Erleah vs Swapcard Comparison

Quick comparison

Capability	Erleah	Swapcard
Core identity	AI-first intelligence layer — full lifecycle	Event management platform with AI recommendations
Recommendation AI	AI-driven — behavioural, contextual, cross-channel	GISMO engine — graph neural networks, custom field weighting, genuine ML
Conversational AI for attendees	Yes — real NLP across 5+ channels	No. Zero conversational capability.
Support automation	40-70% of queries handled automatically	None
Channels	WhatsApp, SMS, email, web, event apps, pop-ups	In-platform only
Lifecycle coverage	Before, during, and after. 365-day engagement.	During-event focus. Post-event is data export and basic follow-up.
Memory	Layered — behavioural, contextual, persistent across events	Isolated per event. User data doesn't leak between events (privacy focus), but no cross-event intelligence.
Revenue generation	New revenue streams through AI-powered monetisation	Positions as "revenue-first" but through sponsorship and exhibitor management, not AI-generated revenue
Works with existing platforms	Yes — platform agnostic	Swapcard ecosystem
Pricing	Managed service, custom	Opaque — annual subscription, 90-day cancellation notice

What Swapcard does well

Swapcard's GISMO engine is real AI. Graph neural networks, custom field weighting (interest fields at 5.0 weight vs biography at 0.3), continuous improvement from user behaviour, privacy-preserving data isolation. Their results at scale are documented — 8,500 connections, 20,000 messages, and 11,000+ meetings in three days at ChangeNOW 2024. They've also been doing this since 2015, which means real refinement over time.

Where the gap is

Swapcard built a smart recommendation engine. Then wrapped a traditional event management platform around it. The AI recommends. It doesn't converse. It doesn't support. It doesn't personalise communications. It doesn't automate the 5,900 support conversations that Erleah handled at Rose City Comic Con.

Swapcard's GISMO is an algorithm. Erleah is an agent. The difference: an algorithm processes data and returns a result. An agent understands context, plans actions, communicates across channels, remembers previous interactions, and gets smarter over time — not just at recommending, but at supporting, engaging, and generating revenue.

Swapcard's privacy-first approach of isolating data per event is good for data protection but means zero cross-event intelligence. You run 20 events on Swapcard — each one starts from scratch. Erleah's compounding data architecture means event number 20 is dramatically smarter than event number 1 because it's learned from every interaction across your portfolio.

Frequently asked questions

Is Swapcard's AI more mature than Erleah's?

Swapcard's GISMO recommendation engine has been running since 2015. For recommendations specifically, it's well-refined. Erleah is newer but architecturally broader — conversational AI, support automation, cross-event intelligence, and omnichannel delivery are capabilities Swapcard doesn't have regardless of maturity.

Swapcard positions as 'revenue-first' — isn't that the same as Erleah?

Swapcard's revenue model is through sponsorship packages and exhibitor management. Erleah generates new revenue directly through AI — €100K+ at Event Tech Live from matchmaking and hosted buyer facilitation. Different mechanisms entirely.

Can Erleah work with Swapcard?

Yes. Erleah is platform agnostic. It adds the intelligence capabilities Swapcard doesn't have — conversational support, lifecycle engagement, cross-event learning.

Erleah vs Swapcard: Intelligence Layer vs Recommendation Engine