Case study · Events and networking
Dream - Multi Tenant SaaS Platform.
An MVP SaaS web application designed to help event organizers create meaningful, high-quality connections between attendees.
- What we built
- Multi-tenant SaaS web app with AI-assisted matching
- Stack
- Next.js, Supabase (PostgreSQL, Magic Link Auth, Row Level Security)
- Services
- Web appSaaS platformAI matchmakingPayments integration
About the project
What is Dream - Multi Tenant SaaS Platform?
This multi-tenant event matching platform is an MVP SaaS web application designed to help event organizers create meaningful, high-quality connections between attendees. Instead of random networking, the platform structures interactions around what attendees want (“Dreams”) and what they can offer (“Gives”), enabling intentional, value-driven matches. The system supports three roles — Attendees, Organizers, and Super Admin — each with tailored dashboards and permissions. With AI-assisted matching, a clean onboarding flow, and a minimal attendee interface, the platform transforms traditional networking into curated, purposeful connections.
The problem
What was slowing them down.
- 01
Low-quality networking
Modern events struggle with one key issue: low-quality networking.
- 02
Attendees don’t know who to connect with
Attendees often don’t know who to connect with.
- 03
Attendees feel overwhelmed
Attendees often feel overwhelmed in large groups.
- 04
Attendees miss opportunities
Attendees often miss opportunities aligned with their goals.
- 05
Manual matchmaking
Organizers face manual and time-consuming matchmaking.
- 06
Lack of tools
Organizers lack tools to facilitate meaningful connections.
- 07
No structured system
Organizers have no structured system to track engagement or outcomes.
- 08
Events lose potential value
As a result, events lose potential value, and attendee satisfaction drops.
The solution
What we built.
Structured, AI-assisted matchmaking
We built a multi-tenant SaaS platform that enables structured, AI-assisted matchmaking.
- Guided Onboarding Flow: attendees submit up to 3 Dreams (needs) and 3 Gives (offers), supported by AI suggestions.
- AI-Assisted Matchmaking: organizers trigger AI to generate high-quality match suggestions based on attendee data, including context like occupation, tags, and categories.
- Manual Control Layer: all matches are reviewed and approved by organizers, ensuring quality and relevance.
- Clean Attendee Dashboard: users receive matches in a simple card-based interface with contact details and AI-generated outreach templates.
- Multi-Tenant Architecture: each organizer operates in a secure, isolated environment with full control over their events.
- Event Monetization: Stripe integration allows organizers to pay before publishing events, enabling scalable SaaS revenue.
Results
What changed.
For organizers
Reduced manual workload
Reduced manual workload through AI-assisted matching.
Higher attendee satisfaction
Higher attendee satisfaction due to relevant connections.
Scalable event management
Scalable event management via multi-tenant architecture.
Monetization-ready system
Monetization-ready system with built-in payment gating.
For attendees
More meaningful connections
More meaningful connections aligned with their goals.
Simplified networking
Simplified networking experience.
Clear next steps
Clear next steps with AI-generated outreach messages.
Platform impact
Increased engagement
Increased engagement and interaction rates.
Higher perceived value
Higher perceived value of events.
Strong foundation
Strong foundation for a scalable SaaS product.
Key challenges
The hard parts, and how we solved them.
Ensuring high-quality matches: AI can generate irrelevant or low-quality suggestions.
Introduced a human-in-the-loop system where organizers review and approve every match.
Multi-tenant data isolation: preventing data leakage across organizers.
Implemented Row Level Security (RLS) at the database level to enforce strict tenant isolation.
Avoiding duplicate matches: same users being matched multiple times.
Applied database-level constraints and suggestion filtering to ensure unique pairings.
Simplifying user experience: reducing friction in onboarding and dashboard usage.
Designed a 4-step onboarding flow and a minimal, card-based dashboard UI.
Balancing automation with control: fully automated systems can reduce trust.
Made AI button-triggered and review-based, giving full control to organizers.
Email & notification reliability: preventing duplicate or spam notifications.
Built an idempotent email system with logging and rate-limiting.
Tech stack
What it runs on.
- Frontend
- Next.js
- Database & authentication
- Supabase (PostgreSQL, Magic Link Auth, Row Level Security)
- Payments
- Stripe (event activation & monetization)
- AI integration
- OpenAI API (suggestions, tagging, match recommendations)
- Hosting
- Vercel
- Version control
- GitHub
Process
How we got there.
01Organizer flow
Sign up and create an event · Complete payment to activate event · Manage attendees and view submissions · Trigger AI match suggestions · Approve/reject matches · Monitor analytics and activity
02Attendee flow
Authenticate via magic link · Complete onboarding: profile setup, submit Dreams, submit Gives, review & finalize · Access dashboard with matches · Reach out using provided contact details
03Matching flow
AI analyzes Dreams, Gives, tags, and context · Returns ranked match suggestions · Organizer approves matches · Users are notified and can accept/decline · Upon acceptance, contact details are revealed
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