myBlock.ai — Municipal Intelligence Platform
This Ospina case study documents how Carlos Rico-Ospina approached a specific risk, infrastructure, revenue, or research problem and what was built to address it.
The Zillow for real investors—an AI assistant that analyzes municipal fiscal health to predict long-term property value.
The Problem
Consumer real estate platforms focus on property aesthetics and features but ignore the underlying fiscal stability of the governing municipality—a critical driver of long-term value and quality of life. Investors are flying blind on the single biggest factor affecting their returns.
The Insight
The key product gap isn't 'more listings data'—it's decision-grade context about the municipality that underwrites property outcomes. Tax base health, debt levels, service quality, and governance stability directly impact property values, yet this data is scattered across PDFs, city websites, and obscure reports.
What I Built
- Built an AI assistant that analyzes municipal financial health and long-term viability
- Designed dynamic data aggregation engine—when users query new locations, system autonomously fetches, processes, and stores relevant data
- Implemented Convex backend for real-time data storage with continuous refinement based on user demand
- Created Next.js 15 App Router frontend with SSR, SEO optimization, and shadcn/ui components
Outcomes
- Turns scattered municipal research into structured, queryable intelligence
- Each query expands the knowledge base—compounding coverage over time
- Production-ready architecture with Convex backend and modern TypeScript stack
- Addresses under-modeled driver of long-term value in consumer RE tooling
Why It Matters
Most RE platforms optimize for browsing. This optimizes for decision-making.
The 'autonomous fetch + store + refine' loop creates compounding value—each query improves future answers.
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