Engram raises $98 million for AI memory optimization to cut token costs
Engram, an eight-month-old AI memory startup, closed a $98 million funding round led by General Catalyst, Kleiner Perkins, and Sequoia, claiming models that match frontier lab performance using up to 100 times fewer tokens.
Engram closed a $98 million Series A led by General Catalyst, Kleiner Perkins, and Sequoia, with participation from OpenAI co-founder Andrej Karpathy, who recently joined Anthropic [1]. The thirteen-person startup, founded eight months ago, positions itself as the "learned memory" of AI, using models that recall organization-specific workflows to deliver responses at a fraction of standard token costs [1].
The company claims its models match or outperform frontier labs while consuming up to 100 times fewer tokens, the unit of currency for AI inference [1]. Engram has already signed Microsoft, Notion, and legal AI startup Harvey as clients, leveraging its memory-optimization approach to cut costs as enterprises face surging expenses from generative AI deployments [1]. Co-founder and CEO Dan Biderman, who holds a PhD in computational neuroscience from Columbia and worked at Stanford's AI lab, said the models excel at specialization rather than general-purpose tasks, prioritizing context retention over breadth [1].
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