LiveLoop · AI Meeting Assistant
Your class ends.
Your notes are already done.
Sixty seconds after the session ends, the summary and action items are in your inbox. Topics discussed. Who agreed to what. What happens next.
No third-party note-taking bot joins your call. LiveLoop is the platform — the AI is built in.
The LiveLoop AI meeting assistant is a native, built-in feature of the LiveLoop video conferencing platform from Databus, Chennai. It runs automatic speech recognition (ASR) during the session to produce a labelled transcript, then extractive summarisation after the session to produce topics, decisions, and an action-item list — delivered in approximately one minute to the host's inbox and the connected ERP. No third-party bot joins the call.
Topics discussed
Balancing chemical equations · Coefficients vs subscripts · The PhET simulation walk-through
Key decisions
Worksheet will be released by Wednesday EOD · Two more breakout sessions next week
Action items
- Mrs. Pillai (teacher) — release worksheet to Class 9-B by Wed
- Class 9-B students — complete first 5 equations before Thursday
- Mrs. Pillai — schedule remedial breakout for 4 students
Real layout from a Tuesday Chemistry session. Names changed.
Sixty seconds, end to end
From "session ended" to "summary in your inbox"
The same Class 9-B Chemistry session from the LiveLoop hero — picked up at the moment the teacher clicks End session.
Teacher clicks End session. The ASR layer has been running through the full 48-minute class — transcript with speaker clustering is already 99% built.
Final 11 seconds of audio process. Transcript ready. Speaker labels assigned: Mrs. Pillai (host), 22 students in lab (grouped), 6 students remote (named individually from their LiveLoop login).
Extractive summarisation begins. Topics discussed are surfaced by keyword density across the transcript ("equations", "balance", "coefficients"). Key decisions are extracted from declarative host statements ("worksheet will be released by Wednesday").
Action-item extraction runs against commitment phrase patterns: "I will release", "by Wednesday", "complete the first five". Each item is attributed to the speaker who said it.
Draft summary lands in Mrs. Pillai's email and inside SchoolDeck attached to the Period 2 Class 9-B Chemistry timetable slot. She has not had to do anything yet.
Mrs. Pillai opens the draft on her phone. She removes one action item that was a joke ("students to bring chocolate"), edits one student's name spelling, and clicks Send to all.
All 28 students (22 in lab + 6 remote) and the academic head receive the final summary in their parent app + inbox. The student who lost video at 10:24 AM now has the worksheet deadline she missed.
What this is: automatic speech recognition + extractive summarisation + rule-based phrase matching for action items. Not a generative AI inferring "what people really meant". The transcript is the canonical record; the summary is a digest of it; the teacher edits before sending.
Inside the AI assistant
What's actually in the pipeline
Honest, mechanism-first. The three things the assistant produces, and one thing it doesn't.
During the session
Automatic speech recognition runs in real time. Speaker clustering separates voices.
Real-time speech recognition (ASR)
The session audio is transcribed live. Words appear as they're spoken. Industry-standard ASR; not magic.
- Languages: English, Hindi, Tamil, Telugu, Bengali, Marathi, Kannada, plus 24 more
- Latency: sub-second on a stable connection
- Failure mode: on weak audio, the system marks uncertainty rather than guessing
Speaker clustering & labelling
Voices are grouped by acoustic similarity. Each cluster gets a label — host, panelist, named participant if they joined logged in.
- Mechanism: voice-embedding clustering — not name recognition
- For named guests: uses the name they entered when joining
- Honest: overlapping speech is harder; the transcript marks it
After the session
Extractive summarisation on the transcript. Pattern-based action-item detection.
Extractive summary — topics & decisions
Topics surface from keyword density. Decisions surface from declarative statements by the host. The output is structured into three sections.
- Format: Topics discussed · Key decisions · Action items
- Length: a 60-minute class becomes a 2-minute read
- Delivery: email + connected ERP within ~60 seconds of session end
Action-item extraction & attribution
Phrase-pattern matching finds verbal commitments. Speaker clustering tells you who made each commitment.
- Patterns matched: "I will…", "let's schedule…", "by [day]…", "you should…"
- Attribution: linked to the speaker who said it (best-effort)
- Quality gate: host previews + edits before sending
What this assistant is not
Mechanism-first means saying the things it doesn't do, too.
Not a generative chatbot inside the call
No "Hey LiveLoop, what's the agenda?" interruption. The assistant works on what was said — by humans, in the actual session.
- Why: we want the transcript to be the canonical record, not a hallucinated narrative
- Privacy: the session is not piped to a public LLM
- For coaching & admissions: a third-party bot would unsettle candidates and parents
Not sentiment scoring or attention tracking
We don't analyse "how engaged" each student was, score emotional tone, or rank participants by behaviour. Attendance & engagement insights are a separate feature, and they measure observable things — join time, leave time, who spoke, who voted.
- No: eye-tracking, attention scores, emotional analysis
- Yes: objective participation data — on the insights page, not here
- Why: POCSO + DPDP boundaries on minor-related behavioural inference
Native AI vs bolt-on bots
LiveLoop AI vs Otter, Fireflies, Fathom, Read
Those are good tools. They're built on top of a meeting platform — a bot in the call. LiveLoop's AI is the platform.
| What you'll notice | LiveLoop AI (native) | Bolt-on bots (Otter / Fireflies / Fathom) |
|---|---|---|
| Who's in the participant list? | Just humans. | A bot avatar appears as a participant. |
| Need to authorise an extra account? | No. It's part of LiveLoop. | Yes — admin must approve a third-party app. |
| Where does the audio leave from? | Stays within LiveLoop's pipeline. | Streams to the bot vendor's cloud. |
| Trust posture for school PTMs | Parents see no extra "observer". | Parents see "Otter Bot" in the call and ask why. |
| Used to train public LLMs? | No. | Some do, depending on plan tier. |
| Time-to-summary | ~60 seconds after session ends. | 2–10 minutes typical. |
| Lands inside your school ERP? | Yes — SchoolDeck / CampusAlly / TutorDesk. | No. Sends to email or vendor dashboard. |
| Pricing model | Included in LiveLoop per-host licence. | Separate per-user subscription on top. |
Concrete use cases
Five real situations where the assistant earns its keep
🏫 School PTM digest for parents
15-minute parent meeting at 6 PM. By 6:16 the parent has the topics discussed and the homework follow-up in their inbox. They didn't have to take notes; the teacher didn't either.
🎓 College admissions interview record
Candidate is interviewed at 11 AM. By 11:16 the admissions committee has a structured note of every candidate's answers — without a bot sitting in the call making the candidate uncomfortable.
📚 Coaching demo class follow-up
Prospect parent joins a 30-min demo. Class ends, the parent gets a summary of what was taught plus next-step enrolment details — converted, before the lead cools.
💼 Team standup & training minutes
Daily L&D session. Manager doesn't have to write up training notes for HR records — the summary lands in the shared channel automatically.
🏛️ Trust / SMC meeting minutes
School management committee sits on Saturday. Minutes are required for the next AGM. The summary becomes the first draft — the secretary edits, signs, and files. From half a day to an hour.
📊 Audit-friendly session record
For institutions where governance asks "what was discussed in that session?", the transcript + summary stands as a clean, dated, attributed record. Not just a video file.
What this page owns, what it doesn't
AI Assistant ≠ Transcription ≠ Translation ≠ Insights
Four LiveLoop features, four owned jobs. If you came here looking for a sibling capability, here's the right page.
This page — /features/ai-assistant/
Owns the post-session summary + action item extraction story. ASR + extractive summarisation pipeline.
/features/transcription/
Owns the searchable transcript archive. The transcript is the raw material the AI assistant digests; the transcription page is where you go for transcript search, timestamped playback, and archive retention.
/features/translation/
Owns the multilingual live captions story — 30+ languages, real-time overlay during the session. AI Assistant produces the summary in the source language; translation is a separate participant-facing feature.
/features/insights/
Owns the attendance & engagement data story. Who joined when, who spoke, who dropped off — observable behaviours, not AI inference. Distinct from the AI summary.
/features/recording/
Owns the cloud recording & absentee auto-share story. The recording is the video; the AI summary is the text digest of it. Both are produced from the same session.
/features/ — features hub
The full 16-feature index. Use this when you want to scan everything LiveLoop has before deciding which deep page is yours.
Frequently asked questions
Ten questions about LiveLoop's AI assistant
What does LiveLoop's AI meeting assistant actually do?
Two things, in sequence. During the session, automatic speech recognition (ASR) transcribes the audio in real time with speaker clustering. After the session ends, extractive summarisation produces a short digest of topics discussed, key decisions named in the conversation, and an action-item list. The summary and the searchable transcript land in the host's inbox and in the connected ERP within about a minute of the session ending. It is a transcription plus extraction pipeline — not a generative chatbot inside the call.
Do I need to invite a bot like Otter, Fireflies, or Fathom to the call?
No. LiveLoop is the video platform, so the AI assistant is native — there is no third-party note-taking bot sitting in the participant list. Bots-in-the-call have two problems. First, they unsettle participants (especially parents joining a PTM or candidates in an admissions interview) — a stranger is in the room. Second, they only see what the bot can record, which means privacy controls have to negotiate around a third party. LiveLoop's assistant is part of the platform itself.
How does the AI extract action items from the conversation?
Through rule-based phrase pattern matching applied to the transcript. The system looks for verbal commitments: "I will send", "let's schedule", "I'll handle", "I'll get back to you on this", "we should do X by Friday". Each detected commitment is attributed to the speaker who said it (using the same speaker clustering that labelled the transcript). This is honest pattern extraction — not an LLM trying to infer "who really meant what". The host previews the action-item list before it is sent.
Is my voice data used to train your AI models?
No. LiveLoop's ASR and summarisation pipeline runs in a private processing environment. Your session audio and transcripts are not used to train public AI models and are not shared with third parties. Recordings and transcripts are stored under your organisation's account and are deletable on request, per the LiveLoop data retention policy.
Can I edit the AI summary before it is sent to participants?
Yes. The host receives a preview of the summary, decisions, and action items immediately after the session ends. The host can edit any section, remove items, add items the AI missed, change assignees, and only then send the finished summary to participants. This matters for school PTM summaries that go to parents — the teacher should be the editor of record, not an algorithm.
Is the transcript searchable separately from the summary?
Yes — the searchable transcript is owned by a separate feature page. The AI summary is a digest; the transcript is the canonical record. Both are produced by the same ASR run during the session. For transcript search, timestamps that jump to the moment a topic was discussed, and how to use the archive for revision, see the LiveLoop transcription feature page.
Does the AI assistant work for languages other than English?
Yes. The ASR layer supports multiple languages including English, Hindi, Tamil, Telugu, Bengali, Marathi, and Kannada. Live caption translation across 30+ languages — the participant-facing multilingual experience — is owned by a separate feature page. The AI summary is produced in the source language of the session by default; on-request translation of the summary is available.
Where does the summary land — email, ERP, both?
By default, the host's email. If your institution is on SchoolDeck (K-12 ERP), CampusAlly (college ERP), or TutorDesk (coaching), the summary also lands inside the ERP attached to the relevant timetable period or batch. If you also use Slack, Microsoft Teams, or a CRM (HubSpot, Salesforce, Zoho), the summary can post to a channel or log against a contact record — these integrations are configurable.
Will the AI mistake casual conversation for an action item?
Sometimes — and that is why the host preview exists. Pattern matching catches verbal commitments but can occasionally over-detect. The host editing step before sending is the quality gate. Over time the extraction is tuned to your usage patterns through pattern-list updates; the system does not silently learn from your private transcripts.
What does the AI assistant cost extra?
Nothing on top of the per-host LiveLoop licence. The AI assistant is included in every plan, starting at ₹499 per host per month. Higher tiers add capacity — longer transcripts, larger session caps, longer recording retention — not access to the assistant itself. See the pricing page for the full tier breakdown.
Related LiveLoop features & pages
Sibling AI features
Core LiveLoop pages
Solutions where AI helps most
Where summaries land
LiveLoop AI · Built in, not bolted on
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