Earnie
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AI writes your code now. Who's checking it?

The agent-native control plane over every line of code, whether it's written by a human or an AI agent. Keep track of all your AI models, cryptography and open source components. A deterministic record you can trust.

Request a demo EVIDENCE, EARNED.▌
Earnie launch recording Missed the launch? Watch it here.

Code used to have two authors.
Now it has three.

Humans

The engineer at the keyboard. Reviewed, mentored, accountable.

Open source

Packages, snippets, vendored code. Licences and obligations attached.

AI agents

They're faster than your review, and never learned your policy.

Now one policy governs all three.

AI is fast.
Earnie is faster.

Your agents write code faster than your reviews can read it. Earnie is already ahead: it knows your policy before the agent starts typing, and checks the result in context via MCP, at pre-commit and at merge.

WORKS WITH
MCP Claude Code OpenAI Cursor GitHub GitLab
MCP · CLI · PRE-COMMIT · GITHUB · GITLAB

Earnie works where the work happens.

MCP
BEFORE THE CODE IS WRITTEN
EARNIE · MCP SESSION · LIVE 
can I use fast-charts@4.2.1 in checkout-service?
↳ Checked against checkout-service policy.
↳ fast-charts@4.2.1 is AGPL-3.0, disallowed by checkout-service policy.
↳ FND-2481 blocked. 2 MIT-licensed alternatives suggested.
CLI
AT PRE-COMMIT, AT THE KEYBOARD
EARNIE · CLI · PRE-COMMIT 
git commit -m "add charting"
↳ earnie: 1 policy violation in staged changes
↳ FND-2481 · fast-charts@4.2.1 · AGPL-3.0 · policy: no-copyleft
↳ Commit blocked. Run `earnie explain FND-2481` for the evidence.
CI/CD
AT MERGE, ON EVERY COMMIT
EARNIE · CI · PR #312
PR #312 · checkout-service
CHECK FAILED
FND-2481 · unresolved · merge gated until policy passes
UI
AFTER THE FACT, EVIDENCE THAT HOLDS
HANDLED AUTOMATICALLY
FND-2481 · fast-charts@4.2.1 · AGPL-3.0
Blocked at: MCP · pre-commit · PR #312
Evidence: 3 entries, linked to source
Earnie MCP setup in a terminal: Claude Code, Cursor and Codex detected and selected as MCP clients

Results you can trust.
Results regulators can audit.

Earnie's findings are deterministic, built on hard, reliable data. The same code produces the same result, every time.

SAME INPUT. SAME RESULT. EVERY TIME.

Always-on governance for

AI models

Detection over declaration. AI SDKs, model files and API keys found, matched against known models and held to your policy. Documented in an AIBOM with provenance and licence. What's really there, in writing.

Cryptography & PQC

Every cryptographic algorithm in your estate, inventoried and checked against policy, with flags on what won't survive the post-quantum transition. The migration starts with an inventory. Earnie keeps it standing.

OSS licence compliance

Components, licences, obligations, down to the snippet someone pasted. Policy applied before it ships. Attribution and notice files generated, SBOMs versioned and retrievable. The record, ready before anyone asks.

Security vulnerabilities

Known vulnerabilities in what you actually ship, found where they live, tied to remediation at AI speed. The fix arrives with the finding, usually one click away.

The programme we built

Detect

Open source, AI models, cryptography and vulnerabilities, caught wherever they enter: in the agent's session, at the keyboard, at merge.

Report

SBOMs, CBOMs, AIBOMs and notice files, generated from what's actually in the code. Versioned and retrievable.

Remediate

Your policy, in your agent's context. With the rules and the evidence in hand, it writes secure, compliant code the first time.

Maintain

Always-on governance. Every project, every commit, the same policy. Records on demand.

The Earnie UI: findings filtered by algorithm, a weak hash flagged in the code, the matching policy and its audit trail

Evidence, earned.

Earnie: always-on code governance
Earnie
Menu
ProductDevelopersTrustField notesDocs ↗AboutRequest a demo

AI writes your code now. Who's checking it?

The agent-native control plane over every line of code, whether it's written by a human or an AI agent. Keep track of all your AI models, cryptography and open source components. A deterministic record you can trust.

Request a demo EVIDENCE, EARNED.▌
✓
!
Earnie launch recording Missed the launch? Watch it here.

Code used to have two authors.
Now it has three.

Humans

The engineer at the keyboard. Reviewed, mentored, accountable.

Open source

Packages, snippets, vendored code. Licences and obligations attached.

AI agents

They're faster than your review, and never learned your policy.

Now one policy governs all three.

AI is fast.
Earnie is faster.

Your agents write code faster than your reviews can read it. Earnie is already ahead: it knows your policy before the agent starts typing, and checks the result in context via MCP, at pre-commit and at merge.

WORKS WITH
MCP Claude Code OpenAI Cursor GitHub GitLab
MCP · CLI · PRE-COMMIT · GITHUB · GITLAB

Earnie works where the work happens.

MCP
BEFORE THE CODE IS WRITTEN
EARNIE · MCP SESSION · LIVE 
can I use fast-charts@4.2.1 in checkout-service?
↳ Checked against checkout-service policy.
↳ fast-charts@4.2.1 is AGPL-3.0, disallowed by checkout-service policy.
↳ FND-2481 blocked. 2 MIT-licensed alternatives suggested.
CLI
AT PRE-COMMIT, AT THE KEYBOARD
EARNIE · CLI · PRE-COMMIT 
git commit -m "add charting"
↳ earnie: 1 policy violation in staged changes
↳ FND-2481 · fast-charts@4.2.1 · AGPL-3.0 · policy: no-copyleft
↳ Commit blocked. Run `earnie explain FND-2481` for the evidence.
CI/CD
AT MERGE, ON EVERY COMMIT
EARNIE · CI · PR #312
PR #312 · checkout-service
CHECK FAILED
FND-2481 · unresolved · merge gated until policy passes
UI
AFTER THE FACT, EVIDENCE THAT HOLDS
HANDLED AUTOMATICALLY
FND-2481 · fast-charts@4.2.1 · AGPL-3.0
Blocked at: MCP · pre-commit · PR #312
Evidence: 3 entries, linked to source
Earnie MCP setup in a terminal: Claude Code, Cursor and Codex detected and selected as MCP clients

Results you can trust.
Results regulators can audit.

Earnie's findings are deterministic, built on hard, reliable data. The same code produces the same result, every time.

SAME INPUT. SAME RESULT. EVERY TIME.

Always-on governance for

AI models

Detection over declaration. AI SDKs, model files and API keys found, matched against known models and held to your policy. Documented in an AIBOM with provenance and licence. What's really there, in writing.

Cryptography & PQC

Every cryptographic algorithm in your estate, inventoried and checked against policy, with flags on what won't survive the post-quantum transition. The migration starts with an inventory. Earnie keeps it standing.

OSS licence compliance

Components, licences, obligations, down to the snippet someone pasted. Policy applied before it ships. Attribution and notice files generated, SBOMs versioned and retrievable. The record, ready before anyone asks.

Security vulnerabilities

Known vulnerabilities in what you actually ship, found where they live, tied to remediation at AI speed. The fix arrives with the finding, usually one click away.

The programme we built

Detect

Open source, AI models, cryptography and vulnerabilities, caught wherever they enter: in the agent's session, at the keyboard, at merge.

Report

SBOMs, CBOMs, AIBOMs and notice files, generated from what's actually in the code. Versioned and retrievable.

Remediate

Your policy, in your agent's context. With the rules and the evidence in hand, it writes secure, compliant code the first time.

Maintain

Always-on governance. Every project, every commit, the same policy. Records on demand.

The Earnie UI: findings filtered by algorithm, a weak hash flagged in the code, the matching policy and its audit trail

Evidence, earned.

Request a demo