The company is known for its Orbital AI platform, which is tailored for the energy sector. Photo via solutions.kbr.com

London-based AI firm Applied Computing has announced a $20 million Series A round and a new office in Houston.

The new Bayou City office is Applied Computing’s first in the United States and part of its North American expansion. The company is known for its Orbital AI platform, which is tailored for energy operations.

The funding round was led by Houston-based KBR Inc., with participation from San Francisco-based Databricks Ventures. KBR’s investment was first announced in March.

KBR and Applied Computing have also entered into a multi-year agreement to deliver exclusive AI products for the energy sector. KBR already has integrated Orbital into its INSITE 3.0 platform for energy projects, and is also using the product for ammonia production.

Applied Computing’s Orbital platform combines physics-grounded intelligence with models across chemical engineering, time-series forecasting and language, according to the company. The system analyzes sensor readings and can recognize a facility’s equipment constraints and operator activity. The platform can also allow technicians to run simulations of how a change to a facility could affect the rest of its operations.

According to TechCrunch, Applied Computing will use the $20 million to further explore projects and deployments with the energy sector, hire engineering and research positions, and continue to expand internationally, potentially into the Middle East.

The company is also working on deals with a major U.S. stream operator, TechCrunch reports. And Applied Computing shared on LinkedIn that it plans to announce its first partnership with a major European oil company in the coming weeks.

“Yesterday we showed Orbital live in deployments at our demo day at the Energy Institute in London,” Callum Adamson, CEO and co-founder of Applied Computing, posted on LinkedIn on July 16. “Today, we're announcing the capital to scale it globally as well as the launch of our new offices in Houston and Bangalore. In the weeks following, there will be more announcements on our progress, partnerships and deployments.”

The company opened its Bangalore offices in December.

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This article originally appeared on our sister site, InnovationMap.com.

Scotty Nyquist discuss the growth in AI data centers and the strain on the system. Photo via HARC report

Houston energy expert asks: Who pays when AI outruns the power grid?

Guets Column

For most of the past 20 years, U.S. electricity policy relied on predictable trends in demand. Electricity use, in most regions, increased gradually, forecasts were stable, and utilities adjusted the system in small steps. Power plants, transmission lines, and substations were generally added to reflect shifts in load, rather than growth, and costs were recovered through modest adjustments to customer bills.

Growth in AI data centers has disrupted this model. A single facility can add as much electricity demand as a small town. That demand comes all at once, runs continuously, and has little tolerance for outages. If electricity service drops even briefly, computation stops, and services shut down. Ironically, data centers need reliable service, a point that their emergence is driving concern around for the rest of the grid.

What the numbers say

The International Energy Agency projects global electricity consumption from data centers to double by 2030, reaching roughly 945 TWh, nearly 3 percent of global electricity demand, with consumption growing about 15 percent per year this decade. McKinsey projects that U.S. data center demand alone could grow 20–25 percent per year, with global capacity demand more than tripling by 2030.

After years of roughly 0.5 percent annual demand growth, many forecasts now place total U.S. electricity demand growth closer to 2–3 percent per year through the mid-2030s, with much higher growth in specific regions. In Texas, some forecasters are saying electricity demand could double over the next five years, a staggering 10 percent per year growth rate. What sounds incremental on paper translates into a major challenge on the ground. Meeting this pace of growth is estimated to require $250–$300 billion per year in grid investment, about double what the system has been absorbing.

Where the system starts to strain

The strain appears first in the interconnection queue. It shows up as long waits, backlogs, and delays for connecting new loads and new generation.

Before new generators or large load customers can be connected, a study is required to assess their impact on the grid, whether it can physically handle the added load, and whether upgrades are required. With AI-driven data centers, utilities face far more connection requests than they can realistically support. In ERCOT, large-load interconnection requests exceed 200 gigawatts, most tied to data centers. That amount exceeds historical norms, and it is several times larger than what can be practically studied or built in the near term.

To be clear, public utility commissions are required to study these requests because they must manage system capabilities to ensure minimal disruption. This means engineers spend time evaluating projects that may never be built, while other more commercially viable projects may wait longer for approvals. This extends timelines and makes infrastructure planning less reliable.

Why policymakers are rethinking the rules

Utilities and their regulators must decide how much generation, transmission, and substation capacity to build years before it comes online. Those decisions are based on expected demand at the time projects are approved. When it comes to data centers, by the time infrastructure is completed, they may end up deploying newer, more efficient chips that use less power than originally assumed. This can result in grid infrastructure built for a higher load than what actually materializes, leaving excess capacity that still must be paid for through system-wide rates.

That’s the central dilemma. If utilities build too little capacity, the system operates with less reserve margin. During periods of grid stress, operators have fewer options, increasing the likelihood of curtailments or outages. However, if utilities build too much, customers may be asked to pay for infrastructure that is not fully used.

In response, policymakers are adjusting the rules. In some regions, regulators are moving toward bring-your-own-power approaches that require large data centers to supply or fund part of the capacity needed to serve them or reduce demand during system stress. At the federal level, permitting reforms tied to datacenter infrastructure increasingly treat electricity as a strategic economic input.

As Ken Medlock, senior director at the Baker Institute Center for Energy Studies (CES), explains:

“Many of the planned data centers are now also adding behind-the-meter options to their development plans because they do not anticipate being able to manage their needs solely from the grid, and they certainly cannot do so with only intermittent power sources.”

Behind-the-meter (BTM) refers to power that a consumer controls on its side of the utility meter, such as on-site gas generation or a dedicated power plant. These resources allow data centers to keep operating during grid-related service. Most facilities remain connected to the grid, but the backup BTM generation serves as insurance for operating their core business.

This shifts responsibility. Utilities traditionally manage reliability across all customers by maintaining an operating reserve margin, or spare capacity. Increasingly, large-load customers manage part of their own electricity reliability needs, which changes how infrastructure is planned and how risk is distributed.

Bottom line

AI-driven load growth is arriving faster and in more concentrated places than the power system was built to accommodate. Utilities and regulators are being forced to make decisions sooner than planned about where to build, how fast to build, and which customers get priority when capacity is limited. The effects extend beyond data centers, showing up in system costs, reliability margins, competition for grid access, and pressure on communities and industries that depend on affordable and dependable power. The issue is not whether electricity can be generated, but how the costs and risks of rapid demand growth are distributed as the system tries to keep up. How regulators balance these decisions will determine who pays as AI demand outruns the power grid.

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Scott Nyquist is a senior advisor at McKinsey & Company and vice chairman, Houston Energy Transition Initiative of the Greater Houston Partnership. The views expressed herein are Nyquist's own and not those of McKinsey & Company or of the Greater Houston Partnership. This article originally appeared on LinkedIn.

Collide has rolled out RIGGS, a large language model for energy professionals. Photo via Getty Images

Houston AI startup rolls out platform to reshape oil and gas workflows

AI for energy

Houston-based Collide is looking to solve AI issues in the energy industry from within.

Co-founded by former oil roughneck Collin McLelland, the company has developed AI software for operators and field teams, shaped by firsthand oilfield experience. Its AI-native platform “retrieves and synthesizes data from authoritative sources to deliver accurate, cited, and energy-focused insights to oil and gas professionals,” according to the company.

“Oil and gas has a graveyard full of technology that was technically impressive and operationally useless,” McLelland tells Energy Capital. “The reason is almost always the same: the people who built it didn't understand what they were actually solving for. When you're an outsider, you see workflows and try to automate them. When you're an insider, you understand why those workflows exist—the regulatory constraints, the physical realities, the liability concerns, the trust dynamics between operators and service companies.”

Collide’s large language model, known as RIGGS, performed well in recent benchmarking results when taking a standardized petroleum engineering (SPE) exam, the company reports. The exam assesses understanding from conceptual terminology to complex mathematical problem-solving.

According to Collide, RIGGS achieved a score of 67.5 percent on a 40-question subset of the SPE petroleum engineering exam, outperforming other large language models like Grok 4 (62.5 percent), Claude Sonnet 4.5 (52.5 percent) and GPT 5.1 (4 percent).

RIGGS completed the test in 15 minutes, while Grok took two hours. Collide hopes over the next few months, RIGGS will receive a score between 75 percent to 80 percent accuracy.

The software could potentially help oil and gas companies produce accurate outputs and automate trivial workflows, which can open up valuable time for engineers and teams to work on other pressing matters, according to McLelland.

“Collide exists because we sat in those seats — we were the engineers, the operators, the field guys,” he says. ”RIGGS scoring higher on the PE exam versus the frontier labs isn't a party trick. It's evidence that the model understands petroleum engineering the way a petroleum engineer does, because it was built by people who do.”

RIGGS was trained on Collide’s Spindletop hardware and is supported by a vast library of information, as well as a reasoning engine and validation layer that uses logic to solve problems.

“Longer term, we see RIGGS as the intelligence layer that sits underneath every operator's workflow — not a chatbot you open in a browser, but something embedded in the tools engineers already use,” McLelland says. “The goal is to give every engineer the knowledge and pattern recognition of a 30-year veteran, on demand."

According to McLelland, Collide is already building toward reservoir analysis and production optimization, automated regulatory compliance (Railroad Commission filings, W-10s, G-10s), workover report generation, and engineering decision support in the field for near-term use cases. In March, Collide and Texas-based oil and gas operator Winn Resources announced a collaboration to automate the time-intensive process of filing monthly W-10 and G-10 forms with the Texas Railroad Commission, completing what’s normally a multi-hour task in under 30 minutes. Collide reports that Winn’s infrastructure now automates regulatory filings and provides real-time visibility into data gaps, which has reduced processing time by over 95 percent.

“Before Collide, I'd spend hours manually keying in filings,” Buck Crum, director of operations, said in a news release. “(In March), we had 50 wells to file and I was done in 20 minutes. It does the majority of the heavy lifting while keeping me in control. That human-in-the-loop approach saves meaningful time and gives us greater confidence in our compliance and reporting.”

Collide was originally launched by Houston media organization Digital Wildcatters as “a professional network and digital community for technical discussions and knowledge sharing.” After raising $5 million in seed funding led by Houston’s Mercury Fund last year, the company said it would shift its focus to rolling out its enterprise-level, AI-enabled solution.
Merab Momen, founder of AI CTO Services. Courtesy Photo

How this Houston expert helps startups turn AI hype into real impact

now streaming

Artificial intelligence is now everywhere. It is mentioned in every startup pitch deck, and every corporate roadmap claims to use it. However, many early-stage businesses struggle with the simple question, “What does AI actually mean for my business?”

In a recent podcast episode of EnergyTech Startups, Merab Momen, founder of AI CTO Services and a long time AI practitioner, explains why most founders misunderstand AI, how startups can practically apply it and why Houston is quietly becoming a serious hub for AI-driven innovation.

Filling the AI Leadership Gap

Merab’s career has spanned decades of technology transitions. He worked on neutral networks in the 1990s, constructed computer vision systems long before they were common, and helped install AI solutions inside huge industrial companies. However, he noticed a huge problem when generative AI started to explode into the mainstream-The requirement of a real partner by the founders for AI integration but inability to rely on a full-time CTO and project-based consultants.

“I really needed something which is much more engaging where I can give that partner-level advice to the founders,” he said. By giving firms on-demand access to high-level AI knowledge and expertise, his methodology enables them to analyse tools, steer clear of cost blunders and eventually transition to a permanent technology leader when the time is right.

AI is Older than Most People Think

Despite its recent rise in popularity, AI is nothing new. AI actually began in the 1950s. Merab in his conversation explained how he worked on his first AI project back in the year 1996 that worked perfectly, but the processing power wasn’t just there to make it practical. He continued how he utilized the swarm intelligence models to optimize supply chains, now referred to as MLPOs and data engineering.

From Language Models to Physical World

Much of the public conversation about AI revolves around chatbots and text generation. But Merab sees far greater potential in AI’s interaction with the physical world, especially in industrial settings. He emphasized edge computing and vision language models (VLMs) as significant advances in manufacturing and energy. This physical shift is opening doors for new opportunities for robotics, automated inspections, and industrial safety applications. Merab added that Houston is uniquely positioned for this transition.

Why Houston has an AI Advantage

Silicon Valley may dominate the AI headlines, but Merab believes Houston’s advantage lies beneath the surface. The city doesn’t lag in AI utilization; it just operates in industries where results show differently.

Machine learning isn’t new to Houston’s core industries. Energy companies, manufacturers, logistics providers, and healthcare systems have been using advanced analytics for decades. The difference lies in them innovating in industrial sectors rather than consumer technology.

What’s Next

With the AI CTO Services growing, Merab is working with startups across industries to deploy AI in practical, business-first ways.

He is more interested in assisting founders in finding answers to critical issues than following new trends.

For Houston’s energy and climate tech community, it needs to transform AI enthusiasm into real-world impact.

Listen to the full conversation with Mehrab Momin on the Energy Tech Startups Podcast to learn more.

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Energy Tech Startups Podcast is hosted by Jason Ethier and Nada Ahmed. It delves into Houston's pivotal role in the energy transition, spotlighting entrepreneurs and industry leaders shaping a low-carbon future.


A new report shows the role Texas could play as the data-center sector enters "hyperdrive." Photo via JLL.com.

Texas could topple Virginia as biggest data-center market by 2030, JLL report says

data analysis

Everything’s bigger in Texas, they say—and that phrase now applies to the state’s growing data-center presence.

A new report from commercial real estate services provider JLL says Texas could overtake Northern Virginia as the world’s largest data-center market by 2030. Northern Virginia is a longtime holder of that title.

What’s driving Texas’ increasingly larger role in the data-center market? The key factor is artificial intelligence.

Companies like Google and Microsoft need more energy-hungry data centers to power AI innovations. In a 2023 article, Forbes explained that AI models consume a lot of energy because of the massive amount of data used to train them, as well as the complexity of those models and the rising volume of tasks assigned to AI.

“The data-center sector has officially entered hyperdrive,” Andy Cvengros, executive managing director at JLL and co-leader of its U.S. data-center business, said in the report. “Record-low vacancy sustained over two consecutive years provides compelling evidence against bubble concerns, especially when nearly all our massive construction pipeline is already pre-committed by investment-grade tenants.”

Dallas-Fort Worth has long dominated the Texas data-center market. But in recent years, West Texas has emerged as a popular territory for building data-center campuses, thanks in large part to an abundance of land and energy. Nearly two-thirds of data-center construction underway now is happening in “frontier markets” like West Texas, Ohio, Tennessee and Wisconsin, the JLL report says.

Northern Virginia, the current data-center champ in the U.S., boasted a data-center market with 6,315 megawatts of capacity at the end of 2025, the report says. That compares with 2,423 megawatts in Dallas-Fort Worth, 1,700 megawatts in the Austin-San Antonio corridor, 200 megawatts in West Texas, and 164 megawatts in Houston.

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Sage Geosystems brings South Texas geothermal plant online

powering up

Houston-based Sage Geosystems, a provider of geothermal power, has begun full operations at its South Texas facility for the San Miguel Electric Cooperative.

Sage says the plant was recently “placed in service,” according to a news release. This means it’s fully built and tested, and ready to generate and supply electricity to the grid.

Since selling its first electricity in Q2 of this year, the facility has been operating for more than four months to evaluate aspects like efficiency and water management. Sage relied on its GeoTwin modeling tool to carry out some of the testing.

The testing demonstrates Sage’s proprietary approach to geothermal power “overcomes one of the industry’s most persistent challenges, high water losses in engineered reservoirs, while delivering the consistent performance required to scale next-generation geothermal,” co-founder and CEO Cindy Taff said in the release.

Sage says the South Texas facility will serve as a model for future projects, including one in Nevada. The Nevada facility will use Sage’s proprietary technology to extract geothermal heat from hot dry rock, creating a reliable, affordable source of carbon-free power.

During the testing period, water losses amounted to less than 10 percent. This low rate indicates most water remains in the system, enabling Sage to capture more usable energy, boost power production and improve the project’s finances, the company says.

“Commercial geothermal isn’t just about creating a reservoir. It’s about creating one whose performance can be engineered, predicted, and consistent,” Lev Ring, the company’s co-founder, president and chief technology officer, said in the release.

Since being founded in 2020, Sage has raised $159 million in outside funding. This includes a more than $97 million Series B round co-led by Ormat Technologies and Carbon Direct Capital.

Two years ago, Sage announced a deal with Meta Platforms, the parent company of Facebook and Instagram, to supply up to 150 megawatts of geothermal power to Meta data centers.

Halliburton Labs adds 3 energy, materials startups to Houston incubator

green team

Three new companies have joined Halliburton Labs, the incubator for early-stage energy and hardtech startups run by Houston energy giant Halliburton.

Halliburton Labs provides the emerging companies with mentorship, industry connections, laboratory access and other resources as they work toward commercialization. The latest companies to join the incubator focus on battery materials, resource recovery and gas separation solutions to address some of the "key bottlenecks in the energy and industrial landscape," according to a news release.

The new members include:

  • Electroflow, a California-based company that produces lithium iron phosphate (LFP) cathode material from lithium brines through its proprietary process. LFPs are critical components in batteries used in electric vehicles, grid storage systems and industrial electrification, according to Halliburton Labs.
  • Osmoses, a Cambridge, Massachusetts-based company that has developed a membrane platform to reduce the energy use, cost and emissions associated with gas separations used in natural gas, hydrogen, helium and other industrial applications
  • SiTration Inc., another Cambridge, Massachusetts-based company that has developed electro-extraction and filtration technologies to recover critical metals like copper directly from mining waste streams more quickly and affordably. Its process has completed successful pilot projects with Tier 1 mining companies in three continents.

"Affordable and reliable molecules are essential building blocks of the energy future. Each company brings a bold, technical solution to a complex industrial challenge. We look forward to helping them scale and deploy their technologies," Andres Cabada, Halliburton Labs managing director, added in the news release.

Halliburton Labs previously added four new members from around the world —Nandina REM, Noon Energy, Proof Energy and Tidal Metals—this spring. Alumni from the incubator include Houston-based companies like NanoTech Materials, which is developing insulation and fireproofing to reduce heat transfer in buildings and outdoor infrastructure; and SolvCor, which develops water additives to improve cooling for data centers and thermal storage.

With the addition of the three new members, the incubator currently supports nine early-stage companies.

Houston companies advance 200MW green ammonia plant in South Texas

coming soon

Two global companies with a major presence in Houston are teaming up on a green ammonia plant in Port of Victoria, Texas.

Topsoe, a Danish company with operations in Houston and Bayport, Texas, has been tapped to provide ammonia synthesis technology for the project being developed by First Ammonia, a New York-based company with offices in Houston and Denmark.

The flagship 200-megawatt plant will use renewable electricity to produce green hydrogen through electrolysis, which will then be combined with nitrogen from a nearby Air Liquide pipeline to make green ammonia, according to First Ammonia. It is expected to serve both U.S. and global markets.

According to First Ammonia, every 100,000 tons of electric ammonia produced avoids approximately 240,000 tons of CO2 emissions compared to fossil ammonia

The Topsoe technology used on site is designed to be able to increase production from 10 percent to 100 percent within 30 minutes, and decrease production at a similar rate, allowing the plant to respond to fluctuations from solar- or wind-based energy sources.

“Topsoe is the world leader in ammonia synthesis, and First Ammonia is delighted to continue our partnership with them in establishing a green ammonia industry in the US and around the world,” Joel Moser, CEO of First Ammonia, said in a news release.

Topsoe has previously signed on to supply its 100-megawatt solid oxide electrolyser (SOEC) to the First Ammonia project. However, the company announced in March that it did not extend the contract after multiple delays.

The First Ammonia project was originally expected to come online by 2027 and to produce 1.1 million tonnes of green ammonia. The project is now expected to reach financial before the end of 2026, with construction slated to begin in 2027 and commercial operations launching by 2029.

“As green ammonia projects move from ambition to execution, operational flexibility becomes increasingly important,” Yassir Ghiyati, chief commercial officer at Topsoe, added in a news release. “We’re proud to support First Ammonia with technology designed to enable efficient and reliable green ammonia production. We look forward to continuing to work with the First Ammonia team to help bring this important U.S project to life.”