Realcomm 2026: Closing the loop on Tech REset
Our team thrived during a very productive week at Realcomm 2026 in San Diego!
Connecting with our peers, partners, and developers who shape commercial real estate is always a valuable opportunity to confirm market demands, align on industry needs, and just catch up.
Last year in Savannah, we demystified the Backwards Equation of real estate tech, where we urged firms to prioritize core business value over technical hype to avoid poor data models. This year, the event theme — Tech REset — served as a continuation of this strategy. A real reset requires stripping away the noise, focusing on software that performs, and establishing data-first foundations for the new agentic AI reality.
Commercial real estate companies are moving past basic experimentation. The industry focus is shifting toward a practical goal: making data systems dependable at scale. The driving force behind this reset isn't just better software, but achieving strategic business goals. A stable data setup transforms fragmented property information into functional insights and tools for precise underwriting, vacancy forecasting, and utility management.
Pillars of a practical tech reset
Conversations on the floor showed that industry leaders want to bridge the gap between their legacy platforms and modern, high-performance infrastructure. They recognize that in order to achieve their strategic business goals, they need their back-end to be architecturally sound, operationally fluid, and built for advanced automation, focusing on three core areas:
- Solid agentic data architecture: Everyone knows disparate and siloed data creates chaos. Legacy systems with clunky bolt-ons are not scalable. Firms now use platforms like Palantir, Databricks, and Snowflake to combine separated datasets, clean up reporting, and turn raw information into a clear business asset.
- Streamlined workflows: Reliable analytics require clean input and output throughout the entire data flow. For example, digitalizing core leasing and property management steps ensures that information stays accurate right from the point of capture.
- Targeted machine learning: Real estate teams deploy specialized models for specific, high-value tasks, such as tenant risk profiling, automated property valuations, and operational demand forecasting.
Cross-industry engineering frameworks for better data standards
Best in breed organizations in more advanced verticals recognize that dependable architecture and strict data governance are the only ways to scale autonomous systems safely. By observing how sectors like fintech and logistics handle high-throughput, risk-sensitive data, commercial real estate can bypass the experimental trial-and-error phase entirely.
CRE executives understand their markets deeply. Because the real estate tech environment is still catching up to other verticals in their data journey, companies can adopt proven data strategies from these adjacent industries to avoid common development mistakes.
"One lesson CRE can borrow from more data-mature industries is that AI systems should not just produce outputs — they need feedback loops and verification layers built into the architecture. In fintech or logistics, you don't only have people checking the work here and there; you track where the agent was corrected, which assumptions changed, and whether its recommendations held up in the real business process. I think that's a big opportunity for CRE now: not just adding agents, but making their work measurable, auditable, and self-improving." — Ihor Kroosh, VP of Data & AI, Proxet
Making AI dependable for real estate operations
Building verification layers directly into your data architecture shifts complex systems like agentic AI from an experimental project to a dependable business tool. By closing the feedback loop, real estate firms gain three critical advantages:
- Measurability: Teams can quantify exact operational returns and model accuracy.
- Auditability: Decision pathways and altered assumptions remain completely transparent and trackable, protecting high-stakes leasing and investment choices.
- Systemic Improvement: Built-in feedback mechanisms allow agentic AI to learn from daily operations, refining predictions automatically over time.
This strategy introduces the institutional engineering standards needed to turn software into a reliable operational partner, building on existing real estate frameworks rather than forcing a complete rewrite.
Community and collaboration
Realcomm succeeds because of the people who attend. Beyond the technical sessions, we spent our evenings focused on long-term relationships over dinner and conversations.
We appreciate everyone who attended our dinner at the historic Hotel del Coronado on Tuesday night. The open dialogue and shared perspectives made it a standout evening.
We also want to thank our co-hosts at Cherre, Dealpath, and Cove for co-organizing another iconic VIP party at The Whiskey House. It was a fantastic week of catching up with friends, meeting new partners, and wrapping up Realcomm on a high note.
Next steps
Proxet is ready to help real estate organizations map the overarching data strategy and build the clear, functional data pipelines they need to scale. Whether you need to design an enterprise roadmap, fix a broken pipeline, or deploy verifiable AI systems, we combine high-level strategic alignment with rigorous engineering to deliver a distinct business advantage.
If we missed you in San Diego, let's connect to talk about how we help market leaders like you build stable tech infrastructure. See you at the next industry event!