<aside> 💡 The difference between a product that ships and one that doesn't? A validated strategy before a single line of code is written. This guide shows you exactly how we do it — with real examples from projects we've delivered.
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Inside this guide, you'll find real SOWs, discovery interview scripts, FigJam research boards, branded delivery documents, and ClickUp project structures — all from actual Varritech engagements.
Every project at Varritech begins with a structured discovery phase. We use FigJam as our collaborative research canvas — mapping user journeys, competitive landscapes, feature prioritization, and technical constraints before committing to a single design or line of code.
PortableTenant is a tenant screening platform that enables renters to maintain a single, reusable screening report. When we kicked off this engagement, we needed to understand a complex multi-sided marketplace: renters, landlords, and integration partners.
<aside> 🗺️ FigJam Research Board — What We Mapped: • User journey maps for 3 personas (renter, landlord, partner developer) • Competitive analysis matrix: TransUnion SmartMove, Experian RentBureau, Avail, Zillow Rental Manager • Revenue model canvas: tenant fees, partner affiliate commissions (20%), yearly partner subscriptions • Technical constraint mapping: data portability requirements, multi-partner sharing, compliance (FCRA, GDPR) • Feature prioritization using MoSCoW: 12 Must-haves, 8 Should-haves, 5 Could-haves • Integration architecture diagram: REST API, embeddable widgets, webhook events
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https://www.figma.com/board/P38eLRVVMJMzainH9VYY5g/Portable-Tenants-Competitive-Benchmark--FigJam-
👆 Open the PortableTenant Competitive Benchmark FigJam board — Includes competitive benchmark of LeaseRunner, Zillow Rentals, and Experian; user stories for tenants and landlords; onboarding flow analysis; and UX annotations across 129 research elements.
Key insight from discovery: The biggest risk wasn't technical — it was that both renters AND landlords needed agreements with PortableTenant for the platform to work. This two-sided adoption challenge shaped our entire go-to-market strategy and phased rollout plan.
TutorAssist AI is a gamified learning platform that combines AI tutoring with personalized feedback to keep students motivated and challenged. Discovery was critical because education technology has diverse user needs — from K-12 students to adult learners — and the AI features needed to adapt to different learning styles, knowledge levels, and curriculum standards.
<aside> 🗺️ FigJam Research Board — What We Mapped: • Learner persona spectrum: K-12 students (guided learning), college students (self-paced), adult learners (skill-building), educators (content creation & monitoring) • Competitive teardown: Duolingo, Khan Academy, Quizlet, Photomath — mapped strengths/weaknesses across engagement, AI quality, gamification, and content depth • AI use cases: Ace AI companion for contextual tutoring, adaptive difficulty, personalized feedback, learning path generation • Infrastructure decisions: React Native (Expo) for cross-platform mobile, Express.js backend, NativeWind/Tailwind for design system • Gamification model: XP/leveling system, achievement badges, daily challenges, leaderboards, streak tracking • Monetization model: freemium with IAP (Apple + Google), premium AI features and advanced analytics as subscription
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https://www.figma.com/board/IM0lHEw6e3XTRKZtvX9R43/TutorAssist-AI-UserResearch--FigJam-
👆 Open the TutorAssist AI User Research FigJam board — User research for an AI-powered tutoring platform, including persona mapping, competitive analysis, and feature prioritization.
Key insight from discovery: Students and parents are overwhelmed by generic learning apps that don't adapt. The number one feature request wasn't more content — it was an AI companion that truly understands where the student is struggling. We designed Ace, a contextual AI tutor that provides real-time hints, personalized explanations, and adaptive difficulty — turning passive content consumption into active, guided learning. The gamification layer (XP, badges, leaderboards) was the retention mechanism, but AI-driven personalization was the core value proposition.