Control and privacy

Your code stays inside the perimeter. So does the AI.

Cardumen runs local or on-prem with a signed license. No exfiltration, no surprise quota and no vendor dependency.

Cardumen coordinates five agents over the same AGENTS.md inside your network. Memory, skills and verify are versioned in the repo. Local inference, offline Ed25519 licensing and auditable hooks.

Operational pain

Where energy is lost

Your product handles NDA, regulated or compliance-bound data. Every time a developer opens Claude or Cursor, part of the product context travels to a chat your compliance team cannot audit. The board asks what AI is worth on the roadmap and you cannot measure it because the bill arrives in tokens, not features. The vendor raised prices last month and notified you by email. You added juniors who use personal accounts for official product work.

Cardumen response

How it is fixed

Cardumen keeps memory, skills and verification inside the repo and your network. The skill router is distributed encrypted and decrypted per session with your offline-signed Ed25519 license: nothing starts without a valid license. Five agents coordinate over the same AGENTS.md, hooks audit every tool call and code never leaves the perimeter. Change model or vendor without rewriting the workflow.

Expected outcomes

Visible impact without changing your stack.

Zero exfiltration: local or on-prem inference, without vendor-cloud tool calls.

Architecture, security and QA criteria versioned in the repo, not personal chats.

AI ROI measured by tasks closed with evidence, not tokens burned.

Compliance-ready: traceable hooks, signed license and executable policy.

No vendor lock-in: change model or provider without rewriting the system.

Fit signals

When this route makes sense.

Your product code is under NDA, regulation or compliance.

An audit asks where code goes and what each AI does with it.

The board or Risk team asks for cost and single-provider control.

Your vendor announced another repricing and you want out of that dependency.

You bought a premium subscription and juniors use personal accounts for product work.

Practical route

Gradual adoption, with evidence.

01

Define shared rules

Architecture, security, QA and product criteria are written and versioned: humans and agents read the same thing.

02

Isolate memory by repo

Each product keeps its context inside the repo. No vendor-cloud history and no personal chats.

03

Measure adoption rigorously

Cost, quality and speed become verifiable metrics tied to verify evidence, not perception.

Next step

Protect the repo without slowing AI.

We activate a local or on-prem pilot with your license server. Your team uses AI; your code stays inside the perimeter.