Real Estate

Klipster

A real estate discovery platform with live streaming property tours, agent-driven content, and rental applications — from MVP strategy through App Store launch and continuous evolution.

2+ years Partnership
960 Deliveries
2+ years Timeline
95% On Budget
4.5d Avg Cycle Time
Klipster — Real estate discovery app with live property tours
Strategy 8 weeks
Launch 8 months
Evolve 2+ years

The Bet

Klipster’s founder saw a gap in how people discover rental properties. Traditional listings are static — photos, descriptions, and hope. Klipster’s vision: let real estate agents live stream property tours directly to prospective renters, creating an interactive discovery experience where buyers can ask questions in real time, save properties, and apply on the spot.

The product needed to work for two audiences simultaneously — agents who create content and list properties, and renters who discover, watch, and apply.

The Complexity

This isn’t a simple listing app. It’s a real-time media platform with transactional workflows:

  • Live streaming infrastructure — WebRTC-based video with real-time chat, viewer management, and the ability to simulcast to external platforms
  • Property discovery — search with map integration, filters for bedrooms, bathrooms, price, and neighborhood — across a growing inventory
  • Agent content platform — agents create “Klips” (short-form property content), manage listings, build profiles, and go live to showcase properties
  • Rental application flow — renters apply to properties directly through the app with a single application form that works across multiple listings
  • Dual-sided marketplace — agent onboarding, renter onboarding, messaging between parties, and notification systems for both sides

The Thinking

We started with a strategy phase — heuristic evaluation of the existing codebase, a tech deep dive, and an MVP launch approach that prioritized the features that would create the core engagement loop: discover → watch → apply.

MVP scoping — We identified the minimum feature set that would validate the live streaming thesis: property listings, agent profiles, live video with chat, and a basic discovery feed. Everything else (applications, simulcasting, map search, Klips) came in subsequent phases based on user feedback.

Architecture decisions — Real-time video required a streaming infrastructure separate from the REST API. We designed the backend with dedicated streaming endpoints (offer/answer/ICE signaling) while keeping property, user, and application data on a conventional API layer.

The Build

960 deliveries shipped over 2+ years, evolving from MVP to a full-featured platform:

MVP & App Store launch — Built the core product from strategy through store submission. Property discovery feed, agent profiles, live streaming with real-time chat, and user authentication. Launched on both iOS and Android app stores.

Application process — Added the rental application flow so renters can apply to properties directly through the app. Single form, multiple properties, streamlined for mobile.

Content & discovery — Built the Klips system (agent-created property content), enhanced search with map-based discovery and filters, added property sharing across platforms, and video upload capabilities.

Growth features — Push notifications, livestream filters, agent discovery views, simulcasting to external platforms, and a web version for property discovery and search.

The Proof

From a concept and an existing codebase to a live product on both app stores — with continuous feature expansion driven by real user feedback.

  • 960 deliveries shipped and validated in production
  • 2+ year continuous partnership from strategy through evolution
  • iOS + Android + Web — full cross-platform coverage
  • Live streaming — real-time property tours with chat, built on WebRTC
  • Complete marketplace — discovery, content creation, applications, and messaging

The Partnership

Klipster is a 0→1 product partnership. We shaped the strategy, built the MVP, launched to app stores, and continue to evolve the product as the market responds. The roadmap is driven by real usage data — not assumptions — and every feature ships through the same predictive delivery system.

Total Deliveries 960
Forecast Accuracy 95%
Avg Cycle Time 4.5d
Timeline 2+ years
On-Time Delivery Rate
96% on time Target: 95%
YL
Yanna Lopes Product Lead
PJ
Paulo Justiniano Lead Engineer
DU
Daniel Urruela Growth & Partnerships
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