What's New at Geolava

Commercial Observer’s Q&A gives a founder-level view of why Geolava exists and what “spatial intelligence” looks like in practice. It discusses the shift from reactive, manual surveying toward a more predictive, continuously updated property intelligence layer. The interview also describes the role of high-resolution imagery and contextual signals like zoning and local conditions in building a complete property picture. If you want a simple narrative you can share with prospects who ask “what’s the wedge?” this is one of the clearest explanations. It’s also a good introduction to the longer-term ambition: monitoring assets over time, not just at transaction moments.

This GlobeSt feature focuses on the gap between how important buildings are and how “offline” property intelligence still is in day-to-day work. It describes Geolava’s approach as building a “digital mosaic” of a property using multiple data sources rather than relying on a single snapshot. The piece calls out how different forms of imagery and sensing can surface issues and patterns that normally require time-consuming site work. The takeaway is less about replacing experts and more about keeping a consistent pulse on assets as conditions change. If your team cares about faster underwriting, tighter valuation support, and fewer blind spots, it’s worth a read.

This Biz Journals profile frames a simple idea: real estate is one of the biggest asset classes, yet property understanding still relies heavily on site visits and fragmented documentation. The story highlights how Geolava approaches assessment and compliance analysis as a software problem, not a manual process. It’s useful context if you’re tracking how AI is being applied to property workflows that traditionally live in spreadsheets, PDFs, and vendor reports. The article also anchors the company in San Francisco’s proptech ecosystem and the broader shift toward digitizing the built world. If you’re evaluating tools that can speed up early diligence and reduce repeat work, this piece is a good starting point. Read the full piece here: Biz Journals article. (One News Page)

Commercial real estate runs on physical constraints, but too many decisions still depend on slow, manual workflows and stale point-in-time data. This Autopilot “Industry Breakdown” walks through where AI is already showing up across the CRE lifecycle, from development and operations to investment decisions. It also lays out the core concepts that sit underneath valuation, including cap rates and how market conditions shape pricing. If you want a solid landscape view of CRE tech, the piece maps the modern stack and explains why “build vs buy” matters for most teams. It’s a practical overview for anyone trying to understand where spatial signals, context, and automation can actually move the needle.

Emerging from stealth, the platform fuses live data with machine reasoning to transform property and infrastructure data into actionable insight.

L'entreprise dévoile une plateforme qui allie données en temps réel et apprentissage automatique pour générer des informations concrètes sur l’immobilier et les infrastructures.

Emerging from stealth, the platform fuses live data with machine reasoning to transform property and infrastructure data into actionable insight.

Commercial real estate has always been one of the most inefficient markets in the world. While we’re still far from making property transactions as simple as stock trades, artificial intelligence is beginning to address fundamental problems that keep $20 trillion worth of real estate difficult to buy and sell.