This is a weekly feature that runs down the week鈥檚 Top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

Startup investors aren鈥檛 exactly hitting pause, but they did scale back on giant rounds this past week, with just two startup financings in the multiple hundreds of millions. The biggest round went to AI infrastructure provider , which picked up $750 million, followed by construction automation unicorn , which secured $200 million. Other sectors that attracted larger rounds this past week included biotech, insurance and AI.

1. , $750M, AI infrastructure: Modal Labs, a provider of cloud infrastructure for AI inference and other applications, secured $750 million in a Series D funding round led by at a $15.75 billion valuation. The New York-headquartered company said it recently exceeded an annual revenue run rate of $1 billion.

2. , $200M, construction tech: Austin-based Icon, a developer of robotic construction systems known for its 3D-printed homes, closed on $200 million in Series D funding led by听 and . The company says it is on pace for 4x revenue growth in 2026.

3. , $135M, RNA therapies: Boston-based Souffl茅 Therapeutics, a developer of RNA therapies, raised $135 million in Series B funding led by as part of a large syndicate of investors.

4. , $103M, gene therapies: Menlo Park, California-based Estuary Biotherapeutics, a developer of genetic medicines for patients with serious kidney diseases, launched with $103 million in financing. The funding includes $93 million in a Series A round led by as well as $10 million in previously undisclosed seed capital.

5. , $100M, AI tools: Labelbox, a developer of technology to train and evaluate AI models and to help deploy agents that improve with experience, picked up $100 million in Series E funding. led the financing for the 8-year-old, San Francisco-based company.

6. , $90M, health AI: Redwood City, California-based Suki, a provider of AI tools that turn clinical conversations into intelligence for documentation, coding and other healthcare workflows, announced a $90 million Series E financing from backers including and .

7. , $82M, caregiver platform: Abby Care, a platform for family members to become certified, paid caregivers and deliver care at home, closed on $82 million in Series C funding, per a . led the financing for the 5-year-old, San Francisco-based startup.

8. , $70M, insurance: Stand, a provider of insurance for homes in areas at higher risk of natural disasters, secured $70 million in Series C funding at a $1 billion valuation. led the financing for the 3-year-old, San Francisco-based company.

9. , $65M, semiconductor systems: Mueon Corp., a semiconductor systems company building a modular platform for AI and hyperscale computing, completed a $65 million Series A funding round. led the financing for the Portland, Oregon-based company.

10. , $60M, AI spend management: New York-based North.cloud, provider of an infrastructure spend intelligence and automation platform, raised $60 million in a Series B round led by .

Methodology

We tracked the largest announced rounds in the 萝莉视频 database that were raised by U.S.-based companies for the period of Oct. 10-16, 2026. Although most announced rounds are in the database, there may be a small time lag, as some rounds are reported late in the week.

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Twenty-one of last month鈥檚 new unicorns are U.S. headquartered. Six new unicorns joined the board from China and three from the U.K., while Canada, Saudi Arabia, Japan, Israel, Italy, Finland, Germany and Bulgaria each added one new unicorn.

Through September, 311 companies have joined the board so far this year, putting 2026 on track to be the second-highest year for new unicorn creation, following only the venture peak year of 2021, when counts topped 600.

Several companies already on the board stand out for significant valuation increases via new raises last month. They include San Francisco-based , developer of AI for software engineering, which saw its value climb to $48 billion, up from $26 billion only four months earlier. Data center provider was valued at $31 billion last month, up from $10 billion a year ago. And Paris-based frontier lab was valued at $24.4 billion in its more recent funding, up from $13.7 billion a year ago.

New unicorns in September

Here are September鈥檚 38 new unicorn companies:

Semiconductors

• , an Austin-based semiconductor manufacturing startup, closed a $500 million Series A at a $3.7 billion valuation, led by . The 3-year-old company builds compact chip factories and develops their manufacturing equipment internally, aiming to make semiconductor production more accessible.
• , an Irvine, California-based semiconductor company, raised a $275 million Series C, valuing it above $3 billion and bringing total funding to $415 million. , and led the round. The 2-year-old company develops digital signal processing chips for high-speed optical connectivity in AI infrastructure.
• , a Hangzhou-based AI chip developer, announced a $149 million Series C. Its IoT inference chips combine memory and computing to reduce power consumption and data movement. The 5-year-old company was valued at $1 billion.
• , a Beijing-based AI chip company, raised $298 million at a valuation of $2.2 billion. The 4-year-old company develops AI computing chips using the open RISC-V architecture.

• , a San Jose, California-based developer of specialized AI chips and software, raised a $150 million Series C at a $1.45 billion valuation, co-led by and , bringing total funding to $500 million. The proceeds for the 8-year-old company will support its software platform and next-generation hardware for robots, vehicles and drones.

Financial services

• , a Texas-based prediction-market platform, announced a strategic partnership with , which will take an equity stake following OG鈥檚 separation from . The less than 1-year-old company was valued at $5 billion. The investment amount was not disclosed; the partnership will route Robinhood event-contract trading through OG鈥檚 regulated exchange and clearing infrastructure.
• Saudi Arabia-based , a digital payments company, raised a $329.5 million Series A at a $1.85 billion valuation. Investors included , and . The 3-year-old company has processed more than $117 billion in 2 years.
• , a Nevada-based financial software company, raised a $250 million Series E at a $1.43 billion valuation led by founder and chairman . The 8-year-old company provides an AI-based investment management platform for asset and wealth managers.
• , a Miami-based remittance platform for Latino immigrants operating through , secured $200 million in financing: an $87 million Series C equity round led by valued at $1.4 billion and $113 million in debt from 鈥檚 Customer Value Fund. The 5-year-old company plans to expand to lending and add to its Latin American reach.

Space technology

• Munich-based space company raised a $450 million Series C, co-led by , and at a valuation of $2 billion. The financing for the 5-year-old company will advance its reusable Nyx spacecraft for an International Space Station mission and support development of its Storm rocket engine.
• , a Seattle-based satellite connectivity company, raised a $200 million Series C at a $1.6 billion valuation, led by , bringing total funding to approximately $300 million. The 5-year-old company enables standard Bluetooth devices to connect directly to satellites for tracking and sensor-data applications.
• Oxfordshire, U.K-based , a satellite company, secured $346 million, co-led by and, valued at $1 billion. The 11-year-old company manufactures satellites and provides infrastructure, connectivity, Earth observation and intelligence services.
• Bulgaria-based , a satellite company, raised $205 million in a round co-led by and , reaching a $1 billion valuation. The 11-year-old company has raised financing that will expand standardized spacecraft production, including new manufacturing capacity in the U.S. and Europe.

Biotechnology and medical devices

• , a Framingham, Massachusetts-based developer of treatments for Alzheimer鈥檚 disease and other neurodegenerative disorders, raised around $79 million. The 听 13-year-old company was valued at $1.9 billion. Investors were not disclosed.
• Shanghai-based , an AI drug discovery company, raised $290 million at a $1.5 billion valuation following its spinoff from . , , and co-led the round. Anew develops tools for biomolecular prediction and drug discovery; ByteDance retained a 56% stake.
• , a New York-based brain-computer interface developer, closed a $250 million Series D, bringing total funding to $430 million and reaching a valuation of approximately $1 billion. and led the round. The 5-year-old company is building a brain鈥揷omputer interface that is minimally invasive and removable.
• Toronto-based , a biotechnology company, raised $153 million at a $1 billion valuation led by the , with participation from and other investors. The 3-year-old company uses AI to identify abandoned drug candidates that could succeed under different treatment approaches. The financing will expand clinical development and support additional drug acquisitions.

AI and internet infrastructure

• , a San Francisco-based AI cloud infrastructure company, raised a $300 million Series B at a $3 billion valuation, led by . The 3-year-old company distributes AI inference workloads across different types of chips to improve performance and efficiency. It announced in March that its customers include one of the leading frontier labs.
• Helsinki-based , an AI cloud company, raised a $189 million Series B led by . The financing valued the 6-year-old company above $1 billion with total funding over $450 million. The company manages physical data centers along with its cloud platform.
• London-based , an internet infrastructure company supporting content delivery for large streaming, gaming and software companies, raised $570 million in its first external financing led by . The 15-year-old company was valued at $1.9 billion.

AI training

• , a San Francisco-based AI training-data company, raised more than $100 million led by at a $4 billion valuation, an eightfold increase from its valuation in September 2025. The 4-year-old company recruits and vets specialists who create training and evaluation data for AI developers. that participants included two frontier AI laboratories and two co-founders.
• , a San Francisco-based provider of expert-created AI training data, reached a $3.2 billion valuation in September, earlier, according to . The funding amount and lead investor were not publicly disclosed. The 1-year-old company鈥檚 datasets capture professional reasoning in fields such as software engineering, law and finance. AfterQuery was part of the Winter 2025 batch.

Robotics and autonomous driving

• , the Shanghai-based operator of Hello鈥檚 robotaxi business, raised about $100 million at a valuation close to $3 billion. fund led the round. The financing to the 1-year-old company will support a 10,000-GPU computing cluster, AI model development and commercialization of Level 4 autonomous driving.
• , a Sunnyvale, California-based robotics perception company, raised a $165 million Series C at a $1.6 billion valuation, led by . Founded by former and engineers, the 5-year-old company combines custom chips, sensors and spatial software to help robots perceive their surroundings. The financing will accelerate deployments across logistics, manufacturing and inspection applications.

Health insurance

• , a San Francisco-based health benefits company, announced a $200 million Series C at a $2.7 billion valuation. led the financing. It also includes $400 million in secondary financing for existing investors. The 7-year-old company provides a benefits platform using AI to make healthcare more affordable for small and midsize employers. The company services 5,000 businesses.
• , a San Francisco-based health benefits platform, raised a $108 million Series C at a $1 billion valuation. Investors included , , and . The 5-year-old company helps employers provide individualized health budgets for employees to select their own medical plans.

Energy and EV infrastructure

• China-based , a battery-swapping and charging company, agreed to a strategic transaction that gives a 30% stake. Geely will contribute its Yiyi battery-swapping operation and a $95 million investment. The 9-year-old company was valued around $2.4 billion.
• , an Austin-based developer of off-grid power systems for AI data centers, raised a $120 million Series A at a $1 billion valuation, led by . The less than 1-year-old company builds modular systems for renewable energy and battery systems as data-center operators seek access to electricity.

AI applications

• raised $150 million led by , at a valuation just over $2 billion. The 3-year-old company, headquartered in London with offices in Los Angeles and Mumbai, built an enterprise audiovisual content platform for media, advertising, sports and healthcare customers. The financing will support research, commercial expansion and a larger Silicon Valley presence.
• , a New York-based AI ranking and personalization company, raised a $90 million Series B at a $1.44 billion valuation, led by . The 2-year-old company鈥檚 software helps enterprises respond to customer behavior in real time, improving the relevance of recommendations.

Cybersecurity

• Rome-based , a cybersecurity company protecting connected devices and autonomous machines, raised $270 million at a $1.7 billion valuation, led by . The financing to the 8-year-old company will support U.S. and Asia-Pacific expansion and development of AI security models.
• Tel Aviv-based , a consumer cybersecurity company, raised $40 million at a $1.1 billion valuation, bringing total funding to $167 million. co-founder joined existing investors including and , among others. The 8-year-old company protects consumers against phishing, scams, malicious websites and AI-generated fraud.

AI models

• Beijing-based , a language-model startup, raised $120 million reaching a valuation of $1.4 billion. Investors include , , and . The less than 1-year-old company raised $400 million across three rounds so far this year, before releasing its first public model.
• , a San Francisco-based developer of open-weight foundation models, announced a $150 million Series B led by , and , valuing the company above $1 billion. The financing will support the 3-year-old company鈥檚 Trinity models and products for customizing and operating them.

Manufacturing and construction software

• , an AI company for manufacturing with dual headquarters in Tokyo and Chicago, raised a $114 million Series D at a $1.2 billion valuation, bringing total funding to $234 million. Investors included and . The 9-year-old company鈥檚 platform organizes design and procurement and production; the financing will deepen its technology and accelerate North American expansion.
• , a Georgia-based construction software company, secured a growth investment of $250 million led by , valuing it above $1 billion. The 17-year-old company manages construction projects, with the investment supporting its AI capability build out and commercial growth.

Defense technology

• , a Washington, D.C.-based defense company, emerged from stealth with $250 million raised across three rounds at a valuation above $1 billion. The 2-year-old company develops long-range cruise missiles with manufacturing in Texas, Germany and Israel. Backers who have led across three rounds include , , , 1, and .

Food

• , the New York-based parent of consumer food brands David Protein and HallPass, raised a $250 million Series B at a $2.25 billion valuation. and co-led the round. The financing to the 2-year-old company will expand retail distribution, develop new products and support additional brands.

Related 萝莉视频 unicorn lists:

• (1,901)
• (666)
• (321)
• (198)
• (119)
• (102)
• (998)
• (547)
• (258)
• (39)
• (489)

Related reading:

• 萝莉视频 Data: Q3 2026 Posted A Record Count Of Billion-Dollar Rounds As The Global AI Race Heats Up
• 29 Companies Joined The 萝莉视频 In August, Led By AI Software And Semiconductors

Methodology

The 萝莉视频 萝莉视频 is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 萝莉视频 data. New companies are as they reach the $1 billion valuation mark as part of a funding round.

The unicorn board does not reflect internal company valuations 鈥� such as those set via a 409a process for employee stock options 鈥� as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter.

Funding to unicorn companies includes all private financings to companies that are tagged as unicorns, as well as those that have since graduated to .

Exits analyzed here only include the first time a company exits.

Please note that all funding values are given in U.S. dollars unless otherwise noted. 萝莉视频 converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to 萝莉视频 long after the event was announced, foreign currency transactions are converted at the historic spot price.

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Founded in 2016, Los Angeles-based Fifth Wall has grown into one of the largest investment firms focused on real estate technology, with roughly $3 billion in commitments and capital under management. Its portfolio includes , , and .

Notable exits include , which went public in 2024, and flexible workspace provider , which real estate giant of in January 2025 at a valuation of more than $800 million.

Major real estate and hospitality companies 鈥� including CBRE, , and 鈥� are among its investors, giving the startups it backs a direct connection to potential customers.

Wallace, who serves as CEO and chief investment officer, brings experience on both sides of that relationship. He worked in real estate finance at and before co-founding workforce analytics startup , which was acquired by in 2014, and ridesharing company .

As AI makes certain products easier to replicate, Wallace sees growing potential in startups that use technology to own and operate real estate themselves.

In a recent interview with 萝莉视频 News, we discussed that shift, why proptech funding totals may overlook some of the sector鈥檚 biggest investments, what it all means for founders, and the housing problems he believes still need solving.

This interview has been lightly edited for brevity and clarity.

萝莉视频 News: Our recent proptech funding analysis showed capital on pace to roughly match or slightly exceed last year, but across fewer deals. What does that tell you about the market?

Brendan Wallace: My sense is that statistic might belie the actual amount of funding flowing in, because what was previously characterized as proptech in the narrowest sense is probably representing a smaller share of where proptech investors are deploying capital.

There are increasingly horizontal solutions that are just as good, if not better, than many of the vertical solutions we鈥檙e seeing. We recently made a very big investment out of our growth fund into , the visualization software company. Most people reflexively think of that as being for movies or rendering scenes. But we saw real estate agents and commercial leasing brokers using it to represent spaces.

For an unbuilt home, you could feed it blueprints, have it build an entire home, put your family inside and walk through it in a full scene. That had surpassed the industry-specific solutions for space visualization that would have been narrowly defined as proptech.

Another category is investment flowing into full-stack businesses: real estate companies themselves. We just invested $135 million into , and I would be surprised if that fell under the definition of proptech.

Those are probably the two categories that might not be captured: horizontal software, with Higgsfield as an example, and businesses that reimagine physical space based on autonomy, AI or robotics. They鈥檙e actually building a physical thing. They鈥檙e building a real estate company, directly or indirectly.

How much is AI driving that shift toward software that serves multiple industries?

Wallace: It鈥檚 a big part of it. Without something profoundly better that you can鈥檛 replicate on your own, there鈥檚 less justification for buying outside software. There still is a justification, to be clear. There鈥檚 just less of one.

No real estate company can build the kind of visualization software Higgsfield has. I don鈥檛 believe they ever will. But a tenant engagement app they might have previously purchased and rolled out to their assets, they can now replicate over a long weekend.

The imperative to buy these third-party solutions has been reduced, and more value has been placed on the best horizontal solutions, whether that鈥檚 a foundation model or a specific use case like sound, audio or visual.

I don鈥檛 know the statistic, but it would be interesting to know how much real estate companies are spending on foundation models versus proptech applications. If I had to guess, we鈥檙e probably close to, or at, an inflection point where it鈥檚 passing it.

No one would characterize as a proptech company, but it might be taking a significant amount of the upstream proptech dollars, and an even larger share downstream once you go through the application layer.

Has AI reset the bar for what makes a venture-scale proptech software company? What does a startup need beyond adding AI to existing workflows?

Wallace: We鈥檙e still figuring that out, but I think it has to be something that can鈥檛 be replicated. Anything where the intellectual property moat is not extremely defensible is very challenged, and I think it will be for some time.

Simple application point solutions are becoming less financeable by venture investors because the capacity of one of the large labs to build that as an interface, or of a company to build it on its own, has caught up so dramatically.

The flip side is that bespoke AI development labs are now building solutions for the real estate industry. We backed one called , which is kind of like a for real estate. Antares works with a number of our strategic limited partners. They go in-house, identify a problem they think can be solved with an AI-based solution, build the intellectual property specifically for the organization, and leave it with them.

It鈥檚 more of a fee-for-development model than an intellectual property licensing model. You鈥檙e seeing that across industries, and you鈥檙e absolutely seeing it in real estate.

In some sectors, founders argue that domain expertise gives specialized AI companies an advantage over broad models. How durable do you think that advantage is?

Wallace: I鈥檓 a little more optimistic about foundation models’ and general intelligence’s capacity to adapt rapidly to specific use cases.

I wouldn鈥檛 argue that specific legal expertise, for example, is not valuable. But I think that moat collapses over time. You might be on a treadmill.

Think about what ChatGPT looked like in 2023 versus what Claude looks like today. At a basic level, it鈥檚 the same thing, but they鈥檙e totally different things. The sophistication, memory, capacity to be personalized, and ability to build off natural language instructions are unlike anything I think anyone could have anticipated in 2023.

What will that look like in 2029? I think what you鈥檙e saying is true today. It鈥檚 less true than it was, and it might become even less true in three years.

Is construction technology still an area of focus for Fifth Wall?

Wallace: It is, but it鈥檚 less of a focus today. When we look at our performance, it has generally favored two things: pure software businesses, whether marketplaces, consumer software or enterprise software, and full-stack businesses.

Anytime you get into physically doing things, the risk profile changes pretty profoundly. I don鈥檛 think it should be surprising that construction tech broadly is probably the biggest graveyard in all of venture. Maybe there鈥檚 a bigger category, but if there is, it鈥檚 not obvious to me.

Why do you think construction tech continues to attract significant funding?

Wallace: Because it鈥檚 still a massive market and a huge problem. If someone can bring greater efficiency to construction, you鈥檙e talking about one of the largest markets on earth. It鈥檚 such a massive prize that the largest funds are going to focus on it.

For founders raising seed or Series A funding, what opportunities would you encourage them to pursue that might not have been fundable a few years ago?

Wallace: You can actually build new real estate companies. That hasn鈥檛 really been conceptualized as being within a venture mandate, but it鈥檚 absolutely something we鈥檇 like to see.

For example, we have a better approach to buying multifamily assets, stripping out costs, and running more of the processes, operations and management with AI. That鈥檚 something I think is eminently fundable today by Fifth Wall and many venture funds. I don鈥檛 think that would have been the case five years ago.

I’d encourage founders to look at building full-stack, capital-R real estate businesses.

The second opportunity is around data. Real estate is characterized by extremely opaque data in a way that鈥檚 different from most large financial markets. Nothing that鈥檚 happened in proptech in the past 10 years has fundamentally changed that.

Where can generative AI provide signal through that opaque data to make capital allocation decisions? Real estate is generally a cash-flowing asset. We don鈥檛 necessarily think of it as comparable to bonds or stocks, but it is, and it should be.

Can you derive signal by looking at more data or different data, or use generative AI to predict what kind of real estate will perform better? The quantitative model that has existed for securities for a very long time now has the potential to affect real estate, because we have computational capacity that wasn鈥檛 previously possible.

Are there opportunities you鈥檙e interested in that have less to do with AI?

Wallace: We鈥檙e intrigued by the intersection of land, real estate and the financial instruments around them. To make most of what we describe as real estate, you usually have to build something on land. Land is an input to almost all real estate, but many real estate owners don鈥檛 want to own it or take on that risk profile.

Is there a way to decouple having to own land from owning and operating a building? That鈥檚 less about AI and more about financial engineering. There are new financial solutions emerging in that category that interest us.

How could AI, robotics and autonomy change the kinds of real estate businesses founders build?

Wallace: I鈥檓 interested in entrepreneurs who can conceptualize real estate through a sociological lens: New catalysts are changing how humans use space compared with the past.

That鈥檚 happened for a long time. E-commerce built the modern industrial real estate industry. Cloud computing, the internet and now AI built the data center industry. Cell towers were born out of mobile phones and smartphone technology. Genome sequencing helped create the lab-space real estate asset class.

I think we鈥檙e on the precipice of three new catalysts that will spawn entirely new real estate asset classes: AI, robotics and autonomy. In many ways, they鈥檙e entangled.

Do you expect fewer dedicated proptech venture firms? What would that mean for founders?

Wallace: I do think you鈥檙e going to see fewer vertical funds, in the narrowest sense of where we started this conversation. Dedicated venture capital funds investing in consumer and enterprise software focused on an industry 鈥斺�� I think you鈥檒l see less of that, and funding will probably continue to go down in that category.

The inverse is venture capital funding real estate businesses themselves. I think that鈥檚 about to explode.

If you were starting a company in this sector today, what problem would you most want to solve?

Wallace: Housing drives everything. Housing costs drive our economy, and the U.S. is grappling with a very real housing affordability crisis. It鈥檚 unclear how to solve it.

There are multiple approaches. One is building homes better, faster and cheaper 鈥� that鈥檚 construction tech. Another is financing homes at a lower cost. How do you bring efficiency to the housing finance market?

That鈥檚 a place where we鈥檝e made a lot of money and invested a lot, but I would argue it鈥檚 still unsolved. Getting a mortgage is still painful, archaic, slow and too expensive.

Related to that is making regulation more efficient to improve housing affordability. That鈥檚 not something an entrepreneur can solve alone, but there鈥檚 potential to bring efficiency to how cities deal with this problem.

There鈥檚 an intersection of regulatory technology, policy work and consulting. How do we make it so more people can afford homes in the cities they live in? That problem isn鈥檛 going away. Despite everything that鈥檚 happened, housing has gotten more expensive, not less.

Related 萝莉视频 queries:

Related reading:

• Sector Snapshot: Proptech Funding Holds Up, But Investors Are Placing Different Bets

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This is a weekly feature that runs down the week鈥檚 Top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

This week鈥檚 list of the largest U.S. startup funding rounds was pretty much all about AI. That includes the biggest financing 鈥� a $1 billion round for , a developer of AI assistants for everyday tasks 鈥� as well as most of the rest of the Top 10. Other large fundraisers include , which is deploying AI for the home rental sector, and , an AI cybersecurity startup.

1. , $1B, AI assistants: San Francisco-based Instinct, a startup building a personal AI assistant for everyday tasks, secured $1 billion in a Series C round. , and backed the financing, which set a $10 billion valuation for the year-old company.

2. , $350M, AI for housing: EliseAI, a startup deploying AI for the home rental industry, completed a $350 million funding round at a $4 billion valuation. and led the financing for the New York-based company, which is also expanding into healthcare.

3. , $255.5M, cybersecurity: Palo Alto, California-based Armadin, an AI-native cybersecurity company focused on autonomous security, raised $255.5 million in Series B funding. and led the round, which set a valuation of more than $2.5 billion for the year-old startup.

4. , $250M, project management:听 Kahua, a platform for enterprises to manage complex capital projects, picked up $250 million in growth funding from . The financing set a valuation of over $1 billion for the Alpharetta, Georgia-based company.

5. , $223M, AI infrastructure: Mountain View, California-based GMI Cloud, a provider of GPU cloud infrastructure used by AI teams to help train and run models, closed on $223 million in Series B funding as well as $445 million in debt financing. led the round, with participation from .

6. , $220M, foundational AI: General Intuition,a developer of AI models trained on videos and virtual worlds to develop skills for physical environments, secured $220 million in fresh funding. and were lead backers in the round, which set a $6.2 billion valuation for the New York-based company.

7. (tied) , $150M, physical AI: SiMa.ai, developer of a software platform for scaling physical AI applications, picked up $150 million in Series C financing at a $1.45 billion valuation. and led the round for the San Jose-based company.

7. (tied) , $150M, agentic app development: Supabase, provider of an AI-enabled open source Postgres platform for app developers, $150 million in new funding led by . The San Francisco-based startup also said it is acquiring database architecture startup .

9. , $145M, AI infrastructure: CScale, a developer of optical interconnect technology for AI data centers, emerged from stealth and says it secured $145 million in Series C funding.听 , , and led the round for the Palo Alto, California-based startup.

10. , $120M, real estate: Austin-based Homeward, a startup that helps homeowners buy before selling their existing homes or get cash offers for their properties, closed on $120 million in Series D equity funding led by . The 8-year-old company also secured $330 million in debt financing.

Methodology

We tracked the largest announced rounds in the 萝莉视频 database that were raised by U.S.-based companies for the period of Sept. 26-Oct. 2, 2026. Although most announced rounds are in the database, there may be a small time lag, as some rounds are reported late in the week.

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, an alternative investment firm specializing in asset-backed credit, led the round, which also included participation from , , , and as well as , , , and .

Homeward has now raised $360 million in equity since its 2018 inception. The company declined to disclose the valuation at which it raised the latest round, saying it was similar to its valuation at the time of its in 2021, when it was reported to be 鈥渏ust north of $800 million.鈥�

Austin-based Homeward has also secured a $330 million asset-backed debt facility to fund more home transactions. The equity funding will support expansion of its financing products and investment in its technology platform.

Its recent funding comes amid an uptick in startup investment to the proptech category this year. So far in 2026, global real estate-related startups have pulled in about $12.7 billion in seed- through growth-stage investment, per 萝莉视频 , putting this year on pace to top last year鈥檚 funding, which totaled $12.3 billion. Investment in the sector still pales compared to the peak years however: In 2019, the second-highest year on record after the 2021 venture funding spike, real estate-related startups raised $24 billion, 萝莉视频 data shows.

Cashing out before a sale

Homeward works through real estate agents to help clients access the equity tied up in their homes and navigate the timing of buying and selling. Its Buy Before You Sell program provides short-term financing so homeowners can purchase their next house before selling their current one, along with a guaranteed backup offer for the existing property.

For homeowners seeking a faster sale, its Sell Before You List program offers a cash purchase and a closing in a matter of weeks. Homeward then fixes up the property and resells it on the open market, returning the profit to the original homeowner while charging a program fee.

鈥淲e realized that there鈥檚 an opportunity to help homeowners sell their home fast without sacrificing all of their home equity like they would have to if they sold to an investor,鈥� founder and CEO told 萝莉视频 News in an interview.

The financing has helped Homeward roll out its cash-offer program across the 48 contiguous states, Heyl said. The company also plans to make its Buy Before You Sell program available nationwide by year-end.

The expansion follows a pivot that Heyl credits with helping Homeward more than quadruple revenue since 2021, even as U.S. home sales fell roughly 30%, according to his estimates.

鈥淥ur ability to really exponentially grow over the last four years or so was a huge thanks to the pivot,鈥� Heyl said, pointing to its Sell Before You List product, which is aimed at 鈥渢he home sellers that are still transacting.鈥�

Over the years, Homeward says it has partnered with more than 25,000 real estate agents and facilitated over $4 billion in transactions.

A pivot as homeowners stayed put

From 2019 through 2022, Homeward focused exclusively on helping homeowners buy their next house before selling their existing one. The offering appealed to agents and consumers competing in a tight housing market, and the company grew quickly, Heyl said.

But rapidly rising interest rates made that customer base harder to reach. Homeowners who might otherwise have moved into a larger or smaller house stayed put as moving costs rose.

鈥淚t became more expensive, definitely a lot more expensive, to move up, but even for a lot of people, more expensive to move down,鈥� Heyl said.

Homeward responded by broadening the types of sellers it could serve. While fewer people were buying and selling at the same time, some homeowners still needed to sell without buying another property. And with homes taking longer to sell and prices less predictable, Heyl saw a growing demand for cash offers.

In early 2023, Homeward launched Sell Before You List. The distinction, in Heyl鈥檚 view, is that Homeward offers a service to sellers rather than seeking to capture the upside from buying and reselling their homes.

鈥淥nce we fix the house up and sell it for its full price on the open market, we send that profit back to the original homeowner,鈥� he said.

Homeward also reworked its original Buy Before You Sell offering, lowering its cost and simplifying it for a market in which homes no longer routinely sell over a weekend with multiple offers. That business has contributed significantly to growth over the past year or so as more homeowners have begun making moves again, Heyl said.

鈥淢ost homeowners that are trying to move up or move down still plan to use the majority of their home equity to make that next purchase,鈥� he said.

Homeward also offers Buy With Cash, which lets buyers make cash-backed offers and refinance into a traditional mortgage after closing.

Direct-to-consumer marketing without the cost

The company鈥檚 distribution strategy centers on real estate agents. Homeward has partnered with more than 25,000 agents and facilitated more than $4 billion in real estate transactions, according to Heyl.

Rather than pursuing individual consumers, it seeks ongoing relationships with agents, teams and brokerages that can repeatedly bring clients to its programs.

鈥淲e don鈥檛 spend a dime going direct to consumer,鈥� he said. 鈥淲e don鈥檛 advertise. We don鈥檛 market.鈥�

Some agents incorporate Homeward鈥檚 offerings into their own marketing under their own brands, also known as white-labeling. Others turn to the company when a client鈥檚 existing home or access to equity keeps them from making a new purchase.

鈥淪ometimes it鈥檚 just unblocking the transaction,鈥� Heyl said.

That growth in a difficult market helped attract Saluda Grade, which is backing Homeward for the first time. , who runs the firm鈥檚 growth equity fund, said the team鈥檚 understanding of the problems facing buyers, sellers and agents stood out when compared with other 鈥渟imilarly situated鈥� operators it had met.

鈥淭hey really understood the core issues they were addressing,鈥� Stepp said. 鈥淚t was evident in their financial performance and their growth.鈥�

Geographic expansion was another draw, he said, with the capital raise creating an opportunity to bring Homeward鈥檚 products to more markets.

鈥淲e recognize the product-market fit, and how useful this product set really is to eliminate some of the friction in the home transaction process for consumers,鈥� Stepp told 萝莉视频 News in an interview, 鈥渁nd how useful of a tool it is for agents to be able to offer this.鈥�

Using AI to speed up underwriting

As many other startups, Homeward is incorporating AI into its workflows. For example, the company is using AI to reduce the manual work involved in processing transactions and underwriting properties.

Large language models extract information from documents and help underwriters review property videos, photos and inspection reports. It does this by pulling out relevant details about roofs, heating and cooling systems, and a home鈥檚 condition, according to Heyl.

鈥淎I has been huge for streamlining operations and underwriting,鈥� he said.

For Heyl, the broader opportunity is to continue addressing the financing and timing problems that complicate home purchases and sales, even as the housing market changes.

鈥淭here鈥檚 been a major pullback, but it hasn鈥檛 changed the opportunity that exists to solve problems for buyers and sellers,鈥� he said.

The company makes money in several ways. Buy Before You Sell carries a 1% program fee and a monthly interest cost. Sell Before You List carries a single program fee, with no monthly charge. Heyl did not specify the fee for that offering.

Homeward also generates revenue through its in-house mortgage and title businesses. Heyl said providing more services within the transaction can simplify the experience for buyers and sellers while reducing the program fee Homeward needs to charge.

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• Sector Snapshot: Proptech Funding Holds Up, But Investors Are Placing Different Bets
• Sector Snapshot: Real Estate Tech Funding Sees Slight Rebound, But Still Far Lower Than Peak Years

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She later became a general partner at , where she led early institutional investments in AI chipmaker , and later invested at . Now she鈥檚 founder and managing partner of , a $52 million fund backing startups that use AI to do work in industries such as construction, industrials and insurance.

In an interview with 萝莉视频 News, Venkatachalam discusses why she looks beyond familiar founder profiles, what makes an AI company durable, and how an early investment in Groq shaped her approach.

This interview has been edited for clarity and brevity.

You invested at Khosla Ventures before starting Axiom. What did you take from that experience, and what did you want to do differently?

Venkatachalam: One thing I took was 鈥檚 open view of where great founders can come from. Silicon Valley has gravitated toward a fairly narrow idea of who can build the next great AI company: Someone with a computer science or machine learning background, or experience at . We鈥檙e looking for more nonobvious founders, particularly in nonobvious industries.

The other thing was how we think about risk. Instead of asking a company every conceivable diligence question, we focus on the risks that matter for its next set of milestones. Can this team do what it says it will do? And if it can, could the result be massive?

That means accepting that many bets won鈥檛 work out while aiming for the outliers. I think that鈥檚 what my investors are backing me to do: identify categories of the future, rather than participate in the categories everyone already recognizes.

At Axiom, the biggest difference is that we鈥檝e built the firm around people who are actively working with AI. If you aren鈥檛 building, productizing, pricing or taking AI to market regularly, it鈥檚 very hard to keep up. Our team includes people doing exactly that in their other roles. They help keep our investment thinking current, and founders want to work with them because they鈥檝e encountered many of the same challenges.

We also use AI throughout the firm. We鈥檝e built what we call the Axiom Brain to help us make sense of market trends, identify interesting people and companies, and move faster on diligence and other work. For me, the value is the ability to act quickly.

You mentioned that some of those AI practitioners have other jobs. How does their role at Axiom work?

Venkatachalam: They have dedicated time to Axiom and work with us part time. They also receive carry in the fund. They鈥檙e partners in the work, rather than people whose names appear on an adviser list.

Their other jobs are central to the model. Some of the best angel investors are people who are still operating and building. I don鈥檛 need these people full time. In fact, they would be less valuable to Axiom if they left the work that keeps them close to the market.

Axiom says it invests in 鈥淎I for the real world.鈥� What does that mean when you鈥檙e evaluating a startup?

Venkatachalam: Our view is that AI should benefit a much broader population than the early adopters who are already using it. We look at industries underserved by technology, where AI can produce an outcome rather than simply provide another software tool.

That may lead us to construction, industrials or insurance. Some of those companies involve hardware, sensors or robotics; others are entirely software-based. What connects them is that they鈥檙e doing work that matters to customers in real-world industries.

We generally don鈥檛 invest in products that look like conventional enterprise software tools. We want to see AI delivering a result.

You鈥檝e described a shift from software people use to digital workers that perform jobs. Are customers actually paying for AI from labor budgets?

Venkatachalam: Yes, and that鈥檚 one of our investment criteria. Even when a portfolio company is at an alpha or design-partner stage, we do diligence to understand whether customers are willing to buy it that way. We鈥檙e often seeing contract values in the hundreds of thousands of dollars, rather than the much smaller contracts you might expect for a midmarket software tool.

We鈥檝e seen that buying behavior play out across the majority of the portfolio companies we鈥檝e invested in.

AI products are becoming faster to build and easier to imitate. What makes one durable enough to become a large company?

Venkatachalam: If you鈥檙e doing important work inside a customer鈥檚 business, and that work is worth a lot of money, you become difficult to replace. You鈥檙e handling what we call the last mile of the job.

In industrial settings, for example, delivering an outcome means integrating deeply with the systems customers use. You have to understand their data, train on it, learn the workflows that matter, and stand behind the result. That takes more than putting an interface on top of a model.

Those relationships and capabilities can become difficult for another startup to replicate. They also involve work that the large AI model companies may have little interest in doing themselves.

Before Axiom, you backed Groq when AI inference was far from an obvious investment category. What led you to it?

Venkatachalam: I came from both hardware and software. At one point, I became interested in why was building its own networking switches when it could buy them from existing suppliers. As I looked into that, I learned it was building its own chips, too.

That led me to , who had been involved in that work and had left to start Groq. I began learning why large technology companies were building chips to train models. Then Jonathan made the case that the much larger future market would be inference.

I鈥檒l be honest: In 2016, I barely understood inference. But if you believed these models would spread, it made sense that people would build on top of them and need the infrastructure to support that. That insight drove my investment.

How did that experience shape what you look for now?

Venkatachalam: It taught me the value of being a little early and having some patience. You don鈥檛 have to be wildly contrarian, but you do have to see the opportunity before it becomes obvious to everyone else.

In a way, our thesis hasn鈥檛 changed. We鈥檙e still asking what will be built on top of AI infrastructure and models. We want to invest while the answer is emerging, before there鈥檚 a consensus.

What happens when one of those early bets doesn鈥檛 work out?

Venkatachalam: We plan for it. We have a $52 million fund and expect to make 35 investments. We fully expect about half of them to fail, whether that means a company shuts down or simply never reaches the growth trajectory we鈥檙e looking for.

The model depends on finding an exceptional outcome. We need one outstanding investment to return the fund. If you invest early enough and the company becomes very large, that can offset many bets that didn鈥檛 work.

That willingness to accept losses is part of making investments before an opportunity is obvious. It鈥檚 built into how we approach the fund.

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From putting nuclear reactors on barges to grading how well AI models can control robots, this month鈥檚 crop of interesting startup deals takes AI and other emerging technologies well beyond the conventional software stack.

Other companies that caught our eye are applying automation to the decidedly old-school worlds of building-material procurement, commercial property maintenance and farm recordkeeping. Let鈥檚 take a closer look.

$50M to put nuclear power at sea

Nuclear power plants are famously difficult and time-consuming to build. thinks putting them on barges could offer another way.

The Long Beach, California-based startup raised what it says was an oversubscribed $50 million seed round led by , just two months after emerging from stealth with $10 million in pre-seed funding. The of investors in the deal included , , , and others.

Bluecore is developing compact, water-cooled small modular reactors designed to operate aboard floating barges. Rather than spending years constructing a new power plant and the accompanying infrastructure on land, the idea is to manufacture the systems and move them to where electricity is needed.

鈥淥ur focus is simple. Create and deliver zero-emission energy as safely and quickly as possible,鈥� CEO and founder wrote in a social media . 鈥淥ver 3 billion people live within an hour of water. We want to power them all.鈥�

Its first target is the Port of Long Beach, with other ports and power-hungry AI data centers among the potential customers. The company says it鈥檚 working with the and as it pursues certification.

Bluecore鈥檚 raise comes amid a broader nuclear funding boom. Nuclear fission startups alone pulled in roughly $2 billion in venture funding in 2025, per 萝莉视频 data, and investors have continued writing enormous checks this year. More broadly, cleantech-, EV- and sustainability-focused startups raised about $15 billion in the first half of 2026, with second-quarter funding reaching its highest quarterly level since 2024.

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$31M to bring AI to the building-materials business

Ordering cement and steel might not sound like an obvious AI use case, but Saudi Arabia-based sees a lot of room for improvement.

The Riyadh startup last week said it had secured $31 million in new capital, consisting of a $13 million Series B equity round co-led by , 鈥檚 venture arm, and , plus an $18 million growth-debt commitment from under a previously announced facility. The company has now raised more than $83 million, .

BRKZ operates a marketplace that connects construction companies with suppliers of building materials, while also handling sourcing, logistics and financing. More interestingly, it says it has amassed some 38 million structured data points that power an AI pricing engine trained on roughly 40,000 requests for quotes.

The company says 84% to 89% of its predicted prices come within 5% of the eventual transaction price. Another AI agent reads photos of cement delivery notes sent through , matches them to orders and verifies deliveries 鈥� with roughly three-quarters processed without human intervention.

BRKZ is riding a in Saudi Arabia even as startup investors remain selective about construction and property technology more broadly. Global proptech startups raised about $6.5 billion via roughly 640 deals in the first half 2026, 萝莉视频 data shows. That’s on pace to top last year鈥檚 dollar figures, even as deal count has dipped this year. Similar to other startup sectors, investors in proptech are increasingly directing capital toward companies using AI and automation to cut costs and streamline operations.

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$24M for robots to take care of commercial properties

Robots are already assembling cars and moving packages around warehouses. wants them to also mow lawns and sweep parking lots 鈥斕齛nd do security patrols of those properties while they鈥檙e at it.

The Santa Clara, California-based autonomous robotics startup last week said it has raised a $24 million Series A led by to scale its fleet of robots designed to handle outdoor property maintenance for commercial real estate owners and operators.

Its machines combine autonomous navigation with attachments that allow them to perform jobs like sweeping, debris removal and landscaping tasks 鈥� all while conducting 鈥渟oft security鈥� 鈥� across large campuses and commercial properties.

Rather than trying to build a general-purpose humanoid robot, Viabot is applying autonomy to repetitive jobs that property owners already pay people and contractors to perform. The startup, which operates on a 鈥渞obot as a service model,鈥� sees an opportunity to fill a labor shortage for what鈥檚 often considered 鈥渄irty, dull and dangerous鈥� outdoor work, , founding partner at , told 萝莉视频 News in 2021, when the company raised earlier funding.

Startup investors are pouring money into those sorts of real-world AI applications. Global venture funding to physical AI companies 鈥� including robotics, autonomous vehicles, aerospace, drones, industrial automation and sensors 鈥� reached $47.4 billion across 521 deals in the first half of 2026, 萝莉视频 data shows. That’s nearly 4x the $12 billion invested in the second half of 2025 and almost 80% above the year-ago period.

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$10M to give AI robots an independent report card

As frontier AI models move from controlling software to controlling robots and machines in the real world, wants the public to have an independent way to understand what they can actually do.

The 3-month-old San Francisco startup announced last week that it raised a $10 million seed round led by , with participation from , , and others.

Robocurve says it wants to act as an independent third-party auditor for physical AI, testing how well frontier models can control real robots and publicly reporting the results. The company says its own research has already found that general-purpose large language models can outperform specialized robotics vision-language-action models on some simple tasks.

Importantly, Robocurve isn’t positioning itself as a conventional robotics benchmarking startup. It is incorporated as a Public Benefit Corporation with a legal duty to independently evaluate the robotics capabilities of frontier AI systems and report those findings to the public.

The company says AI labs don’t dictate its research agenda, evaluation methodology or published results, and it plans to work with governments, policymakers and civil society as robot capabilities advance.

Academia is a big part of that model, too. Rather than developing every benchmark itself, Robocurve funds academic teams and supplies them with robot hardware to create open-source benchmarks. More than 200 institutions 鈥� including researchers from 19 of the world’s top 20 universities, according to the company 鈥� have signed up for its benchmarking program. Robocurve is offering a combined $500,000 in funding plus free robotic arms to participating academic groups.

Its funding is timely given the massive influx of capital pouring into robotics and physical AI. Within the broader physical AI sector, robotics startups alone raised more than $21 billion globally in the first half of 2026, 萝莉视频 data shows, already eclipsing the nearly $16 billion raised in all of 2025 and even the $15.3 billion invested during the venture market’s 2021 peak.

As ever more powerful models move from screens into machines capable of manipulating the physical world, figuring out what those models can 鈥� and can’t 鈥� safely do becomes a more consequential problem.

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$5M to let farmers talk instead of type

A lot of agricultural software has one basic problem: Farmers don鈥檛 spend their days sitting at desks.

With that in mind, this month raised a $5 million pre-seed round led by for a voice-first AI platform designed specifically for farmers, agronomists and other agricultural workers.

Instead of asking someone working in a vineyard or almond orchard to stop and fill out a form or type notes into a computer, Tellia enables them to leave a voice note, send a message, or even submit a photo to log records, generate reports and set reminders. It says its AI then turns that unstructured information into records associated with the correct field, crop and crew.

For example, for a livestock farmer that might mean a voice prompt like: 鈥淭ellia, the vet just checked Herd 3. All clear, next health check due in 6 weeks, log that.鈥�

Or a vineyard manager might ask: 鈥淭ellia, based on this year’s Brix and pH logs, what’s the projected alcohol level for the Cabernet lot?鈥� and receive an instant answer based on previously collected data.

San Francisco- and Paris-based Tellia was founded last year and says its technology is already deployed across 1 million acres, including at and wineries in the U.S., as well as agricultural organizations in Europe.

and also participated in its latest funding.

Its raise comes amid a much tougher environment for agtech startups overall. Venture investment in agriculture and farming remains in a correction from its 2021 peak, when startups in the space raised $10.5 billion across more than 1,400 deals, 萝莉视频 data shows. That makes companies applying increasingly cheap and accessible AI to specific, everyday farming problems an interesting corner to watch. Voice AI, in particular, has emerged as one of the hot spots in artificial intelligence funding in recent years.

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Related reading:

• Sector Snapshot: Robotics Startups On Fire As Venture Funding Surges To Record Numbers In 2026
• VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape
• Sector Snapshot: Agtech Startups Face A Drier Funding Climate
• Why Big Investors Are All Ears For Voice AI Startups

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But startup investors haven鈥檛 abandoned the sector, either, 萝莉视频 data shows. Instead, they鈥檙e being more selective about their bets and putting more money into companies using AI and other technology to make construction, property operations and real estate transactions faster and less expensive.

That shift shows up in both the year鈥檚 largest funding rounds and biggest acquisitions 鈥� and, notably, much of the biggest funding activity is happening outside the U.S.

The broad trend: Even before the pandemic-fueled funding peaks, proptech startups received more than double the venture funding in 2019 than in more recent years. While investors haven鈥檛 given up on proptech, funding to startups in the space remains down as interest rates hover in the .

In case you forgot, during the COVID-19 pandemic, home buyers and owners had access to 15-year mortgage interest rates as low as 2.5%. Those historically low interest rates fueled investor interest in the space, especially in the U.S.

Today, venture investors are backing startups working in areas such as AI-driven construction, property operations, underwriting and transaction infrastructure with demonstrable ROI. At the same time, more generic real estate software and later-stage companies without exceptional growth face significant funding challenges, our data shows.

And interestingly, four of the five largest deals in 2026 to date took place outside the United States.

The numbers: So far in 2026, global real estate-related startups have pulled in about $8.7 billion in seed- through growth-stage financing, per 萝莉视频 . That compares to $24 billion in 2019, the second-highest year on record after the 2021 venture funding spike. It also compares to $12.3 billion raised in 2025. It appears that with four months left in the year, proptech funding is on pace to roughly match or slightly exceed 2025 levels.

Deal count is also down fairly significantly, with 794 deals so far this year. For context, the space saw more than 2,400 deals in 2019. Last year, the sector notched 1,446 transactions. The lower deal count signals both potentially decreased investor interest in the space and larger round sizes.

Noteworthy deals

The three largest deals in the proptech space so far took place in Europe, and two of those top deals involved companies doing work with steel.

Stockholm-based , a green steel startup, landed the largest haul in a private equity deal led by , also of Sweden. In June, the 6-year-old company raised about $1.6 billion in a transaction that made Wallenberg its majority owner.

In August, of Madrid raised $695 million in a venture round led by another Madrid-based company, , for its own green steel plant. The 3-year-old startup raised the money at a $3.1 billion valuation.

And in January, Amsterdam-based , a cloud-native hospitality management system, closed a $300 million Series D funding round at a $2.5 billion valuation. London鈥檚 led the financing for the 14-year-old company.

The only U.S. company to crack the top five when it comes to the largest deals was San Francisco-based autonomous construction tech startup , which raised $270 million in a Series B funding round in February. The financing, co-led by and , brought Bedrock鈥檚 total funding to over $350 million and valued the company at $1.75 billion.

Montreal-based AI-powered digital mortgage startup rounds out the list with a $216 million Series E raised in June at a $1.47 billion valuation.

Exits

There have been some meaningful proptech exits in 2026, although the activity is much stronger in M&A than in IPOs.

The only known significant initial public offering in the space was conducted in January by Columbia, Missouri-based , a construction-equipment rental company with a jobsite technology platform. EquipmentShare raised about $747 million in primary proceeds by pricing 30.5 million shares at $24.50. Including shares sold by existing holders, the offering totaled approximately $859 million.

Real estate-related startup M&A, however, has been robust in 2026 so far, with several of the largest transactions involving brokerage consolidation. Overall, the broad acquisition trend is centered around incumbents buying data, workflow ownership and distribution so they can build credible AI products more quickly.

The largest deal in the proptech space was $3.6 billion cash purchase of , which operated an AI-powered equipment maintenance and asset management platform, announced in May. (MaintainX had seen its valuation jump to $2.5 billion in 2025 after a $150 million Series D raise.)

There were several other large acquisitions.

• In January, completed its acquisition of in an all-stock $1.6 billion transaction that made it 鈥渢he world鈥檚 largest brokerage,鈥� according to .
• Construction tech giant announced in July that it was acquiring , a provider of aerial and ground-based reality-capture software for construction and other industries, for $845 million in cash. In a smaller deal, Procore also picked up construction AI-agent platform .
• Commercial real estate giant in August completed its $800 million cash purchase of , a housing-market data and technology provider for the homebuilding industry.
• And also in August, officially completed its $880 million acquisition of , forming a new parent entity named the Real REMAX Group.

The AI effect

AI is starting to move from the testing stage into everyday use across real estate and construction, according to a from and titled 鈥淧roptech鈥檚 Impact on Real Estate Innovation and Transformation.鈥�

The report says companies are using it to cut costs, make better decisions, and handle routine work more efficiently. Meanwhile, proptech is expanding beyond property-management software into areas such as construction, energy, infrastructure and climate technology.

Overall, proptech funding remains far below its pandemic-era highs, but the types of companies attracting money are evolving. Investors and buyers tend to favor businesses that can show they save customers time or money, particularly in construction, building operations and real estate finance. As such, the proptech sector increasingly includes companies that look quite different from those funded in years past.

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Related reading:

• Sector Snapshot: Real Estate Tech Funding Sees Slight Rebound, But Still Far Lower Than Peak Years
• Steel Startups Forge Ahead With More Funding
• From Batteries To Building Materials, Green Manufacturing Startups Are Raising Billions

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This month鈥檚 installment of this column is all AI, though applications for the technology range widely, from two startups that apply AI to trash or recycling, to another that promises to help people breathe and sleep better, to a company that says its AI can help architects and builders spot commercial projects before they鈥檙e even announced. Let鈥檚 jump in.

$27M to help recycling plants see what鈥檚 in trash

For decades, the recycling industry has relied on sampling and educated guesses to understand what moves through its facilities. But wants every discarded bottle, carton and wrapper to become data.

The London-based startup said last month that it has raised a 拢20.3 million ($27 million) Series B led by technology investor . The company installs AI-powered camera systems above conveyor belts in recycling plants, then uses computer vision to identify materials, products and brands in real time. Greyparrot says that data helps operators recover more valuable materials, improve sorting efficiency and comply with increasingly strict recycling regulations.

Its systems are now deployed in more than 20 countries and have analyzed more than 1 trillion waste objects, per the company. It counts large waste-processing companies such as and among its customers.

The data gathered at plants also feeds Greyparrot鈥檚 Deepnest platform, which it says consumer brands including , and use to understand what happens to their packaging after consumers throw it away, helping to inform redesigns and comply with Extended Producer Responsibility rules in places such as Canada and the EU.

The fresh funding will help expand the company’s footprint across North America and Europe and support its goal of preventing more than 1 million tons of waste by 2030.

The raise reflects growing investor interest in applying AI in the physical world rather than behind computer screens. Companies in the physical AI sector raised nearly $47.3 billion in the first half of 2026, 萝莉视频 data shows, up nearly 80% year over year, as startups increasingly apply artificial intelligence to settings such as factories, recycling plants and other 3D environments.

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$17M to help robots navigate where GPS can’t

For a recently funded robotics company, the next frontier for physical AI is underground: in mines, tunnels and other places where GPS doesn’t work.

Australian startup said last month that it secured $17 million in new funding. That includes a $10 million equity round backed by , , , and as well as a $7 million venture debt facility from the country鈥檚 National Reconstruction Fund Corp. The company plans to scale manufacturing and expand its AI autonomy and cloud mapping platforms.

Emesent is best known for Hovermap, a LiDAR scanning payload that mounts to drones, vehicles or backpacks to create detailed 3D maps of mines, industrial sites and other hazardous environments. But increasingly, the company’s focus is software. Its Cortex AI platform enables robots to navigate autonomously in environments without GPS, while its Aura cloud platform processes and analyzes the resulting spatial data.

The company says its technology is already deployed at more than 200 mine sites worldwide and that it is expanding into the defense, critical infrastructure and construction sectors.

Its raise is another example of increased interest and investment in physical AI. As industries grapple with labor shortages and increasingly dangerous operating environments, startups that combine robotics, computer vision and autonomy are attracting fresh capital to automate work that’s difficult, dirty or unsafe for humans.

Robotics investment funding overall has been on a tear in recent quarters. Startups in the category raised $15 billion globally in 2025 鈥� an annual record that has already been eclipsed partway through 2026 鈥斕芾蚴悠� data shows.

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$12.25M for AI that treats congestion with sound

We鈥檝e covered AI that can do dirty work like help sort through trash or navigate underground mines. What about AI to help people breathe?

San Francisco-based medtech startup recently raised an oversubscribed $12.25 million Series A led by . The company develops FDA-cleared, noninvasive devices that it says use AI and acoustic resonance therapy to treat congestion and improve sleep without drugs.

The company said its flagship Sonu band personalizes sound waves based on a user’s facial anatomy to open nasal passages, while its newer Spatial Sleep device aims to help users fall asleep faster and stay asleep longer.

The raise comes as investors continue to back AI-powered medical devices that combine software with regulated hardware. Companies that intersect 萝莉视频鈥檚 AI and medical devices industries raised more than $629 million in the first half of this year, our data shows, up about 33% year over year.

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$3.85M to turn unrecyclable trash into fuel

Trash and recycling emerged as an unexpected theme in this month鈥檚 column. While Greyparrot helps companies better understand what鈥檚 in landfills and recycling plants, another recently funded company, , says it鈥檚 working to turn unrecyclable garbage into industrial fuel.

The Las Vegas-based company last month announced a $3.85 million round co-led by and to commercialize technology that converts hard-to-recycle plastics and other waste into industrial fuel. The startup says its engineered fuel can replace coal in cement, steel and other heavy industries without requiring factories to modify existing equipment. The new funding will help it build its first commercial U.S. biofuel facility outside Las Vegas.

Global venture investment into cleantech-related startups has been steady but not record-breaking in recent years, 萝莉视频 data shows. Around $15 billion went into rounds for companies in 萝莉视频鈥檚 cleantech-, EV- and sustainability-focused categories in the first half of 2026, putting this year鈥檚 funding on track to slightly exceed the 2025 tally, which was the lowest in several years.

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$3.5M to predict construction projects before they’re announced

The biggest construction opportunities often surface months before the first request for proposals. promises to use AI to spot them first.

The New York-based startup last month raised $3.5 million in seed funding from 鈥檚 accelerator, , and others to build an AI platform for architecture, engineering and construction firms. The startup promises to give such companies an edge over their competitors by helping them discover projects earlier and identify the best path to winning them. Instead of searching public bid databases, Cascade says its tech can analyze signals such as bond filings, property transactions, capital budgets and meeting minutes to identify projects while they’re still taking shape.

Overall funding to real estate-related startups has trended higher in recent quarters, and the sector emerged as a bright spot for seed funding in the first half of 2026, an analysis of 萝莉视频 data shows. Other seed-funded real estate startups this year have spanned areas ranging from streamlining planning and building processes to real estate investing to reducing power consumption in buildings.

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Correction: The article was updated to reflect Forge Industries’ correct headquarters location.

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Before founding , an AI startup for the construction industry, spent more than a decade working in construction. She grew up in a low-income household in Austria, where her father was a carpenter. When the family needed extra money he would take his children to job sites, and Buchner began working as a carpenter as a teenager.

鈥淚鈥檓 a blue-collar worker by background,鈥� she explained.

Buchner never really left the industry. She worked her way up and eventually became a general contractor, managing large construction projects in Europe. But her career took a turn after a worker died on one of the sites she was running.

Experiencing the fatality changed the way she thought about her work.

鈥淥nce you realize that people die under your management, you just look at life differently,鈥� Buchner said.

She decided to build a health and safety app for construction workers. That company was separate from (and before) Trunk Tools, but the project pulled Buchner into software and eventually led her to pursue a Ph.D. focused on data science in construction and early artificial intelligence.

While doing that research, she realized something that would later become central to Trunk Tools: Construction companies produce huge amounts of information, but much of it is fragmented across different systems and buried in documents, making it difficult to analyze.

鈥淭he data was there,鈥� Buchner said, but it was 鈥渟o messy and so unstructured鈥� that traditional data analytics could only go so far.

Turning construction data into an AI business

Buchner saw an opportunity to make that information more useful. She also decided she wanted to build the company in the U.S. rather than Europe.

In 2019, she moved to California to attend . She founded Trunk Tools in 2021 while she was still in business school, graduating in 2022. The early years were messy, as they often are for startups, and Buchner said the company did not really find its first meaningful product and market until 2023.

By then, advances in large language models were beginning to make it more practical to work with large quantities of unstructured information.

That was particularly relevant in construction. Buchner estimates that an average construction site can involve 3 million to 4 million pages of documentation spread across numerous systems. Trunk Tools integrates with those systems and creates a layer on top that can analyze the information and use it to carry out various tasks.

The company sells primarily to general contractors and subcontractors, and has also begun working with some project owners.

Trunk Tools鈥� AI agents can review contracts and construction drawings, flag inconsistencies and help with specifications, submittals and bidding, and pass that information among different workflows. For example, one agent can uncover an issue in a drawing and give that information to another agent to figure out next steps and how any changes might affect other parts of a project.

Such an ability to connect information across a project is important because construction projects are unusually complex, Buchner said.

鈥淔or a human brain, it鈥檚 impossible to calculate the second- and third-order effects of changes because of how complex and connected it is,鈥� she said.

In one case, Buchner said, a project owner requested a change that a construction company might otherwise have simply begun implementing. Trunk Tools calculated that the change would add nearly $4 million to a roughly $100 million project. Once the contractor presented the cost to the owner, the owner decided not to proceed.

In another instance, a worker got chemicals in their eye, and someone nearby used a Trunk Tools agent to ask what to do. The system returned step-by-step instructions for responding to the exposure.

鈥淲ith stuff like that, you have to act fast,鈥� Buchner said, calling it a particularly meaningful example for the company.

From early product to rapid growth

New York-based Trunk Tools now has more than 100 employees. Buchner is a solo founder and said her Ph.D. gave her enough of a technical background that she did not feel she needed to seek out a technical co-founder. She did, however, hire experienced AI engineers early and has since built out a larger technical leadership team.

The startup has also expanded the number of products it offers. Buchner said Trunk Tools had two live AI agents about a year ago and now has more than 10. The pace of development has become fast enough, she said, that the company sometimes struggles to train its own sales and customer-success teams on new products as quickly as they are being released.

The company has raised about $70 million in funding from investors such as , and . Revenue grew fourfold last year and is on track to grow roughly 3.5x this year, according to Buchner. Its most recent funding round was a $40 million Series B in 2025 that valued Trunk Tools at $325 million.

An unconventional founder advantage

Fundraising was more difficult at the very beginning, Buchner shared, in part because she did not fit the mold investors often associate with startup founders.

鈥淚 think it was hard very early on because I might not look like the traditional tech founder,鈥� she said.

But after Trunk Tools gained traction, Buchner believes her industry background became an advantage. She had spent years doing the work her customers do and could answer detailed questions about construction in investor meetings.

鈥淚 walk into a room full of investors, and I know that 鈥� I鈥檓 definitely the smartest person in the room when it comes to my industry,鈥� she said.

Bringing AI to a tech-wary industry

Historically, the construction sector has been slower than others such as finance and legal services to adopt new software or technologies. One reason for this, Buchner believes, is that there is often a long distance between the person buying a product and the person expected to use it.

Executives may make purchasing decisions while sitting in an office. Meanwhile, the actual users are often out in the field working on job sites and under constant pressure to keep projects moving. Contractors also typically operate on relatively thin margins. So if a bad decision is made around purchasing technology, the potential cost is greater.

Still, Buchner believes that AI is being adopted faster than other technologies she has seen enter the construction industry.

Over time, Trunk Tools has also learned that simply selling the software is not always enough. The company now provides training and change-management services for customers trying to roll out AI across large organizations.

It鈥檚 another area where having an industry background makes a difference, in Buchner鈥檚 view. Rather than sticking to a model of what a conventional software business is supposed to look like, she said, she is more focused on what construction companies actually need.

鈥淚鈥檓 just thinking about solving a problem,鈥� she said.

Looking back, Buchner said she always expected to start a business someday. For years, she assumed it would be her own construction company.

Instead, the carpenter-turned-contractor ended up building a software company for the industry she had already spent much of her career working in.

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