Startup Bets on Proprietary Data, Not Language Models, to Gain AI Edge

In November 2025, Alembic Technologies, a San Francisco-based startup, announced that it has raised $145 million in Series B and growth funding. The company, which builds AI systems that identify cause-and-effect relationships, is using a significant portion of the capital to deploy what it claims is one of the fastest privately owned supercomputers ever built.

Alembic's strategic direction reflects a broader shift in enterprise AI, as the performance gap between competing large language models narrows. While startups and tech giants have poured billions into building ever-larger chatbots, Alembic is pursuing a different approach, believing that the real value in AI will accrue to systems that can process private corporate data to answer questions that generic models cannot.

The company's platform has already helped customers like Delta Air Lines and Mars measure the impact of marketing and strategic investments, unlocking insights that were previously elusive. Alembic's causal AI models can predict revenue, close rates, and customer acquisition up to two years in advance with 95% confidence, a capability that has transformed decision-making for its enterprise clients.

Alembic's success is built on its proprietary mathematics, massive computing infrastructure, and ability to work with fragmented, messy data that enterprises typically struggle with. The company's contrarian bet on private data and causal reasoning could reshape the future of enterprise AI, as companies seek to gain a competitive edge by extracting intelligence from information that their rivals cannot access.