09-01

Nothing in the product operates browsers or other software interfaces on the user's behalf. The assistant joins meetings to capture audio, video and screen shares, which is ingestion through conferencing integrations rather than operating an interface, and everything else runs over connected CRM, email, calendar and Slack integrations, all programmatic routes. No hosted or local browser, desktop session, remote computer control, web scraping or third-party browser engine is documented.

Sourcesybill.ai meeting capture and integrations descriptionsread 2026-09-01