Introduction
In the last year a San‑Francisco startup called Alembic Technologies has moved from a modest signal‑processing lab to the front of a new wave in enterprise artificial intelligence. The company’s recent $145 million Series B round, led by Prysm Capital, Accenture, and a host of other investors, is not just a financial milestone; it is a statement about where the next competitive edge in AI will lie. Alembic’s thesis is simple yet profound: as large language models (LLMs) converge in capability, the real differentiator will be the ability to mine proprietary, private data for causal insights that generic models cannot provide. To support this vision the firm has poured a significant portion of its capital into a custom‑built, liquid‑cooled Nvidia NVL72 superPOD that it claims is one of the fastest privately owned supercomputers in the world.
The story behind this leap is a classic startup narrative of resource constraints, unexpected breakthroughs, and a partnership with a tech giant that turned a potential stumbling block into a launchpad. Alembic began as a company that measured marketing performance through correlation analytics, but a pivotal experiment on an “army of Mac Pros” revealed that its underlying causal model could be applied across any domain that had time‑series data. That discovery reshaped the company from a niche marketing vendor into a platform that could become the central nervous system of an entire enterprise.
In this post we explore how Alembic’s causal AI differs from the correlation‑based tools that dominate business intelligence, why the company chose to build its own supercomputer instead of relying on cloud providers, and how its partnership with Nvidia and its focus on private data could redefine the future of enterprise AI.
Main Content
Causal AI Versus Correlation Analytics
Traditional business intelligence tools and many AI systems rely on correlation: they identify patterns that co‑occur but do not necessarily imply a cause‑effect relationship. For example, a marketing dashboard might show that higher social media engagement correlates with increased sales. While useful, this insight can be misleading if both variables are driven by a third factor, such as a viral event or a seasonal trend.
Alembic’s approach is fundamentally different. By modeling cause‑and‑effect relationships, the platform can answer questions that correlate‑only tools cannot. It can determine whether a particular marketing campaign directly caused a lift in revenue or whether the lift was merely coincidental. This level of insight is critical for executives who must allocate budgets and make strategic decisions under uncertainty. The company’s customers—Delta Air Lines, Mars, Nvidia, and several Fortune 500 firms—have reported that the platform provides a unified view across channels and campaigns, unlocking insights that were previously inaccessible.
A concrete example comes from Delta’s measurement of its Team USA Olympic sponsorship. Traditional attribution models would have treated brand exposure as an abstract “awareness” metric, but Alembic’s causal framework linked the sponsorship directly to ticket sales within days of activation. This real‑time, granular measurement is something that has eluded marketers for decades.
From Mac Pros to a SuperPOD: The Computational Imperative
The computational demands of Alembic’s models are far beyond what typical LLMs require. Unlike a chatbot that is trained once on a static dataset, Alembic’s system uses online, evolving models built on spiking neural networks—brain‑inspired architectures that continuously learn as new data arrives. Each customer’s data feeds into a unique model that permutes through billions of possible combinations to identify the strongest causal signals. This process is computationally intensive and requires more than the “production Porsche” infrastructure that cloud providers typically offer.
To meet these demands, Alembic partnered with Equinix to deploy an Nvidia NVL72 superPOD in San Jose. The system is liquid‑cooled and equipped with Nvidia’s latest Blackwell GPUs, a configuration that the company claims is unique among non‑Fortune 500 firms. Alembic’s founder, Tomás Puig, explained that the platform writes custom CUDA code and GPU kernels optimized specifically for causal inference workloads—optimizations that are impossible on standard cloud configurations.
The decision to own the infrastructure also addresses data sovereignty concerns. Many of Alembic’s clients—particularly in financial services and regulated industries—are contractually prohibited from storing sensitive data on public cloud platforms. By operating its own neutral data center, Alembic can serve customers who would otherwise refuse to use cloud‑based analytics, creating a moat that is difficult for hyperscale providers to replicate.
Nvidia’s Role: From Curiosity to Collaboration
The partnership between Alembic and Nvidia began in an unexpected way. After Alembic announced its Series A funding, Nvidia CEO Jensen Huang read a VentureBeat article about the startup and reached out to the team. Nvidia’s involvement went beyond providing hardware; the company helped Alembic secure a private cage at Equinix, supplied an H100 GPU cluster, and later fast‑tracked access to the next‑generation NVL72 superPOD.
Nvidia’s AI Enterprise software suite—particularly cuGraph for graph processing and TensorRT for high‑speed inference—has become a secret weapon for Alembic. The integration allows the company’s research teams to focus on breakthrough mathematics rather than low‑level engineering. Nvidia’s support also extended to hardware maintenance; when Alembic’s extreme workloads melted GPUs, Nvidia accelerated the rollout of liquid‑cooled systems, turning a potential failure into a competitive advantage.
Real‑World Impact Across Industries
Alembic’s customer base has grown rapidly, spanning marketing, finance, technology, and even sports. Mars used the platform to measure the sales impact of changing candy shapes for themed promotions, while a Fortune 500 technology firm increased its sales pipeline by 37 % using Alembic’s attribution models. Financial services clients now link CEO public appearances and co‑marketing expenditures to actual fund flows, a level of insight that was previously unattainable.
The platform’s predictive capabilities are equally impressive. Alembic claims it can forecast revenue, close rates, and customer acquisition up to two years in advance with 95 % confidence. This predictive power, combined with causal inference, allows executives to see not just what happened but why it happened and how it will happen.
Competitive Landscape and Strategic Moats
Alembic operates in a crowded field that includes Nielsen, Google Analytics, Meta’s measurement tools, and emerging AI‑powered analytics startups. However, the company’s proprietary mathematics, massive compute requirements, and focus on private data create structural advantages that are difficult to replicate.
First, the causal models are protected by patents and built on proprietary algorithms that would require a fresh research effort to duplicate. Second, the compute footprint—hundreds of millions of dollars a year in equivalent cloud costs—acts as a natural barrier to entry. Third, the data sovereignty model positions Alembic as a trusted partner for regulated industries that cannot use public cloud services.
Finally, Alembic’s ability to handle messy, fragmented data stems from years of engineering that preceded its causal breakthrough. This foundational signal‑processing capability ensures that the platform can ingest and clean data from a wide variety of sources, a feature that many competitors lack.
The Future of Enterprise AI: Private Data as the New Frontier
Alembic’s $145 million Series B validates a contrarian bet in an AI landscape dominated by the race to build ever‑larger LLMs. While OpenAI, Anthropic, and others compete on general‑purpose conversational abilities, Alembic is building infrastructure for a different kind of intelligence—one that understands cause and effect in the proprietary data that defines each company’s competitive position.
The company’s evolution—from bootstrapped simulations on Mac Pros to a private supercomputer—mirrors the maturation of enterprise AI. As the technology moves from experimentation to mission‑critical deployment, companies need more than general‑purpose models trained on public data. They need systems that can process their private information to answer questions that competitors cannot.
Puig’s thesis—that private data becomes the key differentiator as public models converge—echoes historical shifts in technology. Search engines commoditized public information, making proprietary data more valuable. Cloud computing turned infrastructure into a utility, elevating the importance of what you build on top of it. If large language models similarly converge, the competitive advantage will flow to those who can best extract intelligence from data others cannot access.
Alembic is already testing its technology beyond marketing analytics, exploring pilots in robotics and launching GPU‑accelerated databases. The ambition is to become the central nervous system of the enterprise, connecting cause and effect across every business function.
Conclusion
Alembic Technologies has positioned itself at the intersection of causal AI, private data, and high‑performance computing. By investing in a custom liquid‑cooled superPOD and partnering with Nvidia, the company has built a platform that can process proprietary data at a scale that would cost hundreds of millions on public cloud services. Its proprietary mathematics and focus on causal inference give it a moat that is difficult for competitors to erode.
While the enterprise AI landscape remains competitive and the technical challenges are significant, Alembic’s early successes with Delta, Mars, and other Fortune 500 clients demonstrate the tangible value of moving from correlation to causation. If the company can continue to scale its infrastructure and refine its models, it could redefine how businesses make data‑driven decisions, turning private data into a sustainable competitive advantage.
Call to Action
If you’re an enterprise executive, data scientist, or AI enthusiast looking to move beyond correlation and unlock the true potential of your proprietary data, consider exploring Alembic’s causal AI platform. Reach out to their team to discuss how a private, high‑performance supercomputer can transform your decision‑making processes. For those building the next wave of AI infrastructure, Alembic’s story is a compelling reminder that the future of enterprise AI may not be about bigger language models, but about smarter, data‑centric systems that understand cause and effect at scale.