Agenda assistant
"What should I see on Tuesday about battery storage?" answered from the programme and the attendee's interests, with one-tap add to schedule.
Event Technology
AI for events that attendees notice
An agenda assistant that knows the programme and a support bot grounded in your event's own knowledge. Sessions transcribed and summarised as they happen, and insights sponsors can act on. All built into the app.
- Grounded in event data only
- Claude, OpenAI, Gemini or open models
- Inside Eventify or your app
What we deliver
Five AI services for the event lifecycle
Support bot
Attendee questions answered from event knowledge, 24 hours a day, in the app and on the web. Built with our support copilot approach; see the Eventify support bot case study.
Session transcription and summaries
Live transcripts, key points and action items per session, published to the app and searchable afterwards. See the speech-to-text case study.
AI matchmaking
Ranked introductions and booked meetings from attendee and exhibitor profiles. Detail on the matchmaking page.
Sponsor and exhibitor insights
Which topics drew the audience, which stands had the right visitors and what buyers asked about, summarised per sponsor.
Organiser copilot
Draft push messages, answer "how many VIPs have checked in", and flag sessions filling up, from the admin console.
How the bot stays honest
Grounded in event knowledge, not the open internet
The assistant does not know anything until the event's data is indexed: agenda, speakers, exhibitors, map, FAQs, policies and the attendee's own registration. Every answer is retrieved from that index and cites where it came from.
Tools let it act, not just talk. It can look up a session, add it to a schedule, book a meeting slot or hand over to the organiser desk with context attached. When the agenda changes, the index updates within seconds, so the bot is never more out of date than the app.
Guardrails are explicit. It will not answer outside the event, will not reveal other attendees' data, and tells the attendee when it is unsure. We test this with the same prompt injection defences we apply to any customer-facing assistant.
A day in the life
Where AI shows up during one event day
- 07:30
"Where do I collect my badge?"
The support bot answers from the venue map and the attendee's ticket type: hall entrance B, kiosk row, open from 08:00.
- 08:15
Kiosk check-in
Badge printed in seconds. The organiser copilot reports VIP arrivals and a queue forming at entrance A; a push redirects latecomers.
- 09:00
Keynote transcribed live
Captions in the app for the room and the remote audience. Key points published within minutes of the applause.
- 10:30
Agenda assistant
"Anything on grid-scale storage this afternoon?" Two sessions and a stand suggested, one tap to add, clash with a booked meeting flagged.
- 12:00
Matchmaking meeting
A meeting booked yesterday from a ranked suggestion happens at table 14. Both parties rate it; tonight's suggestions improve.
- 14:00
Lead captured with a voice note
Exhibitor scans a badge, records 20 seconds, and the lead record gets a summary, a suggested score and a draft follow-up.
- 16:30
Session summaries pushed
Attendees receive summaries of the sessions they favourited but could not attend, with links to the recordings.
- 18:00
Sponsor insights
Each sponsor gets a digest: attendance at their session, questions asked, stand visitors by role and the topics that trended.
Decide
Where AI pays off at events, and what each needs
| Capability | Data it needs | Main risk | Guardrail |
|---|---|---|---|
| Support bot | Agenda, venue, FAQs, policies, ticket data | Confident wrong answers | Retrieval only, citations, hand-off to desk |
| Agenda assistant | Programme, interests, personal schedule | Bland suggestions | Interests from registration plus behaviour; explain why |
| Transcription and summaries | Clean audio feed, speaker names, glossary | Misheard jargon, consent | Glossary priming, review step, speaker consent |
| Matchmaking | Profiles with goals, exhibitor catalogue | Feeling watched | Opt-in, visibility controls, no sensitive attributes |
| Sponsor insights | Session scans, questions, stand visits | Over-claiming from thin data | Show sample sizes; aggregate, never individual |
| Organiser copilot | Admin data, check-in stream | Acting without review | Drafts only; a human sends pushes |
Process
From one use case to an AI layer
Pick one
Usually the support bot or transcription: measurable, low risk and visible to every attendee.
Connect the data
Event content indexed, tools wired to the platform API, privacy rules written down.
Evaluate
A test set of real attendee questions from past events, scored for accuracy and safe refusal before launch.
Launch and watch
Live at the event with a review queue; unanswered questions become next year's FAQ content.
Extend
Add the agenda assistant, insights and the organiser copilot on the same index and guardrails.
Typical timelines
Support bot on Eventify: two to four weeks including evaluation. Transcription and summaries: two to three weeks plus a rehearsal with the AV team. Integrating the AI layer into another event app: four to eight weeks depending on the data connections.
Related
Go deeper
AI for events guide
The long-form guide to use cases, costs and pitfalls.
ReadSpeech-to-text
How we build live transcription and summaries.
ExploreSupport copilot
The grounded-answer approach behind the event bot.
ExploreEvent analytics
Where the insights data comes from.
ExploreAll of it runs on the Eventify platform or inside your own app via the white-label platform.
FAQ
Frequently asked questions
What can an event support bot actually answer?
Anything in the event's own data: agenda, rooms, speakers, exhibitors, floor plan, badge collection, dietary options, policies and the attendee's own registration. It answers from that data only, shows where the answer came from and hands over to the organiser desk when it is unsure.
How accurate is live session transcription?
Good enough for searchable transcripts and summaries in most conference rooms with a proper microphone feed. Accuracy drops with crowd noise and heavy jargon. We feed speaker names and a glossary to the model and offer a quick review step before publishing. See speech-to-text.
Which AI models do you use?
Anthropic Claude, OpenAI and Google Gemini through their APIs, and open-weight models where data must stay in a specific region. The model is a component; the event data, retrieval and guardrails around it are what make the difference.
How do you keep attendee data private?
The bot can only see the attendee it is talking to and public event content. Transcripts are processed under a data agreement, speakers consent to recording, and personal data is never used to train models. Our data privacy page covers the controls.
Can we add AI to an existing event app?
Yes. The assistant, transcription and insights run as services with APIs, so they can sit inside Eventify, your own app or a web widget. Integration typically takes two to six weeks depending on how much event data needs connecting.