Meeting Bots: Bring Product Intelligence Into the Call
TL;DR: We shipped Meeting Bots—TestChimp agents that join your Meet / Zoom / Teams / Webex calls with the complete product context and QA posture already built about your SDLC. One cloud join captures tribal knowledge (organized transcripts you retain and feed into workflows), teammates @testchimp / @BotName for product-aware answers—not a blank LLM—and you can assign work live, then hand the transcript to ChimpHands to turn conversations into QA workflows (stories, scenarios, tests). Use cases go beyond QA—pre-sales, support, FDE—via custom bots with bespoke personalities. Docs: Meeting Bots.
The note-taker gap
Meeting tools got good at recording. They did not get good at knowing your product—or at keeping what the team said from evaporating after the call.
| What the team needs | What a generic meeting bot does |
|---|---|
| “Codify what we just decided so it isn’t tribal forever” | Leaves a recording someone may never re-open |
| “Do we already cover this scenario?” | Summarizes the last ten minutes of talk |
| “File the flaky auth bug we just reproduced” | Dumps a bullet list into Slack |
| “Turn this agreement into stories” | Leaves a transcript for someone else to rewrite |
| “Help on a pre-sales / support / FDE call with product truth” | One personality, no product graph |
If you already run TestChimp, the expensive part is done: plans as code, coverage, issues, releases, knowledge bases, observability, API schemas, QA Brain. The meeting was the last place that context stayed outside the room—and the last place team knowledge got said once and lost.
Meeting Bots close that gap.
What Meeting Bots are
A Meeting Bot is not a second product. It is a way for TestChimp Agent to sit in the call with the same control plane your agents and humans already use.
Core loop:
- Capture tribal knowledge — detailed notes organized and retained in cloud; optional PII redaction + summarize; knowledge can be fed into later workflows
- Ask in-call —
@testchimpor@QABot(or just ask for help) answers with product / SDLC context - Assign — capture tasks and issues from the discussion, not from post-hoc archaeology
- Follow up — after the call, turn the conversation into workflows (author the stories and scenarios you just agreed, scope tests, file issues)
Concepts (full walkthrough in docs):
| Concept | Role |
|---|---|
| Bots | Personalities (job role + policy)—QABot, SalesBot, or custom |
| Meetings | Joined calls + transcripts (your retained tribal knowledge) |
| Calendars | Google / Outlook so upcoming joins and @mentions work cleanly |
Capture tribal knowledge
Alignment that lives only in the room dies when the call ends. Meeting Bots treat the conversation as first-class product memory: transcripts are organized, retained, and available for redact / summarize and for agent handoff—so what your team shares can be captured, codified, and reused, not trapped in chat scrollback or someone’s private notes.
That is the foundation for everything else: live answers, task assignment, and post-meeting workflows all consume the same durable meeting context.
One join, many personalities
Here is the part people get wrong about “AI in meetings”: they assume every personality means another recorder and another bill.
In TestChimp, one visible agent joins and one transcript is produced for the meeting. You can still attach multiple custom bots—FDE assist, PM critique, pre-sales, support—and @mention the right lens. Transcription is per meeting, not per bot. Adding three personalities does not triple the join meter.
Policy files live where the rest of your agent governance lives:
plans/knowledge/policies/meetbots/<BotName>.policy.md
Version them. Review them. Treat brainstorming agents like any other playbook—not a one-off system prompt someone typed into a vendor dashboard.
Billing stays boring on purpose: $1 / hour of join time, plus ChimpHands credits when you actually invoke the LLM (@testchimp / @BotName, or transcript PII / summarize processing). Passive transcription does not burn credits every spoken second. Details: Billing.
Beyond QA
The use cases go well beyond QA.
TestChimp already has deep product-context awareness across user stories, scenarios, tests, executions, releases, KBs, issues, observability metrics, API schemas, and more. Meeting Bots put an assistant with that context into the conversation—ready when needed.
Imagine that assistant in:
- Pre-sales calls — product truth without improvising capabilities you do not have
- Customer support conversations — grounded in real issues, releases, and scenarios
- FDE interactions — implementation help against plans and APIs, not vibes
Define custom bots with bespoke personalities and objectives, then bring the right assistant(s) into each meeting. Docs: Custom bots.
Post-meeting is where the loop compounds
Live answers are useful. Governed follow-through is the unlock—especially for QA workflows built from what the team agreed.
From a meeting detail page you hand the transcript to ChimpHands with a draft like:
/testchimp referring the meeting <id> as context, do the following :
author user stories for the behaviour we agreed, with scenarios
Same catalog workflows you already run for author-plans, fix-issue, upkeep—except the meeting is first-class context instead of a forgotten Google Doc.
That is the difference between “we recorded the alignment” and “the alignment became stories, scenarios, and tests in the repo.”
Why this belongs in TestChimp
We have argued that skills are SaaS distribution, that automations close the routing gap, and that Studio collapses the stitching tax on the desktop.
Meetings were still a parallel universe: rich talk, thin product memory, tribal knowledge that never got written down.
Meeting Bots put product intelligence in the call—so the hour you already spend aligning becomes fuel for delivery, support, and sales loops, not another artifact to reconcile later.
Your meetings—now part of your product intelligence.
Get started
- Open Meeting Bots in the app (capability on eligible plans).
- Connect a calendar.
- Join with QABot (or create a custom personality for sales, support, or FDE).
@testchimpa product question mid-call.- Afterward, open the meeting → ChimpHands → turn the agreement into work.
Full product docs: Meeting Bots · Custom bots · Meetings & PII · Billing.
Frequently asked questions
Do I need a calendar?
Strongly recommended. Calendar-linked joins improve upcoming-event pickers and team @mention guards when participant emails are available. You can still paste a Meet / Zoom / Teams / Webex URL for transcription-only joins.
Who can @mention the bot?
Org members only (email preferred). Externals get a denial—your product brain stays inside the team.
Does every spoken minute cost ChimpHands credits?
No. Join time is the $1/hour meter. Credits apply when you @mention for an LLM answer (or when LLM-backed redact / summarize runs).
Will adding more bots double my bill?
No for transcription / join time. Multiple personalities share one meeting session and one transcript.