Oramo is the property management platform I’m building for Romanian landlords in a three-founder bootstrap. Two features I led from design through implementation — with AI (Claude Code) as a force-multiplier, not a replacement — and the question about data, trust, and multi-tenancy they turned out to share.
Tens of discussions across the country — 30+ with landlords managing portfolios of 1 to 10+ properties, 10+ with agencies — plus competitive review and structured survey work surfaced one finding: most landlords operated their portfolios with zero profitability visibility. The tool of record was Excel paired with WhatsApp. Supply-side software penetration was near zero.
Co-founder and product designer. In a three-person founding team — real-estate expertise, financial strategy, and me on product strategy, design, and implementation — I own the product surface end-to-end. Bootstrapping meant leveraging AI (Claude Code) heavily as a tool to extend what three founders could ship — not to replace roles on the team. AI as force-multiplier, not solution.
1. Build the data layer as if it were the product. The market-intelligence engine parses asking-price data from three of Romania’s largest real estate listing websites into per-zone benchmarks. The move that matters is what comes next: as Oramo users sign leases inside the product, the platform accumulates the first real-rental-price dataset in Romania — actual closed-lease data, per zone. The listing scrape is designed as a bootstrap; the surface stays stable while the data behind it gets better.
2. An AI assistant inside a multi-tenant product is a permissions problem in a conversation costume. The in-product assistant answers a landlord’s questions from context they legitimately own. The hard decision was what it must never do — reveal anything about another landlord’s portfolio, a neighboring property’s rent, or any global detail — on every turn, for every phrasing.
Working principle: the assistant’s context is the user’s own data plus anonymized zone-level aggregates. Anything more specific than a zone is outside the boundary, regardless of how the question is phrased. Implementing that — retrieval scope, refusal behavior, confidence of refusal — is most of the work.
3. Choose conversation where the alternative is a form nobody wants to fill in. Small-portfolio landlords aren’t power users. Dashboards reward people who want to look at their data; conversation rewards people who have a question. Nobody we spoke to said “I wish I had a dashboard.” They said “is this one still worth it?”, “am I leaving money on the table?”, “did Gigi pay?” The interface followed the question.
The most consequential decisions on an AI-native product are about what the system should and should not be able to know. Those decisions live in the data layer, not the interface.
And: a small team using AI as a tool, not a solution, can now ship what would have taken a much larger team two years ago. That’s the shape of work I’d bring into a larger team — someone who designs the surface, understands the system underneath, and makes the tradeoff calls in the room alongside engineering, not in isolation.