The Living State
Your LLMs and agents forget everything between sessions.
T.E.M.P.R.A (Temporal Embedding Memory Projection Reconsolidation Architecture) encodes time as a native geometric coordinate inside the embedding space —
not a filter, not metadata. The first true cognitive memory for AI models.
Every enterprise deploying LLMs faces the same wall: stateless models, zero contextual memory, no temporal coherence. TEMPRA solves this at the architecture level — not the prompt level.
RAG, summaries, temporal filters — these are patches. None of them treat time as a fundamental dimension of reasoning. TEMPRA changes the architecture, not the prompt.
Semantic embedding + timestamp filter in post-retrieval. Time is external metadata — the model doesn't "feel" temporal distance, it reads a date.
Mem0, MemGPT, Zep — all built on this logic. Recency is computed after the fact, not integrated into the geometry of the representation space.
τ is encoded as a multi-scale sinusoidal coordinate directly inside the vector. Time becomes a dimension of ℝ¹⁶³² — not a filter, not a sort.
Temporally coherent retrieval without manual rules. Cosine similarity naturally integrates temporal distance. Demonstrable delta vs semantic baseline.
From message reception to memory reconsolidation. Each step produces a traceable, auditable state — deployable on-premise within your infrastructure.
On-premise, data sovereign, no infrastructure rebuild.
Your LLMs and agents get a cognitive self — persistent, temporally coherent memory.
TEMPRA is proprietary infrastructure — not a consulting engagement, not an agency build. We're raising a pre-seed to ship the MVP, and partnering with enterprises that want to run it on their own stack.
We talk about your concrete problem — and in few minutes we'll know if TEMPRA is the right fit.
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