Agent memory that updates knowledge without deleting it, can answer "what did we know, and when", and gives every AI agent the same company memory.
An Agent Should Know Not Just What Is True, but When It Was True
Most AI memory either forgets everything or piles everything up. Both end the same way: the agent mistakes an old decision for today's truth. In APEX we built memory as bi-temporal. New knowledge does not delete the old, it supersedes it; the old record stays archived, knowing what replaced it. Even "what did we know in March?" has an answer.
Remembering is selective, too. Near-duplicates are never written twice. At retrieval, semantic similarity, importance and recency are scored together; recency decays exponentially, so an old but important decision is not lost. Knowledge with a retention period drops out on its own when it expires, and because embeddings are generated locally, the memory never leaves the company.
The memory belongs to no single agent. It is served over MCP; conversation, coding and content agents read from and write to the same brain. The same server also connects to Linear and Google Drive. Every call leaves an audit record and a trace with personal data masked.
How It Works
- Bi-temporal records: supersede, version chain, never delete
- Deduplication that never writes a near-duplicate twice
- Re-ranking by meaning + importance + exponential recency
- Retention-based forgetting for KVKK; local embeddings
- MCP server: recall, remember, context; Linear and Drive tools
- OAuth 2.1 access, an audit record and trace on every call
Hybrid Retrieval
We use the same thinking in product search. In Hardiye, precise requests such as price, flavour or weight go to a structured query in milliseconds; vague ones like "something for after training" go to semantic search. Both sit behind a single tool — the real-time model never has to decide which to use.
Where We Use It
In APEX, Unitic's own collective AI brain, and in Hardiye's product search.
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