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Introducing Meilynx

Why we're building Meilynx, what it is today, what's coming, and how regulated teams can join the design partner program.

Cassio MeloCassio MeloCo-Founder3 min readGovernance

We started Meilynx because Julia and I kept running into the same pattern from different sides of the problem.

Julia had spent years building and leading engineering teams in finance analytics, where model adoption only works if risk, controls, lineage, and auditability are part of the system. I had spent years on large-scale infrastructure and safety work at Google, where reliability and efficiency are not afterthoughts; they are operating constraints.

When we spoke with teams in banks, hedge funds, healthcare platforms, and other regulated environments, the issue was rarely enthusiasm for AI. People wanted to use LLMs in production. The blocker was that security could not express the controls, finance could not see or forecast the spend, and platform teams were being asked to approve systems without the audit trail their regulators would expect.

The tools they had tried usually solved one side of the problem: governance without cost visibility, cost dashboards without enforcement, or generic AI gateways that were not built for regulated workflows. Meilynx is our answer to that gap: governance and cost observability for AI workloads, built for the auditors a CISO actually has to sit across from.

What it is

Meilynx sits between your applications and your LLM providers and enforces policy inline — model allowlists, cost caps, content rules, and an audit trail an examiner can read. You change one environment variable in your application; nothing else changes.

Most AI governance tools focus on the security layer or the cost layer. We think regulated teams need both in one place, with the audit trail an examiner expects.

A piece of policy is just a file. Here's what allowlisting models and capping per-request spend looks like:

# governance.yaml
validators:
  - name: model-allowlist
    apply_to: request
    models:
      - claude-opus-4-7
      - claude-sonnet-4-6
      - gpt-5-mini
  - name: cost-cap
    apply_to: request
    max_usd_per_request: 2.50
    period_caps:
      daily_usd: 250.00

Compliant. Auditable. Under control.

What's coming

A few things on the near roadmap:

  • Expanded compliance pack coverage. Preset bundles ship today for SR 11-7, NYDFS 500, FINRA 24-09, and SOC 2 Type II. HIPAA, EU AI Act, ISO 42001, and NIST AI RMF are configurable now and getting curated bundles next.
  • Broader deployment options. Managed and Self-Hosted are live. Bring Your Storage, where audit data lands in your own infrastructure while we operate the proxy, comes online next.
  • Deeper FinOps correlation. Beyond per-request cost caps, we're working on tying spend to outcomes — which workflows are economically viable, which aren't, and how that shifts as models reprice or change capability.

Design partner program

The design partner program is open. We're working closely with a small number of teams in financial services, healthcare, and other regulated industries running production AI workloads — CISOs, FinOps leads, and the platform teams who carry the weight of getting AI into production safely.

Partners get early access, direct input on the roadmap, and pilot pricing.

If that's you, write to partners@meilynx.com.

FAQ

Frequently asked

Where does Meilynx run?

In every deployment mode, Meilynx runs in per-customer isolated infrastructure — your data is never mixed with another customer's. Managed: we operate the proxy in a per-customer environment, ~1 day to deploy. Bring Your Storage (BYOS): audit data lands in your storage; we operate the proxy. Self-Hosted: you operate the proxy in your environment, 1–2 weeks to deploy.

Is the proxy open source?

The proxy is going Apache 2.0 at SOC 2 GA — before our first paying customer. Design partners get source access today under mutual NDA.

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