Stellitron
Solving the 'Popularity Bias' in VLM-Based Infrastructure Dating
Solving the 'Popularity Bias' in VLM-Based Infrastructure Dating
The Problem
Standard Vision Language Models (VLMs) suffer from 'popularity bias,' accurately dating landmarks while failing catastrophically on the 'boring' infrastructure that makes up 99% of municipal assets.
- ⚠Multi-billion dollar inaccuracies in insurance risk profiling.
- ⚠Flawed municipal depreciation schedules based on unreliable age data.
- ⚠Generic AI models ignore critical architectural markers like HVAC styles and window glazing.
The Solution
Stellitron utilizes a proprietary VLM architecture fine-tuned on architectural evolution datasets to provide forensic-grade age estimation for non-landmark buildings.
Architectural Forensics
Detecting specific building code markers and material degradation patterns.
Bias Elimination
Training on 'long-tail' infrastructure rather than just famous landmarks.
GIS Integration
Seamlessly embedding accurate dating data into municipal workflows.
Market Opportunity
“Organizations are moving from AI experimentation to impact in 2025, with specialized VLM applications leading growth.”
Competitive Landscape
Competitive Landscape
| Feature | Cape Analytics | ZestyAI | Nearmap | Stellitron |
|---|---|---|---|---|
| Non-Landmark Dating | Low | Low | Medium | High |
| Architectural Forensics | Low | Low | Low | High |
| Inventory Accuracy | High | Medium | High | High |
Business Model
Municipal SaaS
Annual subscription for city-wide infrastructure inventory and dating.
Insurance API
Per-lookup fee for insurance carriers during property underwriting.
Strategic Data Licensing
Bulk data access for real estate analytics and urban planning firms.
Traction & Validation
“Stellitron's ability to identify building ages through architectural markers is a game changer for our risk assessment models.”
Financial Projections
Yearly Revenue Projections
Key Performance Indicators
The Ask
Use of Funds
Exit Strategy
Exit Scenarios
Comparable Exits
Risk Analysis
Risk Analysis & Mitigation
Intense competition from established incumbents like Nearmap.
Focus on high-margin vertical integrations for automated underwriting.
Accuracy degradation across diverse geographical terrains.
Continuous active learning loop with human-in-the-loop verification.
High CapEx for premium data acquisition.
Hybrid sourcing model using public data for baseline inventory.
Sources & References
Generated by
Stellitron AI
Data Sources
Exa AI Web Search
Municipal Building Permit Registries
Geospatial Industry Reports 2025
References
For inquiries, contact:
contact@stellitron.comThis pitch deck is AI-generated for illustrative purposes. All financial projections and market data are estimates based on context available as of Dec 2025.