Engram just raised $98M because your enterprise AI is burning 100x more tokens than it needs to

Your enterprise AI is a genius stranger.

It can synthesize anything. But it rereads the same documents, relearns the same context, and rediscovers the same institutional knowledge on every single query.

Engram just emerged from stealth with $98M from Sequoia, Kleiner Perkins, and Andrej Karpathy to fix that.

Their bet: the biggest problem in enterprise AI isn't model quality. It's cost.

Every token your models ingest to maintain context is money leaving your budget. Engram's "learned memory" layer lets models study your organization once and remember it forever.

The result: they claim up to 100x fewer tokens while matching or beating frontier models on enterprise tasks.

Microsoft is already testing it inside M365. Notion and Harvey are integrating it into their platforms.

13 employees. $98M raised. The investors are betting that token efficiency becomes the next battleground after model capability.

If your AI budget is growing faster than your AI output, this is the inflection point. The companies that solve organizational memory will cut their inference costs by orders of magnitude. The ones that don't will keep burning cash on models that learn nothing between sessions.

Audit your AI token spend today. If your models are reprocessing the same context on every query, you're paying for compute that a memory layer would eliminate.