Applied Computing raises $20m to build a foundation model for the refinery



A single refinery can carry thousands of sensors measuring temperature, pressure, velocity, and viscosity. According to Applied Computing, operators make decisions using less than 8% of what those sensors tell them.


The London startup has raised a $20m Series A to close that gap, led by engineering giant KBR with Databricks Ventures participating. Founded in 2023, it is building a foundation model for oil, gas, refining, and petrochemicals.

The problem is not collection, according to co-founder and chief executive Callum Adamson. Operators already gather the information.

They cannot combine sensor readings, engineering documentation, and the underlying physics and chemistry fast enough to predict anything useful from them.

The shape will be familiar: a foundation model trained on proprietary industrial data, with a large incumbent as both investor and route to market.

Mistral launched its industrial engineering tier with Airbus, BMW, and EDF as named customers on the same logic.

“It’s getting those three data sources to talk to each other in real time,” Adamson told TechCrunch. “That’s the real key.”

Its model, Orbital, is not a language model with an industrial skin on it. The company says it fuses a time series model, a physics-based model, and a language model to predict the state of a facility, reading sensor data while accounting for chemistry, equipment constraints, and what the operators are actually doing.

It also lets technicians simulate how a change in one part of a plant would ripple through the rest. That is the part the industry has historically paid consultants and weeks of downtime for.

It is also where the stakes sit. A refinery is not a customer-service queue, and Amazon has already warned that human oversight of AI degrades precisely because people stop scrutinising a system that is usually right.

The pitch, in the end, is speed. Applied Computing claims Orbital can flag an anomaly, work out what caused it, and model whether a proposed fix creates a problem somewhere else, all within minutes. Adamson says investigations that took days or weeks compress into seconds.

Some of this is landing. The company says it went from stealth to double-digit millions in annual recurring revenue in under 18 months, with Orbital deployed at unnamed “large, publicly listed” upstream, refining, and petrochemical companies.

Adamson declined to say how many customers it has, which is the sort of omission worth noticing next to a revenue claim.

KBR has integrated Orbital into its INSITE 3.0 platform and is using it for ammonia production. Adamson said the company is working with a major US upstream operator and expects to announce a European oil major in coming weeks.

The competitive picture is crowded and old. AspenTech sells simulation and AI-powered modelling across upstream, refining, and chemicals, while AVEVA does physics-based process simulation and what-if modelling.

Cognite and Seeq work the data layer. None of these are startups that can be outrun.

Adamson’s answer is that none of them are competing for the right talent. “It’s an AI problem. It’s not a data problem, and it’s not an energy problem,” he said. “If you’re a tier-one AI researcher, where are you going to work? I don’t think Shell’s on that list.”

It is a good line, and it is also the entire bet. The claim is that the moat is neither industrial data nor process knowledge, both of which the incumbents have in depth, but the ability to assemble researchers who can build a model that beats Orbital.

Whether a $20m Series A buys that against AspenTech’s installed base is the open question.

There is a second-order argument underneath. Adamson notes that operational data from working refineries is not public, and that simulated data cannot reproduce what happens inside a live plant, which makes deployments themselves the asset.

The KBR partnership matters for the same reason: it brings operational data, industry expertise, and introductions.

That reasoning is why heavy industry keeps ending up here. UPS is running a real-time digital twin of its entire logistics network on much the same basis.

The money goes on international expansion and research and engineering hires. The company opened a Houston office on Thursday, adding to its London headquarters and Bengaluru operational hub. The Middle East is next.



Source link

Leave a Reply

Subscribe to Our Newsletter

Get our latest articles delivered straight to your inbox. No spam, we promise.

Recent Reviews


Netflix’s recommendation algorithm has evolved and can be good at predicting what you’ll want to watch next. The problem is that it often ends up showing the same titles, making it harder to actually discover what aligns with your unique tastes.

If you’ve ever felt like your homepage is stuck recommending the same movies or Netflix Originals, you’re not alone. Fortunately, there are better ways to discover your next favorite watch without wasting time scrolling through the home page. Here’s what you need to know.

The issue with Netflix’s home page recommendations

The loop is hard to break

Netflix’s recommendation algorithm is great at one thing: keeping you hooked on that browsing doomscroll.

The problem, however, is that it often ends up showing you more of what you’ve already watched instead of helping you discover something new. If you’ve recently binged a crime thriller or romantic comedy, your homepage can often become flooded with only similar titles, creating a sort of recommendation bubble that’s hard to escape.

New, trendy releases and Netflix Originals also tend to be shoved down your throat and given more priority over your actual watch history, which means older classics, international films, and hidden gems can get buried.

While the homepage is useful for casual browsing, I’ve found that relying on it alone can make Netflix’s massive catalog feel a little limited. That’s why I turn to these sources for building my Netflix watch list instead.

Award databases

Official sources for the star-studded recommendations

A look at a previous Oscars ceremony. Credit: Oscars

One of my favorite places to discover new (and old) movies and shows is the extensive award databases that house some of the most critically acclaimed titles of the decade and beyond. The likes of the Academy Awards and Golden Globes have official award databases, sorted by years, decades, and even award categories, allowing you to browse flexibly. This is one way I find interesting foreign language films, as well as classics that often may not show up on Netflix’s search results.

You can also specifically look up award-winning title collections on Netflix (the “Oscars Spotlight” is a favorite of mine) or filter your search using award names to narrow down titles. I’ve found many underrated, local gems by simply searching for award-winning films in my region.

Review aggregators

A look at a top 250 film list on IMDb for Netflix.

This might be an obvious one, but an external source that many viewers turn to when it comes to discovery is their favorite review aggregator. I always look up the IMDb and Rotten Tomatoes scores of shows that I’m on the fence about. Although it’s not a foolproof way to find titles, it’s definitely a good way to get the big picture about movies and shows, especially newer ones.

You can also use review aggregators as a recommendation system, outside of just glancing at a title’s overall rating. When you use a website like IMDb, you can find top 100 or top 250 lists that show, for instance, the highest-rated titles on Netflix. Such lists can be further filtered by popularity and number of ratings, allowing you to narrow down to the cream of the crop. This tactic has helped me filter and find the most popular titles liked by audiences, looking beyond Netflix’s own popular lists.

Beyond IMDb and Rotten Tomatoes, websites like Metacritic and TV Time can be useful. Each platform has its own community and ranking system, which means you’ll often uncover recommendations that never surface on Netflix’s homepage.

Tudum lists

Curated lists for your discovery

Netflix Tudum's personalized recommendation lists.

Netflix Tudum is an often-overlooked resource for Netflix recommendations. This official Netflix companion website houses a wide collection of lists and recommended watches (often trendy and specific), so you can find similar titles to your most loved recent watch. Tudum’s “What to Watch” section is specifically built to help you find new titles that align with your tastes, and if you want to look past the trending, new titles, Tudum can also help you find personalized picks (since it taps into your Netflix history).

I’ve also found Tudum useful for seasonal watch lists, genre-specific recommendations, and curated collections built around new releases. Since the recommendations come directly from Netflix, it’s often one of the easiest ways to discover titles that may not be prominently featured on your homepage.

The More Like This feature

An underrated feature, right under your nose

Netflix's More like This feature.

This is a Netflix feature that’s often ignored because of its placement, but if you’ve ever noticed it, the “More Like This” recommendations that pop up when you click under a title’s preview can actually be really useful for discovery. This lets you find similar titles to the one you’re previewing without any extra searches, and I’ve found these recommendations to be more relevant and less focused on trending or new titles.

Connectivity

Bluetooth 4.2/HDMI ARC/Optical in/AUX in

Drivers

Dynamic Driver

With 2 angled full-range drivers, spatial audio, and Bluetooth built-in, the TV speaker is a powerful soundbar to help deliver amazing sound to the entire room.


If I finish a movie I love, this is usually one of the first places I check before I move on to Tudum or reviews. To find these recommendations, simply click the down arrow on a title (when you hover over it on your home page), scroll down, and find the More like this section.

Netflix secret codes

The secret to better browsing

Camp Sci-Fi and Fantasy titles on Netflix found using a secret code.

Netflix’s secret codes, an underutilized Netflix tool that flies under the radar, are a cheat code to beat the search algorithm. If you’ve ever felt frustrated with the results of your (super niche) searches or felt that results are often irrelevant to your search, then secret codes will help you filter titles by genres, languages, and specific tropes.

Using a Netflix secret code is simple: once you find a genre or trope-based secret code, you just need to type and search for it in your Netflix search bar. You can access Netflix’s full list of secret codes and use them for easy filtering and discovery.

Film creators and reviews

Finding filmmaking excellence

Letterboxd reviews. Credit: Letterboxd

Finally, my favorite way to find new titles, beyond the homepage recommendations and external ratings, is engaging with content from film creators. I love to watch video essays and cinema analysis, and creators like Thomas Flight have helped me find some of the best films I’ve watched, on Netflix and beyond.

You can also tap into resources like Letterboxd for detailed reviews, lists, and recommendations from fellow movie lovers. These tend to be more detailed and focused on aspects like storytelling, performances, and filmmaking techniques, making them a perfect resource for finding great films that escape your Netflix algorithm.


While Netflix’s homepage can be a useful starting point, it shouldn’t be your only source of discovery. This combination of external resources and Netflix’s underrated discovery tools can help you find titles that fill in the gaps of your personal watch list.

Subscription with ads

Yes, $8/month

Simultaneous streams

Two or four

Stream licensed and original programming with a monthly Netflix subscription.




Source link