Enterprise AI memory

The RAG layer that gives memory to your AI agents

Argy Enterprise RAG grounds Argy Code, Argy Chat, and your agents on real enterprise knowledge: USER, PROJECT, ORG, TENANT scopes, dedicated collections, metadata filtering, audit, and RBAC.

Enable RAG Read docs

Useful links: Argy Code · multi-provider LLM Gateway · AI engineering platform · AI security governance · docs

Your organization accumulates decisions, patterns, errors and policies. Today that knowledge is scattered across wikis, emails and people's heads. Argy Enterprise RAG turns this collective knowledge into operational memory — accessible to your AI agents, filtered by access rights, audited and isolated by organization.

Multi-scope

Memory aligned with every governance level

Agents retrieve only the knowledge authorized for their execution context. Each scope is isolated, audited, and filterable.

USER

Personal memory

Preferences, habits, individual context, and usage history.

PROJECT

Project patterns

Conventions, technical decisions, known pitfalls, and reusable modules.

ORG

Organization memory

Team standards, internal policies, glossaries, and shared best practices.

TENANT

Enterprise knowledge

Cross-company corpus, governance documents, reference material, and long-term memory.

Collections & API

Query, save, delete, list — with metadata filters

Pre-configured system collections and custom collections for your business domains — for example architecture-decisions, compliance-policies.

API operations

rag query

Semantic search with citations, scores, and metadata filters.

rag save

Add knowledge, decisions, patterns, or document fragments.

rag delete

Controlled deletion by collection, scope, id, or retention policy.

rag list

Inventory collections and accessible content by role and tenant.

RAG query

{
  "collection": "your-project-docs",
  "scope": "PROJECT",
  "query": "NestJS DTO validation pattern",
  "filters": {
    "app": "api",
    "language": "typescript",
    "domain": "validation"
  }
}

Native in Argy Code

Agents consult memory before acting

Argy Code uses RAG to retrieve codebase patterns, user preferences, project decisions, and organization policies. The result: fewer repeated mistakes, better consistency, and changes aligned with doctrine.

Multi-specialist agents

Builder, Planner, Explorer, Reviewer, Sec-Auditor, and Browser share a common memory.

Checkpoint/resume

Long-running missions recover context, decisions, and partial statuses instead of starting over.

Metadata filtering

Filter by app, language, domain, scope, source, version, or any business metadata.

Grounding through LLM Gateway

Responses can be grounded on RAG while keeping multi-provider routing, quotas, and audit.

Tenant isolation

Every read and write respects your organization boundary, RBAC roles, and collection scope.

Audit & compliance

Trace RAG queries, collections consulted, applied policies, and citations used.

Reduce onboarding time by 60% — every new AI agent and developer instantly accesses the team's patterns and decisions.

Security and compliance

Governed RAG, not an isolated vector database

Argy Enterprise RAG combines RBAC, tenant isolation, audit trail, signed service calls, quotas, and retention policies. It naturally fits AI security governance.

Tenant isolation

RBAC

Audit trail

Signed and encrypted inter-service calls

Metadata filters

Retention policies

Citations

LLM Gateway grounding

European SaaS

GDPR compliant & hosted in EU

No Lock-in

Built on open standards

API-First

Everything is automatable

Ready to get started with Argy?

Start with the Free plan. Upgrade when you're ready, or contact us for an enterprise rollout.

Start for free Explore automations