Quick Summary
- ▸ Built for a real estate analytics startup in Los Angeles, CA, United States.
- ▸ CodeBranch built an AI-powered proptech platform with interactive maps and conversational AI to evaluate commercial and industrial real estate assets using data-driven insights.
- Delivered a fully functional MVP with interactive maps, conversational AI chat, and statistical engines
- Built a proprietary proxy sales model estimating asset revenue with reliable precision where no market data existed
Overview
Real estate agents and investors evaluating commercial properties often lack the data they need to make confident business decisions — sales figures are guarded, foot traffic is opaque, and comparing locations requires manual research across fragmented sources.
A proptech startup partnered with CodeBranch to solve this problem by building an AI-powered web application that gives users instant, data-driven insights into commercial and industrial assets. The platform features an interactive map with Google Maps integration, a conversational AI chat powered by OpenAI, and proprietary statistical models — including a proxy sales estimator and a foot traffic analyzer that differentiates raw traffic from engaged visitors.
The application includes an administrative dashboard with membership plans and user tiers. CodeBranch delivered a cost-optimized MVP that the client is currently using as the core product asset for venture capital fundraising.
Industries
Services Provided
Approach
CodeBranch took a consultative co-creation approach, guiding the founders through the process of translating business pain points into technical requirements. The team managed the full backend infrastructure — cloud orchestration via Terraform, geospatial mapping with Google Maps API, demographic data from the Data Commons API, and AI integration with OpenAI — while keeping the client focused purely on product value and market fit.
Through cooperative solution building and iterative product discovery, the project evolved from its initial concept into an advanced analytics platform capable of evaluating foot traffic patterns at the storefront level, estimating proxy sales using proprietary statistical models, and running automated competitive benchmarking against major retail chains.
The conversational AI interface was built with automated data guards for security and off-topic filtering, ensuring reliable responses for end users. The platform was architected specifically for low infrastructure costs, maximizing the startup's runway during their fundraising phase.
The team grew dynamically from 1 to 3 full-stack engineers alongside a software architect and project manager, adapting to the project's velocity at each stage.
CodeBranch partnered with a proptech startup to build an AI-powered analytics platform that transforms how real estate agents and investors evaluate commercial and industrial assets in the United States.
From Vision to Working Product
The founders came to CodeBranch with a clear business insight: real estate agents and investors lacked a way to evaluate commercial properties based on actual performance data rather than just listing prices and square footage. They needed a technical partner who could take that vision and turn it into a product — handling everything from architecture decisions to cloud infrastructure while they focused on the market.
What the Platform Does
The application lets users explore a map of commercial and industrial properties, select any location, and instantly see data-driven insights about that asset. A conversational AI chat powered by OpenAI allows users to ask natural language questions — “How does this pharmacy compare to others in the area?” or “What’s the estimated annual revenue for this location?” — and get answers backed by real data.
Behind the interface, the platform runs three proprietary analytics engines:
- Foot Traffic Differentiation — separates raw foot traffic from simple visits and from highly engaged consumers who stay and purchase, giving agents a clear picture of a storefront’s visual attraction and conversion potential.
- Proxy Sales Estimation — cross-references demographic data, traffic patterns, and regional market indicators to estimate an asset’s annual revenue with reliable precision, providing certainty where no direct sales data exists in the market.
- Competitive Benchmarking — compares the same store across different locations, evaluates neighborhoods against each other, and benchmarks any store against others in the same category. Users can see how a pharmacy in one zip code performs relative to the same chain in another area, or how it stacks up against competitors like Walgreens or CVS in the same region.
Built for Low Cost, Built for Fundraising
The platform was architected from day one with the startup’s financial constraints in mind. Cloud infrastructure managed via Terraform keeps monthly operating costs low, giving the founders an extended runway during their evaluation and fundraising phase. The administrative dashboard includes customizable user tiers and membership plans, making the platform monetization-ready from launch.
The MVP is fully operational and currently serves as the core product asset for the client’s ongoing venture capital fundraising rounds.
Results
- Delivered a fully functional MVP with interactive maps, conversational AI chat, and statistical engines
- Built a proprietary proxy sales model estimating asset revenue with reliable precision where no market data existed
- Automated foot traffic analysis differentiating raw traffic, visits, and engaged visitors
- Enabled competitive benchmarking against major retail chains
- Optimized infrastructure costs for extended startup runway
- Platform serves as core asset for ongoing venture capital fundraising
