AI Integration with Fonteva for Event Session Recommendations

The integration connects at the Attendee Registration and Event Session object level within Fonteva's Salesforce-native architecture. As a member registers for an event, an AI agent analyzes their profile data—such as job title, past session attendance, declared interests in their member record, and community forum activity—against the session catalog. This creates a real-time, personalized recommendation score for each available session, which is surfaced directly within the Fonteva event portal and mobile app. The system uses a RAG (Retrieval-Augmented Generation) layer on top of session descriptions, speaker bios, and past attendee feedback to ground its reasoning and provide contextual explanations for each suggestion.

From an implementation standpoint, the AI service typically runs as a serverless function or microservice, triggered by registration webhooks from Fonteva. It queries the platform's APIs for attendee and session data, processes it through the recommendation model, and writes the scored suggestions back to a custom object or a dedicated field on the registration record. This allows Fonteva's native workflows, reports, and automations to leverage the AI output. For example, you can build a Flow that sends a personalized "Your Top Sessions" email 48 hours before the event, or create a dashboard widget showing the most recommended sessions across all attendees to help planners adjust room capacities.

Rollout should be phased, starting with a pilot event where AI suggestions are presented as "AI-Powered Picks" alongside the standard agenda. This allows for A/B testing on engagement metrics like session add rates and post-event survey scores. Governance is critical: ensure the AI's logic is auditable, with logs tracking which data points influenced each recommendation. Implement a human-in-the-loop review for the first few events, allowing planners to override or adjust suggestions, which also serves as valuable training feedback for the model. This approach mitigates risk while demonstrating clear value—turning a generic schedule into a curated experience that makes each attendee feel the agenda was built just for them.

The integration connects at two key surfaces within the Fonteva platform: the Event Registration object and the Fonteva Events Mobile App. A background process, triggered upon registration or app login, ingests structured attendee profile data (job title, company, declared interests from custom fields) and unstructured data (past session attendance, community forum posts). This data is vectorized and stored in a dedicated session recommendation engine—a separate microservice that maintains a semantic index of all conference sessions, including descriptions, speaker bios, and tags.

For each attendee, the engine performs a real-time similarity search against the session index. The resulting recommendations are ranked by relevance and contextualized with a brief explanation (e.g., "Matches your interest in 'non-dues revenue'"). These are delivered via a secure API call back to Fonteva, where they populate a custom Lightning component on the attendee's event portal and are pushed as personalized agenda suggestions within the mobile app. The entire flow is logged to a custom Recommendation_Audit__c object in Salesforce for analysis and to respect data privacy preferences.

Rollout is typically phased, starting with a pilot group of attendees for a single large conference. Governance is critical: we implement a human-in-the-loop review step where association staff can review and adjust the AI's top recommendations before they go live, ensuring alignment with event goals. Post-event, the audit logs are analyzed to measure recommendation engagement (clicks, adds-to-schedule) and iteratively refine the underlying models for future events.

Implementation begins by mapping the Fonteva data model—specifically the Event Session, Attendee, Member Profile, and Registration objects—to a secure vector index. This ensures session recommendations are grounded in your actual event catalog and member data. All AI operations should be executed via a dedicated service layer that enforces Salesforce platform permissions and logs all recommendation generation events back to a custom AI_Interaction__c object for auditability and model feedback.

A phased rollout is critical for user adoption and risk management. Start with a low-risk pilot in a controlled environment, such as a small committee meeting or a pre-conference workshop. Use this phase to:

• Validate recommendation accuracy against a human-curated 'gold standard' list.
• Gather qualitative feedback on the relevance of suggested sessions and connections.
• Monitor system performance and API latency within the Fonteva event app framework.
• Refine prompts and ranking algorithms based on real user behavior.

For governance, establish clear human-in-the-loop checkpoints. For example, the AI can suggest a networking match, but the initial connection request should require attendee approval. Similarly, session recommendations should be presented as 'suggestions' with clear explanations (e.g., 'Because you attended last year's leadership track'). Implement a feedback loop where users can thumbs-up/down recommendations, feeding directly into a continuous evaluation dataset to detect and correct model drift over time.