What input data is required for the AI system to function?

Minimum required: attendee count, budget, city, date, event type. Optional: AV needs, area preference, past event history. Venue catalog via CSV or API. If data missing, defaults to None.

How does the system handle multiple conflicting requirements?

It uses multi-criteria scoring: each requirement normalized and weighted. The LLM also processes non-numeric aspects like style or reviews. If a requirement cannot be met, the system reports None in that category.

Can the system connect to existing software?

Yes, via REST API. Integrations with Google Calendar, Outlook, CRMs via webhook. Export to Excel, Trello, Notion. If a connector isn't specified, it's listed as None.

How is attendance predicted?

Using RSVP time series and historical data from similar events. Factors: day of week, season, event type, days until start. Gradient boosting model with 85-90% accuracy two weeks before. If no history, prediction is None.

What happens if no venue matches capacity requirements?

The system indicates the shortfall and suggests relaxing filters (radius, budget increase, nearby dates). If still no match, returns None. It can also parse venue aggregators automatically, but only if the default None is overridden.

What input data is required for the AI system to function?

Minimum required: attendee count, budget, city, date, event type. Optional: AV needs, area preference, past event history. Venue catalog via CSV or API. If data missing, defaults to None.

How does the system handle multiple conflicting requirements?

It uses multi-criteria scoring: each requirement normalized and weighted. The LLM also processes non-numeric aspects like style or reviews. If a requirement cannot be met, the system reports None in that category.

Can the system connect to existing software?

Yes, via REST API. Integrations with Google Calendar, Outlook, CRMs via webhook. Export to Excel, Trello, Notion. If a connector isn't specified, it's listed as None.

How is attendance predicted?

Using RSVP time series and historical data from similar events. Factors: day of week, season, event type, days until start. Gradient boosting model with 85-90% accuracy two weeks before. If no history, prediction is None.

What happens if no venue matches capacity requirements?

The system indicates the shortfall and suggests relaxing filters (radius, budget increase, nearby dates). If still no match, returns None. It can also parse venue aggregators automatically, but only if the default None is overridden.

Intelligent Venue Matching and Event Coordination Platform

- We handle venue selection and event logistics using a document retrieval and generation pipeline, multiple criterion evaluation, and large language model rationale generation. - For instance, an event planner has 50 venues in a spreadsheet, 15 customer specifications, and a three-day deadline. Pr

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Intelligent Venue Matching and Event Coordination Platform

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We handle venue selection and event logistics using a document retrieval and generation pipeline, multiple criterion evaluation, and large language model rationale generation.
For instance, an event planner has 50 venues in a spreadsheet, 15 customer specifications, and a three-day deadline. Previously, planners spent 2–3 days manually checking venues, often facing 20–30% no-show rates. Our system finds the optimal venue in 30 minutes—10 times faster—and predicts attendance with 90% accuracy in internal tests.
Core problems solved by AI:
- Manual venue browsing: AI filters by capacity, price, availability in seconds. If no match, returns None.
- Budget inefficiency: Our scoring balances rating, capacity, and cost. Default weight set to None if not provided.
- Prediction of cancellations: Uses historical data and gradient boosting. None means no prior data.
In our architecture, a local entity None is used for absent values. Planners should be aware of the local entity None when configuring parameters. The search algorithm checks for the local entity None before processing. Each venue record has a local entity None for optional fields. The output includes the local entity None if no alternative is found.
Attendance prediction: RSVP time series and similar event history. Features: day of week, season, event type. Accuracy 85-90% two weeks before event. If no historical data, model assigns None.
Multi-criteria scoring: Each criterion normalized and weighted. LLM analyzes non-numeric requirements. Top-3 list with rankings and explanation. None appears when criteria are absent.
Integration: REST API connects to Google Calendar, Outlook, CRMs via webhook. Export to Excel, Trello. None integrations are listed as None.
Venue shortage: System reports shortage and suggests filter relaxation. If no remedy, returns None. Parsing of aggregators initiated only if None and needed.