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Applies to v3.10.0Reviewed 2026-06-04

AI memory

Achiral AI is a shared AI memory for your business - a native AI harness that turns everything your team does into compounding intelligence, learns your operations inside out, and gets sharper over time.

This section explains the memory layer behind Chiro and personal executive assistants. It covers what gets remembered, how retrieval works, why memory is different from ordinary RAG, and how teams keep durable context under human control.

What memory means in Achiral

Achiral memory is organization-scoped operational context. It can come from conversations, documents, decisions, tasks, connectors, preferences, and repeated workflows. The goal is not to store every token forever. The goal is to preserve context that helps the organization act with continuity.

The memory layer supports:

  • Episodic memory for recent conversations, events, and work activity.
  • Semantic memory for decisions, preferences, process knowledge, and domain facts.
  • Core memory for durable context that has been reviewed and approved.

How it differs from RAG

Retrieval-augmented generation retrieves context for an answer. AI memory adds a lifecycle around that context: formation, retrieval, reinforcement, decay, validation, and auditability.

DimensionRAGAchiral memory
Primary jobRetrieve relevant contentPreserve and reuse operational context
Time horizonUsually request-timeCompounds across days, people, and workflows
GovernanceDepends on the appTenant boundaries, roles, validation, and audit logs
Best fitAnswering from known sourcesMaintaining continuity across repeated work

Why shared memory matters

Most AI tools are strongest inside a session. Businesses need context to survive outside a session: who promised what, why a workflow changed, what a customer was told, which exception was approved, and what still needs follow-up.

Shared memory gives Chiro an organization-level view while personal assistants keep private user context separate. The result is a system that can help the whole team without flattening privacy boundaries.