The compliance system of record for AI in finance, insurance, healthcare, and HR.

Regulated firms now run generative AI somewhere: a copilot, an agent, a customer-facing feature. Very few of them can answer the examiner's questions about it cleanly. Which models are in use, how they are supervised, what stops sensitive data from leaking, and where the records are.

Every other material process at the firm has controls, owners, and an evidence trail. AI outran them, and examiners have started writing that up.

The durable value sits in governing AI and proving it. What an examiner wants is the control that stopped the request and the record showing it ran, so Meilynx produces both: policy enforced inline in the request path, and examination-ready evidence end to end.

That is what we mean by the compliance system of record: the one system that owns the audit layer an examiner cares about and can prove what your AI did.

Meilynx sits inline in the AI request path. From that one position it enforces policy before the request and after the response, seals every decision into a tamper-evident audit chain, and attributes cost and risk, while raw prompts and responses never leave your perimeter. Each deployment mode keeps your data in infrastructure dedicated to your organization.

That single position is a deliberate design choice. A control the request has to pass through cannot be sidestepped, and the record comes from the component that made the decision, which is what gives the evidence its weight.

Enforcement lives in the live request path, so the engineering standard follows from the position. The bar for correctness is high: a control either ran or it did not, and the record of it has to hold up under review years later. The latency budget is measured in milliseconds, and every change is held to it.

We write architecture decisions down before we build them, and every change passes review and automated gates before it reaches production. The product is precision, and that is the standard we hold our own work to.

• Inline by design

  Every control sits where the request has to pass. Enforcement and evidence come from the same component, at the same moment.

• Evidence over logs

  The audit chain is tamper-evident, and an examiner can verify its integrity independently of us.

• Isolation as the invariant

  Dedicated infrastructure for every customer and environment. Raw prompts and responses never leave your perimeter.

• Measured before claimed

  Latency and throughput figures come from load campaigns we run and repeat. A number we publish is one we can reproduce.

Meilynx is co-founded and built by engineers who have spent their careers on infrastructure that is not allowed to fail: at Google, Microsoft, and BlackRock. That background spans keeping global cloud infrastructure reliable and efficient at scale, leading platform and AI-enabled product engineering, and building analytics inside financial services.

It is a deliberately relevant mix. Governing AI in a regulated firm is an infrastructure problem before it is a compliance problem: the enforcement has to be in the data path, the latency budget is real, the audit trail has to hold up years later, and the people who sign off on it answer to a regulator. We have worked on each side of that line.

Meilynx is headquartered in Bellevue, Washington, and builds for compliance, risk, and security leaders in financial services, insurance, healthcare, and HR. Meilynx runs in production inside regulated firms. Our security and compliance posture, and how to reach our security team, are documented in the Trust Center.

We work closely with the teams who deploy us, from the first architecture conversation through examination. We are hiring across engineering and customer programs.

If governing AI well is on your desk, we would like to hear from you at hello@meilynx.com.