Reducing Post-Conference Connection Attrition Through Context-Aware Relationship Management
Context & Challenge
Business challenge
The conference industry is experiencing a post-pandemic renaissance, with events like Afrotech drawing 40,000+ attendees annually. However, the fundamental problem of maintaining meaningful connections post-conference remains unsolved. Existing tools (LinkedIn, business card apps) focus on collection rather than context preservation and relationship nurturing.
As someone entering my fourth year attending Afrotech, I recognized this as both a personal pain point and a market opportunity. The timing was strategic: testing during Afrotech 2024 would provide immediate validation with hundreds of potential users in a real-world, high-stakes environment.
The Problem
Root Issue: Conference networking tools solve connection capture but ignore the context that makes connections valuable.
Specific Pain Points:
Context loss: Forgetting who someone is, what you discussed, or why the connection matters
Weak follow-ups: Generic "nice to meet you" messages that don't reference shared conversations
Missed opportunities: Failing to reconnect with the right people at the right time because you've lost the "why" behind the connection
Cost of Inaction: Continued investment in conferences with diminishing returns on relationship building.
Initial Constraints & Considerations
Technical Constraints
4-day timeline (conference was immovable deadline)
Solo execution (no team to delegate to)
Must work offline (conference WiFi is notoriously unreliable)
No time for App Store approval process
Strategic Constraints
Unknown variables around what features would actually provide value in real-world use
Testing hypothesis of AI-assisted development at conference-deadline speed
Success Criteria
Unknown variables around what features would actually provide value in real-world use
Testing hypothesis of AI-assisted development at conference-deadline speed
Technical Goals
Functioning prototype deployed before Afrotech
Data successfully syncing to database
PWA installation working on both iOS and Android
Personal Context: After three years attending Afrotech, I identified a consistent pattern: I would meet valuable people, connect on LinkedIn, then completely forget the context of our conversation within days. This wasn't just forgetfulness—it was a systematic failure of existing tools to preserve what made connections meaningful.
My Role & Approach
Leadership & Sole Execution
As a solo founder on this project, I wore multiple hats but approached it with senior-level strategic thinking:
Product Strategy: Defined the problem space, competitive landscape, and MVP scope UX Design: Created design system, interaction patterns, and user flows Engineering: Built full-stack application using AI-assisted development QA & Testing: Conducted field testing in real conference environment
Strategic Decisions
Decision 1: AI-Assisted Development Workflow This project leveraged AI-augmented building at production scale. Rather than treating AI as a code completion tool, I used it as a strategic thought partner throughout the entire development process.
Why This Approach:
4-day timeline made traditional development impossible
Previous experience with AI tools demonstrated potential for velocity with proper planning
My strength is in product thinking and UX; AI could accelerate implementation
What I Decided NOT to Do:
Traditional design-first approach with high-fidelity mockups
Waterfall development (spec everything before building)
Building for scale instead of learning
Trade-offs I Navigated:
Speed vs. polish: Prioritized functioning over beautiful
Learning AI tools vs. using familiar stack: Invested time upfront in AI workflow for long-term velocity
Solo execution vs. team validation: Accepted I'd miss blind spots in exchange for speed
Product Ideation
Exploring Solutions
I explored the solution space through dialogue with Claude to determine feasibility and scope of the prototype. I treated it like a strategic planning session with a co-founder:
Strategic Decisions
Decision 1: AI-Assisted Development Workflow This project leveraged AI-augmented building at production scale. Rather than treating AI as a code completion tool, I used it as a strategic thought partner throughout the entire development process.
Why This Approach:
4-day timeline made traditional development impossible
Previous experience with AI tools demonstrated potential for velocity with proper planning
My strength is in product thinking and UX; AI could accelerate implementation
What I Decided NOT to Do:
Traditional design-first approach with high-fidelity mockups
Waterfall development (spec everything before building)
Building for scale instead of learning
Trade-offs I Navigated:
Speed vs. polish: Prioritized functioning over beautiful
Learning AI tools vs. using familiar stack: Invested time upfront in AI workflow for long-term velocity
Solo execution vs. team validation: Accepted I'd miss blind spots in exchange for speed
Figma Mocks
Rationale: Based on my pet project experiments, I learned that AI code assistants (like Cursor) are poor designers. They can implement a design system brilliantly, but can't make good aesthetic decisions independently. By creating a comprehensive design system upfront, I could maintain visual quality while leveraging AI for implementation speed.
Trade-offs: Spent half of Day 1 on a simple design rather than features. This felt uncomfortable given the tight timeline, but paid dividends in consistent, professional output throughout the build.
Implementation
Launch Strategy
Soft Launch Approach: Personal use during Afrotech with no public announcement. This was intentionally low-key to allow for learning without pressure to support multiple users.
Why This Approach: With limited testing and tight timeline, a broader launch would have introduced support burden and expectations I couldn't meet. Better to validate core hypothesis first, then scale.
Impact & Outcomes
Development Velocity
2.5 days to functioning prototype (40% under 4-day target)
Full-stack application: frontend, backend, database, authentication, offline sync
Zero bugs that prevented core use case
Qualitative Outcomes
Personal Validation: Successfully used the app throughout Afrotech to capture 16 connections with rich context. One week post-conference, I could recall specific conversation details for all the connections using the app.
Technical Learning: Validated AI-assisted development workflow that I've since applied to subsequent projects. The PRD-first approach with Claude became my standard process.
Product Validation: Proved the core hypothesis: context preservation matters, and speed of capture is critical. The app provided measurable value to at least one user (me), establishing a foundation for further development.
Business Value
Proof of Concept: Demonstrated feasibility of AI-accelerated solo development for 0-to-1 products. This has implications for future side projects and consulting work.
Market Validation: Identified an underserved niche in conference networking tools. Existing solutions focus on contact capture; opportunity exists for context preservation.
Personal ROI: Transformed my Afrotech 2025 experience. Connections that would have faded are now accessible with full context, leading to 3 follow-up meetings that wouldn't have happened otherwise.
What's Next
I've decided to move forward with building the app. Plan for production and marketing:
Create a IOS & Android specific apps using React Native
Mobile device task based usability test
Create goto market strategy to determine how to market the app
Learnings & Reflections
What Worked Well
AI-Assisted Development at Speed: Building a full-stack application in 2.5 days felt like science fiction. The key was treating AI as a thought partner in the planning phase, not just a code generator. The investment in a comprehensive PRD paid dividends throughout.
Design System First: Creating a design system before building felt slow initially, but resulted in a visually cohesive product without constant design decision-making during development. This approach is now part of my standard process.
Constraint-Driven Focus: The 4-day timeline forced ruthless prioritization. Every feature had to justify its existence. This constraint paradoxically led to a better product than if I'd had unlimited time.
Impact on Future Work
Process Adoption: The AI-assisted workflow from this project is now my default for rapid prototyping. I've used it on 3 subsequent projects with similar success.
Product Thinking: This project reinforced that solving your own problem is the fastest path to product-market fit validation. I'm now more intentional about identifying pain points in my own life as product opportunities.
Scope Management: The aggressive scope reduction taught me that shipping something narrow but excellent beats shipping something broad but mediocre. This mindset now influences all project planning.