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Project overview
Ryna, a pioneering co-living and apartment rental platform, embarked on a transformative journey to revamp its digital presence. With a mission centered on empowering individuals who identify as women to secure safe and convenient housing, Ryna sought to revolutionize the renting experience through technology.
Listing
enquiry rate
Concept to Live
MVP
Support queries
automated
The challenge
Ryna’s mission was clear: give women a trustworthy, curated way to find housing without the anxiety, safety concerns, and information gaps that come with generic rental platforms. But the product wasn’t delivering on that promise. Listings were inconsistent, verification was manual, and the experience left users.
How we worked
We began with deep audience research — understanding exactly what women renting in Indian cities needed, feared, and expected from a housing platform. We mapped the full journey from first search to move-in, identifying every friction point and every trust signal that mattered. This shaped every design and AI decision that followed.
We built and shipped the full platform — web and mobile, backend infrastructure, Claude agent integrations, and an admin dashboard for the Ryna team — in twelve weeks. The MVP launched with AI-powered listing matching, a 24/7 support agent, and automated verification workflows active from day one. No hand-offs. One team, start to finish.
We designed Ryna’s AI architecture around three core jobs: matching renters to verified listings based on nuanced preferences and safety requirements, automating the support and query layer so no question went unanswered at 11pm, and streamlining listing verification so the team could keep data quality high without scaling their ops headcount. Claude’s long context window was central to how the matching agent reasoned about safety and fit simultaneously.
We rebuilt the information architecture around trust, clarity, and safety — not just discoverability. Every listing page, search flow, and profile screen was redesigned so that the most important signals (verification status, house rules, nearby safety infrastructure) were the first things a user saw. Navigation was reduced to its essential paths, and mobile was treated as the primary surface throughout.
Scope of work
A production-ready Claude agent that understands a renter’s full context — budget, location, lifestyle, safety priorities, and community preferences — and surfaces the most relevant, verified listings with clear reasoning behind each match.
A Claude-powered support agent that answers queries about listings, lease terms, house rules, neighbourhood safety, and platform policies at any hour — escalating to the Ryna team only when human judgment is genuinely needed.
A fully rebuilt web and mobile platform — listing search, profiles, verification badges, saved properties, enquiry flows, and a community space — all backed by a secure Node.js backend and designed mobile-first throughout.
An AI workflow that automatically reviews new listing submissions, checks for completeness and policy compliance, flags anomalies for human review, and approves or returns listings with structured feedback — keeping data quality high without manual overhead.
A visual and interaction design system built specifically around trust signals — verification badges, safety ratings, community reviews, and host credentials — so every screen communicates reliability before a user has to ask.
A dedicated admin interface that gives the Ryna team live visibility into listings, enquiries, verification queues, and support agent performance — with the ability to manage content, update policies, and monitor AI outputs without engineering support.
Services applied
Claude-Powered AI Agents
- Tool Use
- Long Context
- Human-in-Loop
AI SaaS Development
- Full-stack Build
- Web & Mobile
- Backend APIs
Claude MVP Development
- 12-week Delivery
- Lean Scoping
- AI Architecture
AI Workflow Automation
- Verification Pipeline
- Auto-classification
- Ops Automation
Results
Ryna launched to a meaningfully different reception than before.
Listing enquiry rate increased 3× in the first month, driven by the AI matching agent surfacing genuinely relevant results.
Onboarding completion improved significantly after the redesigned first-session flow and reduced step count.
80% of support queries handled end-to-end by the Claude agent from day one of launch — no human escalation needed.
Listing verification time reduced from days to under 30 minutes per listing via the automated pipeline.
Mobile session duration increased as users engaged with the redesigned discovery and safety-signal layers.
“PixlerLab understood Ryna’s mission at a level we didn’t expect from a technology partner. They didn’t just build a better website — they built a platform that actually feels safe. That’s a hard thing to design for, and they got it right.”
Ryna Team
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