Ideal customer profile

# Who Mneme Is For

Mneme is for teams that already have architectural decisions, standards, or repo constraints they need AI coding tools to respect. It is strongest when engineering teams are using Cursor, Claude Code, GitHub Copilot, or agent workflows to accelerate development, but still need deterministic governance over what gets generated.

Mneme is not a generic code review bot, chatbot safety platform, or runtime agent monitoring tool. It focuses on architectural governance for AI-assisted software development.

Strong fit

## Best-fit teams

Platform engineering
Platform Engineering Teams

Teams maintaining shared APIs, internal developer platforms, or shared infrastructure that define architectural standards AI agents can unintentionally bypass.

Data & infrastructure
Backend and Data Platform Teams

Teams owning data pipelines, backend services, or shared infrastructure where consistency across AI-generated code directly affects reliability and standards adherence.

AI-native product
AI-Native Product Engineering Teams

Product teams using Cursor, Claude Code, Copilot, or agentic workflows daily, where architectural governance has not yet caught up with generation speed.

Architecture & enablement
Architecture Guilds and Enablement Teams

Cross-functional groups responsible for ADRs, standards, and engineering enablement who need those decisions to propagate into AI coding workflows without manual enforcement.

Target scenarios

## Example scenarios

These are illustrative target scenarios, not customer case studies. They reflect the governance problems Mneme is designed to solve.

Fintech platform team

For example, a fintech platform team could enforce approved data access patterns across AI-generated service code, preventing agents from introducing unapproved direct database calls or bypassing the existing repository abstraction layer.

Data platform team

A data platform team could keep AI-assisted pipeline work aligned with existing ingestion standards, preventing agents from generating schema migrations that violate partition strategies or naming conventions already defined in their ADRs.

Enterprise SaaS modernization

An enterprise SaaS team modernizing a legacy monolith with AI coding assistance could ensure generated code respects module boundaries and does not reintroduce patterns the architecture has already moved away from.

Internal tools team

An internal tools team could enforce observability, logging, and security conventions across AI-generated code, keeping changes aligned with org-wide engineering standards without relying on manual PR review to catch every deviation.

Less ideal

## Less ideal fits

- Solo projects without architectural complexity
- Teams only looking for generic PR code review
- Runtime AI safety or chatbot governance use cases
- Pure frontend teams with little architectural constraint enforcement

## Think your team fits?

If your team is already using AI coding tools and architectural review is becoming harder to scale, request a pilot.

Request a Mneme pilot →
See use cases → View benchmark → GitHub →
Browse by role
For CTOs & VP Engineering

### AI Increases Code Throughput. Your Review Capacity Does Not.

The bottleneck for AI-assisted teams isn't generation — it's governance. Enforce architectural standards before code reaches review and stop trading throughput for drift.

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For Platform & DevEx Teams

### Centralize Architectural Standards Across AI Coding Workflows

One decision corpus. Every agent. Every tool. Three-tier rollout — org policy, team architecture, per-feature override — with deterministic precedence resolution.

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For Staff & Principal Engineers

### Stop Repeating Architectural Decisions in Every PR Review

Record an architectural decision once and have it enforced before AI writes the code. No more leaving the same review comment on the third agent of the week.

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## Continue reading

The same governance layer, viewed from adjacent angles — reference architectures, integrations, and the conceptual case for governance before generation.

Use cases
Reference architectures
How teams apply pre-generation governance across coding assistants, multi-agent pipelines, security, data, and design systems.
Integrations
Mneme across coding agents
Cursor, Claude Code, GitHub Actions, and more — one governance layer, every surface.
Insight
Why prompt engineering is not governance
Prompts steer; governance enforces. Why architectural integrity needs deterministic constraints.
Insight
Governance across heterogeneous coding agents
Why one governance layer beats per-agent rules files when your team uses more than one coding tool.
Standards
The agent governance landscape
NIST CAISI, Model Context Protocol, AGENTS.md — the foundations of a future cross-tool governance standard.
Benchmark
Governance benchmark methodology
Pass/fail examples and deterministic enforcement measured across simulated AI development workflows.