Alembic Technologies has raised $145 million in Series B and progress funding at a valuation 15 occasions greater than its earlier spherical, betting that the following aggressive benefit in synthetic intelligence will come not from higher language fashions however from proprietary knowledge and causal reasoning.
The San Francisco-based startup, which builds AI programs that establish cause-and-effect relationships slightly than mere correlations, is utilizing a good portion of the capital to deploy what it claims is without doubt one of the quickest privately owned supercomputers ever constructed — an Nvidia NVL72 superPOD that can energy its enterprise-grade causal AI fashions.
The funding, led by Prysm Capital and Accenture with participation from Silver Lake Waterman, Liquid 2 Ventures, NextEquity, Friends & Family Capital and WndrCo, positions Alembic amongst a choose group of well-funded AI laboratories remodeling how companies make multimillion-dollar selections.
The funding spherical and the corporate’s strategic course replicate a broader shift going down in enterprise AI because the efficiency hole between competing massive language fashions narrows. Whereas startups and tech giants have poured billions into constructing ever-larger chatbots, Alembic is pursuing a unique thesis: that the actual worth in AI will accrue to programs that may course of non-public company knowledge to reply questions that generic fashions can not.
“As highly effective synthetic intelligence fashions more and more converge in functionality, the important thing aggressive benefit shifts to proprietary knowledge,” stated Tomás Puig, Alembic’s founder and chief government, in an interview with VentureBeat. “Getting an actual edge is not about utilizing the most effective LLM; it is leveraging the distinctive info rivals cannot entry.”
Puig illustrated the issue going through enterprise executives: “Think about I run a CPG firm and I set up the newest ChatGPT. I ask, ‘Hey, ChatGPT, give me a method for how one can enhance my income share within the northeast.’ Then your competitor down the highway asks the very same query. How a lot hassle are you in once they get the very same reply?”
How a broke startup on Mac Execs found a breakthrough that modified every thing
The dramatic valuation enhance—from roughly $50 million on the Collection A to roughly $645 million now, based on folks accustomed to the matter — displays a elementary transformation in Alembic’s expertise and market positioning since its earlier funding spherical.
When the corporate raised its Collection A in early 2024, it was primarily a sign processing and correlation analytics firm targeted on advertising measurement. “Causal didn’t exist as a expertise for us until after the Collection A,” Puig instructed VentureBeat. The corporate was so resource-constrained that it could not even run simulations to check whether or not its causal fashions would work.
The breakthrough got here after the Collection A when the corporate lastly had sufficient capital to check its theories. “We have been so broke that we could not even run the simulation to see if it labored,” Puig recalled. After they did run the exams — initially on an “military of Mac Execs” as a result of they did not but have GPU infrastructure — they found one thing surprising: their causal mannequin labored not only for advertising analytics however throughout nearly any enterprise area with time-series knowledge.
“We began including capabilities as clients requested them, which was simply wise—iterative,” Puig defined. “We discovered the mannequin works throughout an enormous majority of information universally. What we thought may be a mannequin for a selected vertical ended up being a full, generalized foundational mannequin.”
That discovery remodeled Alembic from a advertising expertise vendor into an organization constructing what Puig describes as “your entire central nervous system of the enterprise throughout all verticals — not simply gross sales, advertising, provide chain, finance, and past.”
Why cause-and-effect AI issues greater than correlation for enterprise decision-making
Causal AI is a essentially completely different method from the correlation-based analytics that dominate most enterprise intelligence instruments and even many AI programs. The place conventional analytics would possibly present that social media engagement correlates with gross sales will increase, causal AI can decide whether or not the social media exercise truly prompted the gross sales carry — or whether or not each have been pushed by some third issue, like a viral information occasion.
The excellence issues enormously for executives making finances allocation selections. “Most companies aren’t brief on knowledge,” Puig stated. “They’re brief on solutions.”
For Alembic’s clients, which now embody Delta Air Lines, Mars, Nvidia and several other Fortune 500 firms throughout monetary providers, expertise and client packaged items, the platform can reply beforehand unanswerable questions on advertising effectiveness, operational effectivity and strategic investments.
“Alembic’s means to attach advertising publicity on to enterprise outcomes—with pace, precision and granularity—is what made this relationship so transformative for us,” stated Alicia Tillman, chief advertising officer at Delta Air Traces. “In contrast to conventional measurement instruments, Alembic gave us a unified view throughout channels and campaigns, unlocking insights we merely could not entry earlier than.”
The airline used Alembic to quantify the income carry from its Team USA Olympics sponsorship inside days of activation, immediately linking model actions to ticket gross sales—a kind of measurement that has eluded entrepreneurs for many years. Conventional attribution fashions both ignore brand-building completely or assign it obscure “consciousness” metrics that do not translate to monetary affect.
“It is very transformative,” Puig stated of the client affect. “What’s attention-grabbing is that executives themselves are the customers of our software program and our outputs. It isn’t a device utilized by a single marketing campaign supervisor.”
Contained in the two-story liquid-cooled supercomputer that actually melted GPUs
Alembic’s choice to speculate closely in owned computing infrastructure slightly than depend on cloud suppliers stems from each the technical calls for of its causal fashions and the acute knowledge sensitivity necessities of its enterprise clients.
The corporate is deploying an Nvidia NVL72 superPOD — an enormous liquid-cooled system outfitted with Nvidia’s most superior Blackwell graphics processing items — in partnership with knowledge heart operator Equinix in San Jose, Calif. In line with Puig, Nvidia knowledgeable Alembic that it’s the solely non-Fortune 500 firm on this planet to function such a system.
The necessity for this stage of compute stems from how Alembic’s fashions work. In contrast to massive language fashions which might be skilled as soon as on historic knowledge after which deployed, Alembic’s system makes use of “on-line and evolving” fashions constructed on spiking neural networks — brain-inspired architectures that repeatedly study as new knowledge arrives.
“It creates itself as you feed it knowledge, like human evolution,” Puig defined. “The mannequin is singular, however it finally ends up creating a unique mind for each single firm.”
This steady studying occurs at huge scale. When a buyer brings in knowledge, Alembic’s system mechanically permutates by billions of attainable mixtures of how that knowledge might be analyzed — testing each conceivable strategy to slice metrics and dimensions to seek out the strongest causal alerts. That stage of computation requires what Puig calls “F1 automotive” infrastructure slightly than the “manufacturing Porsche” supplied by cloud suppliers.
The corporate writes customized CUDA code and low-level GPU kernels optimized particularly for causal inference workloads — optimizations that are not attainable on normal cloud configurations. The method has confirmed so demanding that Alembic famously as soon as melted down its GPUs by pushing them past their thermal limits. “We actually simply drive these circuits so laborious that we’d like the F1 automotive model and we’ve got to have entry to it,” Puig stated.
The transfer to liquid-cooled programs addresses that drawback, however it additionally permits Alembic to run workloads that might price orders of magnitude extra on cloud platforms. “We did the mathematics—if we have been to purchase only one subsection of our compute from AWS, it might be $62 million a 12 months,” Puig stated. Proudly owning the infrastructure prices “a fraction of that.”
The supercomputer technique serves one other essential objective: knowledge sovereignty. Lots of Alembic’s clients — notably in monetary providers, client packaged items and controlled industries — have contractual prohibitions towards placing delicate knowledge on Amazon Web Services, Microsoft Azure or Google Cloud.
“CPG firms don’t need any knowledge to exist on Amazon, ever,” Puig stated. “They merely will not enable it. Some clients refuse to make use of Microsoft, others keep away from completely different suppliers. And sure banks and monetary establishments are legally prohibited from utilizing cloud platforms in any respect.”
By working its personal infrastructure in impartial knowledge facilities, Alembic can serve clients who would by no means contemplate cloud-based analytics — a aggressive moat that might be troublesome for hyperscale cloud suppliers to copy.
How Jensen Huang learn a information article and adjusted Alembic’s future
Alembic’s relationship with Nvidia illustrates each the startup’s technical ambitions and the way the chip large helps promising AI firms. Nvidia is Alembic’s founding enterprise buyer, unique supercomputing accomplice and a key technical collaborator — although notably not an investor.
The connection started in an unlikely approach. After Alembic introduced its Collection A funding in early 2024, Nvidia co-founder and CEO Jensen Huang learn the VentureBeat protection and emailed his employees suggesting they discover the corporate, based on Puig. As a result of Alembic did not but have a contact kind on its web site, an Nvidia director reached out through LinkedIn.
The partnership almost foundered on a primary constraint: computing capability. After Alembic delivered its first causal evaluation — which took weeks to generate on an array of Mac Execs — Nvidia requested if they might produce weekly experiences. “I stated no, as a result of it took weeks on this military of machines,” Puig recalled.
When Alembic stated they might do it with GPUs however could not safe the mandatory compute — cloud suppliers on the time required committee approvals and supplied two- to six-week lead occasions with no ensures — Nvidia intervened immediately. The chip maker organized for Equinix to offer a non-public cage in Northern Virginia with enough energy capability and helped Alembic supply its first H100 GPU cluster.
“With out that, the corporate would by no means have existed,” Puig stated. “We could not get the compute within the configuration we would have liked anyplace else.”
The partnership has since deepened. Alembic makes use of Nvidia’s AI Enterprise software suite, together with specialised libraries like cuGraph for graph processing and TensorRT for high-speed inference. The tight integration, Puig stated, permits “our analysis groups to leverage multi-exaflop-level compute and Nvidia’s algorithmic software program stack. This integration is certainly one of our secret weapons: we spend extra time on breakthrough analysis and arithmetic and fewer time on repetitive low-level engineering.”
Nvidia’s assist prolonged past expertise. When Alembic stored destroying GPUs beneath excessive workloads — pushing chips so laborious that thermal stress cracked circuit boards — Nvidia fast-tracked the startup’s entry to next-generation liquid-cooled programs. “The humorous purpose we acquired [the NVL72],” Puig stated, “is as a result of after we melted the chips, Nvidia was actually aggravated with how typically they needed to service our guarantee.”
From Olympics sponsorships to viral sweet moments: How Fortune 500s measure what was unmeasurable
Alembic’s buyer roster has expanded quickly as enterprises search methods to measure AI and advertising investments that conventional analytics can not seize. The corporate now works with Delta Air Lines, Mars, a number of Fortune 500 expertise and monetary providers corporations, and Texas A&M University’s athletics program.
The use instances span far past Alembic’s authentic advertising focus. Mars used the platform to measure the gross sales affect of fixing sweet shapes for themed promotions. A Fortune 500 expertise firm expanded its gross sales pipeline by 37% utilizing Alembic’s attribution fashions. Monetary providers corporations are utilizing it to attach CEO public appearances and co-marketing expenditures to precise fund flows.
“Alembic helped us transfer previous impression counts to point out what truly drove net-new funding,” stated the top of co-marketing at a Fortune 200 monetary providers firm. “For the primary time, we might see how our CEO within the public eye and our co-marketing {dollars} with exchanges translated into actual fund flows.”
For Mars, the flexibility to measure beforehand unmeasurable actions has remodeled decision-making. “We’re utilizing math to liberate creativity,” stated Gülen Bengi, lead world chief advertising officer for Mars and world chief progress officer for Mars Snacking. “Our followers and communities create billions of natural conversations and content material about our manufacturers. When a viral second occurs, we usually know it is directionally optimistic, however we won’t attribute the gross sales uplift or its place within the buyer journey. Alembic’s Causal AI is a breakthrough, permitting us to maneuver past correlation to see precisely how that natural dialog created a sequence that immediately impacted gross sales.”
The platform can predict income, shut charges and buyer acquisition as much as two years prematurely with 95% confidence, based on Puig. “What they have been doing earlier than was they really actually didn’t find out about sure issues,” he stated, describing how clients beforehand estimated the worth of stadium naming rights or main sponsorships with out ever measuring precise greenback affect. “Now you possibly can go and be prefer it had this impact on this a lot P&L, and that is the place it is flowing, and you’ll know inside days or close to actual time.”
Why Google, Meta and Nielsen cannot simply replicate what Alembic constructed
Alembic operates in a aggressive panorama that features conventional advertising measurement distributors like Nielsen, analytics platforms from Google and Meta, and rising AI-powered analytics startups. However Puig argues the corporate has constructed structural benefits that might be troublesome to copy.
First, the corporate’s causal fashions depend on proprietary arithmetic developed over years and guarded by patents. “You would need to begin from scratch,” Puig stated. “This isn’t like an LLM that makes use of a transformer that has a paper, and you may try and recreate. You’d truly need to go and recreate the methodology from scratch.”
Second, the large computing necessities create a pure barrier. Alembic operates at “foundational mannequin ranges of compute, not like even one thing you’ll run from [AWS] Sagemaker,” Puig stated. “We’re speaking about tons of of tens of millions of {dollars} a 12 months” in equal cloud prices.
Third, the info sovereignty necessities of enterprise clients create alternatives for impartial third events that hyperscale cloud suppliers wrestle to handle. As one enterprise capital investor famous, enterprises more and more fear about placing strategic knowledge into programs owned by potential rivals.
Lastly, Alembic’s means to work with messy, fragmented knowledge displays years of engineering that preceded its causal AI breakthrough. “The primary 4 [or] 5 years of the corporate’s life was constructing that big sign processor that handled messy knowledge,” Puig stated. “We’d not have the ability to do it if we had not taken all that point.”
Why Alembic’s contrarian wager on non-public knowledge might reshape enterprise AI
The $145 million funding spherical validates a contrarian wager in an AI panorama dominated by the race to construct ever-larger language fashions. Whereas OpenAI, Anthropic and others compete on whose chatbot can write higher code or reply extra trivia questions, Alembic is constructing infrastructure for a unique type of intelligence — one which understands trigger and impact within the messy, proprietary knowledge that defines every firm’s distinctive aggressive place.
The corporate’s evolution from a bootstrapped startup working simulations on Mac Execs to working one of many world’s quickest non-public supercomputers mirrors the broader maturation of enterprise AI. Because the expertise strikes from experimentation to mission-critical deployment, firms want greater than general-purpose fashions skilled on public knowledge. They want programs that may course of their non-public info to reply questions their rivals can not.
Puig’s thesis — that non-public knowledge turns into the important thing differentiator as public fashions converge — resonates with how different applied sciences advanced. Search engines like google and yahoo commoditized entry to public info, making proprietary knowledge extra useful. Cloud computing made infrastructure a utility, elevating the significance of what you construct on prime of it. If massive language fashions equally converge in functionality, the aggressive benefit flows to whoever can greatest extract intelligence from knowledge others can not entry.
The corporate is already testing its expertise past advertising analytics. Pilots are underway in robotics, the place causal fashions might assist autonomous programs perceive how actions result in outcomes. New product strains are launching, together with the GPU-accelerated database that clients are shopping for individually. The ambition, Puig stated, is to grow to be “the central nervous system” of the enterprise — the layer that connects trigger and impact throughout each enterprise perform.
Whether or not Alembic can ship on that imaginative and prescient stays to be seen. The corporate operates in advanced enterprise environments the place gross sales cycles are lengthy and integration challenges are vital. Opponents aren’t standing nonetheless, and the technical moats that shield it right this moment might erode as causal AI methods grow to be higher understood.
However for now, Alembic occupies a singular place. It has marquee clients attaining measurable outcomes. It has infrastructure that might price tons of of tens of millions to copy on cloud platforms. It has proprietary arithmetic refined over years of coping with messy enterprise knowledge. And it has $145 million to scale what Puig describes as a elementary shift from correlation to causation.
In his interview with VentureBeat, Puig drew a parallel to quantitative hedge funds that use arithmetic to achieve buying and selling benefits that general-purpose AI can not match. “ChatGPT nonetheless cannot equal Renaissance Applied sciences,” he stated, referring to the secretive agency that has generated historic returns by quantitative fashions.
The comparability captures Alembic’s core perception: that in a world the place everybody has entry to the identical general-purpose AI, sustainable benefit comes from specialised programs that perceive the cause-and-effect relationships hiding in your knowledge. It is a wager that the way forward for enterprise AI seems to be much less like a common chatbot and extra like a non-public intelligence engine — one which, to Puig’s authentic level, prevents your competitor from getting the identical reply once they ask the identical query.
