Resource
RAG retrieves. AI memory compounds.
Retrieval-augmented generation helps an AI answer with external context. Shared AI memory goes further: it preserves decisions, preferences, documents, handoffs, and workflow state so organizational context can be reused over time.
| Dimension | RAG | Shared AI memory |
|---|---|---|
| Primary job | Find relevant existing context | Preserve and reuse operational context |
| Time horizon | Usually request-time | Compounds across days, teams, and workflows |
| Governance | Depends on index and app design | Memory lifecycle, validation, roles, and auditability |
| Best for | Answering questions from known sources | Continuity across decisions, handoffs, and repeated work |
The Achiral position
Achiral AI is not just a RAG wrapper. It is a shared AI memory for your business: a private memory layer that turns team activity into reusable operational context.
RAG is still useful. In Achiral, retrieval is one part of a larger memory system that can rank, reinforce, decay, validate, and act on context with human approval.
For the technical model, read the docs on ACT-R memory. For buying context, compare AI memory platforms.