Centralize Architectural Standards Across AI Coding Workflows
Every team has their own .cursorrules, their own system prompts, their own interpretation of the standards. Mneme HQ gives you one structured decision corpus that all AI agents query, consistently.
Rules sprawl is the hidden cost of AI-assisted development.
Every team maintains separate Cursor Rules, CLAUDE.md, and system prompts
Fragmented rule files multiply across repos and teams. No team knows what the others have defined.
No single source of truth for what the AI "knows" about your architecture
Each agent operates on a different subset of standards. Outputs diverge in ways that are hard to trace back.
Prompt templates diverge over time: no versioning, no conflict resolution
Templates are copied, modified, and forgotten. There is no audit trail and no way to know which version is current.
Onboarding new teams to AI coding means re-documenting the same standards
Platform teams repeat the same setup work for every new team. There is no reusable distribution mechanism.
One corpus. Every agent. Every tool.
Single source of truth
project_memory.json is the canonical architecture source, queried by all agents. One file to maintain. One place to update.
Scoped rules
Payments team gets payments rules, analytics team gets analytics rules. Per-file pattern matching ensures each agent sees exactly what it needs.
Conflict resolution
The precedence engine resolves org-level vs team-level exceptions automatically. No manual arbitration. No ambiguity about which rule wins.
Universal enforcement
Claude Code hook integration means you install once and enforce everywhere. Standards propagate to every team without any per-team setup.
Built for platform and governance use cases.
AI Coding Assistant Governance
Enforce architectural standards across every AI coding assistant in your organization from a single decision corpus.
Read use case →Multi-Agent Workflow Governance
Maintain consistent standards across orchestrated multi-agent pipelines where multiple models collaborate on the same codebase.
Read use case →How platform teams roll this out
The deployment model platform engineering teams typically settle on has three tiers, each owned by different parts of the org but driven by the same decision corpus.
Tier 1 — org-wide policy. A small set of decisions every repo inherits: approved dependencies, security patterns, observability conventions. Owned by platform / security. Lives in a shared project_memory.json imported by each repo's local corpus.
Tier 2 — team architecture. Service boundaries, language and framework choices, data-access patterns. Owned by the team that operates the service. Lives in the repo's own project_memory.json with its own ADRs.
Tier 3 — per-feature exceptions. Tracked overrides for specific PRs, with rationale and expiry. Owned by the engineer making the change, reviewed in PR like any other diff. The override is a decision record itself, so weakening a constraint is visible.
Mneme's precedence engine resolves the three tiers deterministically: more-specific scope wins over less-specific scope, more-recent decision wins over superseded decision, explicit override wins for the duration of its expiry. The result is policy-as-code that is queryable by any agent, applied at the seam every agent eventually has to write to disk, and auditable in the same way as the rest of the codebase.