01 / CONTEXT
Every conversation starts over
Important decisions and preferences disappear between runs, forcing people to repeat themselves and agents to guess.
A private, searchable memory layer for production AI agents—scoped by user, agent, and run, with provenance you can inspect and refreshes you can trust.
Adding a vector database is not the same as building memory you can operate. The hard part is scope, promotion, provenance, refresh safety, and knowing what the agent actually used.
Important decisions and preferences disappear between runs, forcing people to repeat themselves and agents to guess.
Without explicit boundaries, private, shared, and temporary context become one undifferentiated memory pool.
Teams cannot tell where a result came from, whether it is stale, or whether a refresh changed the wrong system.
We connect durable agent memory to a controlled PostgreSQL/pgvector foundation, then add the knowledge and operational safeguards that make it usable in production.
The result is not just “more context.” It is memory that can be scoped, promoted, searched, refreshed, inspected, and handed to an engineering team without mystery.
Open the interactive walkthrough →Private, shared, and run-scoped memory with explicit promotion instead of accidental sharing.
Index internal Wiki, procedures, and code with source-aware retrieval that gives the answer a trail.
Deterministic, idempotent refreshes with a hard boundary around production memory.
Review agents, data flows, scopes, retrieval, and failure modes.
Install the controlled memory and knowledge layer around your workflows.
Test isolation, provenance, idempotence, and production safety before launch.
Monitor refreshes, tune retrieval, and evolve the system as agents change.
Fixed-scope starting points. Larger multi-agent or compliance-heavy deployments are scoped after the audit.
Know what to fix before you build another memory system.
The production-ready memory and knowledge layer.
Keep memory useful, safe, and aligned as your agents evolve.
No. The memory layer sits alongside your agent stack. GaleOps keeps the working production provider intact and adds controlled memory and knowledge capabilities around it.
The reference architecture uses PostgreSQL with pgvector in the customer-controlled environment. The exact deployment and retention model are set during the audit.
Yes, when explicitly designed and promoted. Private and run-scoped memory remains isolated by default.
We run a production canary, verify provenance and idempotence, and record a before/after check showing that the production memory table was not modified by the knowledge refresh.
Bring your current agent workflow. We’ll map the smallest safe path from “it forgets” to “we can run it.”
Book a 15-minute architecture call →