Building the memory layer for AI
We believe AI agents deserve real memory — not just vector search. Memory that encodes, consolidates, reasons, and explains.
Our Mission
Current AI memory solutions are glorified vector databases — they store embeddings and return the nearest match. But real memory is richer: it encodes through multiple pathways, strengthens over time, links causes to effects, and explains itself.
Engramma brings cognitive science principles to AI infrastructure. We're building the memory engine that lets agents learn, reason, and remember — so developers can focus on building great products.
What We Believe
Memory should think
Storage without understanding is just a database. We build systems that reason about what they remember.
Explainability by default
Every operation produces a full trace. No black boxes. If you can't explain it, you can't trust it.
Developer-first
Clean APIs, great docs, predictable pricing. We build for engineers who ship production AI.
Privacy as architecture
Multi-tenant isolation, GDPR compliance, and data sovereignty are core design constraints — not afterthoughts.
Timeline
Research begins
Initial research into multi-pathway memory systems and causal reasoning for AI agents.
Prototype validated
First proof-of-concept: 10-phase pipeline with confidence routing outperforms pure vector search by 3x on recall tasks.
Beta launch
Private beta with select partners. Validated multi-tenant architecture and consolidation cycles.
Public launch
General availability with Free, Starter, Pro, Scale, and Enterprise tiers. SDK + full API.
Scale milestone
Processing 10M+ patterns across all tenants. p95 latency under 50ms maintained.
Research Foundations
Engramma's architecture draws from established cognitive science and machine learning research:
Want to work with us?
We're always looking for engineers and researchers passionate about AI memory.