Engram announced that it has emerged from stealth with $98 million in funding to build what it describes as a learned memory layer for AI. The funding came from General Catalyst, Kleiner Perkins, Sequoia Capital, Factory, Modern, Amplify Partners, Neo, and notable angels and advisors. The angel and advisor group includes Assaf Rappaport, co-founder and CEO of Wiz; Andrej Karpathy, co-founder of OpenAI; and Pieter Abbeel, AI and robotics pioneer and co-director of the Berkeley AI Research Lab.
Engram is focused on helping enterprise AI systems understand the unique context of each organization. The company said many AI systems used by employees today have to repeatedly reread documents, relearn organizational context, and rediscover institutional knowledge with every query.
The company’s technology trains models to study an organization’s world and anticipate questions in advance. This creates a compact and continuously improving memory unique to each customer.
Engram said its models can match or outperform frontier models while using up to 100 times fewer tokens. The company said this can help enterprises reduce the cost and complexity of running AI agents across business functions.
Engram was founded by AI researchers from Stanford, Berkeley, and Cornell. CEO and co-founder Dan Biderman completed postdoctoral work at Stanford under Chris Ré and previously earned a PhD at Columbia’s Center for Theoretical Neuroscience.
The founding team also includes CTO Sabri Eyuboglu, a Stanford PhD who created Cartridges, a method for turning large bodies of documents into reusable memory; Jessy Lin, a Berkeley PhD who developed Active Reading; Jack Morris, a Cornell PhD known for work on retrieval and memorization in large language models; Scott Linderman, a Stanford professor of statistics and neuroscience focused on state space models; and Chris Ré, a Stanford professor and co-founder of Engram.
Engram is entering the market with early commercial traction, including a partnership with Microsoft. The companies are working together to test Engram’s models within Microsoft 365, with the goal of making enterprise AI more efficient and more attuned to each organization’s specific context.
The Microsoft partnership also includes a commitment to GPU capacity across Dapple and Azure, giving Engram infrastructure to train its models at scale.
Engram is also partnering with Notion and Harvey to bring its memory layer into their platforms. Notion is testing Engram’s models inside its new custom agents, while Harvey is working with Engram to build learned enterprise memories for legal and enterprise use cases.
Engram said its approach gives companies ownership over the intelligence their AI systems develop. As organizations use Engram, the company said their models become more specialized and proprietary, creating enterprise-specific AI that improves with continued use.
KEY QUOTES:
“Whatever the AI knows about you is improvised on the spot — a sticky note about your past, a document pulled mid-conversation. If we can anticipate your interactions, we can prepare memories ahead of time instead of pasting them on the fly.”
“Today, even if you wanted to make your AI better, there’s almost nothing you can do. Your AI gets better when the model behind it gets better. How you use it has almost no effect. We are building towards a different future: the more you work with a model, the more it learns your world and the better it becomes for you.”
Dan Biderman, CEO and Co-Founder of Engram
“When an AI reads a 70,000-word legal contract, which is roughly 400 kilobytes of text, its internal memory of that document can swell past 100 gigabytes. That’s 250,000 times larger than the original file, and a huge part of what makes AI slow and expensive to run. We do that studying once, ahead of time, training the model to compress everything it learns into a compact memory it can reuse on every query.”
Sabri Eyuboglu, CTO and Co-Founder of Engram
“Our customers have built up extraordinary knowledge inside Microsoft 365, and we’ve only begun to tap what it can do for them. Engram’s approach could turn that knowledge into a kind of memory each organization owns and controls, while making AI efficient enough to power the long-running, proactive agents we believe every knowledge worker will eventually rely on. It’s the sort of frontier bet we want to be making.”
Jason Graefe, Corporate Vice President, AI Partner Catalyst at Microsoft
“Our enterprise customers are running long-lived agents across their Notion workspaces, and that kind of always-on work can burn through tokens fast, even for something as simple as triaging a task. We’re testing Engram’s models inside our new custom agents, and we’re already seeing them approach frontier quality while using an order of magnitude fewer tokens, because the agent already knows the workspace instead of rediscovering it on every query.”
Simon Last, Co-Founder of Notion
“Law firms and enterprises hold a lot of unique knowledge. Soon every employee will rely on agents that are adding millions of tokens per day of new context — faster than context windows or search can keep up. We’re working with Engram to build learned enterprise memories that are secure, cost-efficient, and turn unstructured context into compounding agentic knowledge bases.”
Gabe Pereyra, Co-Founder and President of Harvey
“Memory is the missing ingredient in AI. We see enormous potential for Engram’s technology across the companies we’re building and transforming in healthcare, legal, and financial services, where the institutional knowledge is deep and the cost of running AI against it is only growing. The ability to improve the speed, independence, and cost efficiency of agents is one of the most important things any company can deliver.”
Hemant Taneja, CEO of General Catalyst
“Most of the conversation around enterprise AI has focused on making models generally smarter. But for the companies actually deploying AI at scale, that was never the hard part. Getting a model to truly remember a specific organization and its unique ways of working is the problem nobody had convincingly solved. Dan, Sabri, Jessy, Jack, Scott and Chris have spent years on the research that finally makes persistent organizational memory possible, and they are now working to bring this to every AI-native company.”
Leigh Marie Braswell, Partner at Kleiner Perkins
