For CTOs & VP Engineering

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

Teams shipping 10× more AI-generated code with the same number of reviewers accumulate architectural debt at a pace that's invisible until it's expensive. Mneme HQ enforces architectural decisions before the code is written.

By Theo Valmis · Published Apr 2026 · Updated May 2026

Talk to the founder See use cases
The Problem

## The bottleneck isn't generation. It's governance.

AI coding assistants have eliminated the typing bottleneck. They haven't eliminated the governance bottleneck. Senior engineers are still the gate — reviewing more PRs, catching the same violations, and explaining the same decisions to tools that have no memory of them.

01

#### AI output volume exceeding review capacity

Assistants generate code faster than reviewers can validate it. The queue grows. Shortcuts follow.

02

#### Architectural inconsistency across teams

Different engineers use different tools with different context. The codebase fragments.

03

#### Drift compounding into technical debt

Each small violation is harmless. At scale, they harden into structural problems that are expensive to unwind.

04

#### Repeated PR corrections consuming senior time

The same violations surface sprint after sprint. Reviewers explain decisions that were already made. Time compounds.

Why Existing Approaches Don't Scale

## Rules files, prompts, and RAG all break at the same point.

The common thread: each approach depends on a human keeping something up to date, or a model remembering something it was told once. Neither scales with your decision history.

Approach | Why It Breaks at Scale | Mneme HQ
--- | --- | ---
Rules Files | Flat text decays as teams add more rules. No semantic retrieval. Context limit hit as the project grows. | Structured decision store — only relevant decisions are surfaced at query time
Prompt Templates | Static. Can't cover a growing decision history. Manually maintained and easily out of date. | Decisions retrieved dynamically — the constraint set stays current without manual curation
RAG | Documents aren't decisions. Retrieves text, not enforceable rules. No violation detection layer. Why RAG fails → | Enforcement layer on top of retrieval — checks for violations, not just relevance
Code Review | Catches violations after code is written. Wastes senior engineer time on avoidable corrections. Why code review doesn't scale → | Pre-flight check surfaces violations before generation — review stays for judgment, not repetition

How It Works

## Architectural decisions become rules. Rules become a gate.

Your team's architectural decisions — service boundaries, data handling, vendor choices, naming and review standards — live as structured records in the same repository as the code. Every AI coding session, in any tool your engineers use, reads from that single source before it generates anything.

When a generation would violate a decision, the violation is surfaced before the change lands — not after a senior engineer has spent twenty minutes catching it in a PR. When a decision changes, you update it once, and every AI agent across the org picks it up on the next session. No per-tool configuration sprawl. No drift between teams using different assistants.

Mneme HQ is the layer between your architectural standards and the AI-generated code that has to honor them. It is the only place a decision needs to live.

Business Outcomes

## Where the budget actually moves.

Adding AI coding tools without governance shifts cost from typing to reviewing — and from this quarter to the next two. Mneme HQ is what stops that line item from growing.

ROI 01

#### Senior engineering hours back on the calendar

Drift caught pre-generation is drift the principal engineer never has to explain in PR review for the third time. Hours that compounded out of the team return to it — without adding headcount.

ROI 02

#### Throughput without architectural debt

The point of AI tooling is more output per engineer. The unspoken cost is the technical debt that comes with it. Governance keeps the throughput and removes the rework cycle.

ROI 03

#### One governance layer instead of N rules files

Cursor rules, Copilot instructions, Claude memory, internal SDK agents — every tool your team adopts compounds the maintenance burden. Mneme replaces the sprawl with a single artifact.

ROI 04

#### Audit-ready by construction

Every architectural decision and every override is a structured, version-controlled record. Compliance, security, and architecture review read from the same artifact engineering already maintains.

Where this is going. The public roadmap covers enforcement modes, the standards landscape (NIST CAISI, MCP, AGENTS.md), and the integrations queued for the next quarters — the same view your platform team will plan against.

See the roadmap →
Proof

## Deterministic. Tested. Production-ready.

Mneme HQ is not a prototype. The enforcement pipeline is validated against a deterministic test harness with full coverage of the violation detection logic.

170
integration tests covering deterministic enforcement — violation detection, scope matching, precedence resolution, and CI gate behavior. See the demo →