, a startup that has built AI-powered commercial real estate software for institutional investors, has raised $18 million in a Series A round at a $100 million valuation, it tells 蹤獲弝け News exclusively.
led the financing, which included participation from 泭硃紳餃 angel investors from , 泭硃紳餃 , among others. Founded in 2022 by former institutional commercial real estate operators and , Cambio has now raised a total of $22 million. It participated in Y Combinators summer 2022 cohort and then went into R&D mode.
The San Francisco-based company launched its offering at the end of 2023, and de Guzman claims it has since seen rapid adoption across enterprise customers and geographies scaling to 35 countries and to more than 2 billion square feet in assets. It recently opened a London office to support EU and APAC growth.
From messy to investment-grade data
Put simply, Cambio uses large language models and agentic artificial intelligence workflows to turn messy building data into investor-grade decisions and reporting. And it claims to do so within minutes.
Commercial real estate owners sit on thousands of pages of unstructured documents spreadsheets, PDFs, invoices, energy audits, regulatory filings that historically required months of manual analysis, de Guzman told 蹤獲弝け News. Cambio applies large language models and agentic AI to ingest, reason over, and synthesize that data automatically, delivering investment-grade capital and compliance decisions in minutes.
Cambio, she said, is architected around agentic AI software that can reason across unstructured data, run multi-step analyses, and continuously adapt as regulations, assets, and market conditions change.
In a nutshell, it aims to help institutional investors figure out where to deploy capital, which assets to prioritize, and how to maximize returns. Customers include , , , 泭硃紳餃 , among others.
The market opportunity, according to de Guzman, is enormous: commercial real estate is estimated to be in the U.S. alone.
In 2025, global real estate-related startups pulled in about $10.5 billion in seed- through growth-stage financing, per 蹤獲弝け . Thats up about 17% from $9 billion in 2024.
An industry track record
Part of Cambios strategy is to have built a (largely female) leadership team that has directly worked in the space it is trying to serve. De Guzman and Grayson began their careers at large institutional firms such as and Oxford Properties.
The startup also recently hired alumna to serve as head of product innovation. , formerly of Oxford Properties and , has been tapped to serve as lead of Cambios 蹤獲弝け and APAC business.
The moves from institutions to a startup serving them reflect a broader shift happening in commercial real estate, noted Grayson the choice to step inside AI transformation, and not just observe it from the sidelines.
Cambio operates an enterprise SaaS revenue model. It plans to use its new capital primarily to scale product and engineering.
, managing director at Maverick Ventures, told 蹤獲弝け News via email that his firm was impressed with the fact that Cambios founders had spent more than two decades running commercial real estate portfolios.
That lived experience matters, he said. It gave them an intuitive understanding that this wasnt a tooling problem it was a workflow problem.
Maverick was also impressed by the fact that Cambio wasnt trying to bolt AI onto existing processes.
Re-architecting the workflow
Cambio isnt automating around the edges, its re-architecting the workflow end to end in an AI-native way, with a deeply product-minded approach, Isono said. Knowing what to build, which workflow to wedge into, and how to sequence products requires having lived these workflows, which Leia and Steph have.
The investor also praised the startups technical team, noting that CTO was one of the earliest backend engineers at , a marketplace and wholesale platform that was valued at nearly $13 billion in 2022. That technical team, he said, also includes Ph.D.s with deep expertise in building science.
That matters because commercial real estate is fundamentally tied to the physical world. Much of the data isnt clean or fully digitized, and automating it is meaningfully harder than in purely digital domains, Isono said. Solving that unlocks a uniquely valuable dataset that compounds over time.
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