Non-deterministic by nature
The same prompt returns different output every time. You cannot sign off once and assume it holds. Each call is a new decision that has to be governed and recorded.
Platform
End-to-end AI compliance,
from prompt to examiner.
Meilynx is the compliance system of record for AI in financial services, insurance, healthcare, and HR. One system enforces policy, governs agents, and produces examination-ready evidence.
audit chain · prompt → examiner
- seq 1042Request sealedprompt · policy context
- seq 1043Decision sealedPHI rule · redacted
- seq 1044Response sealedtokens · cost · outcome
→ Examination package
hash-linked · WORM archive · examiner-verifiable
Why AI traffic is different
A regulated data flow nothing in your stack was built to govern.
Each prompt and response is a fresh, non-deterministic decision that carries sensitive data across a third-party boundary. The controls you already run were not designed to see it, let alone prove to an examiner what happened.
Your most sensitive data, across a trust boundary
MNPI, client identifiers, and account data flow into prompts and out in responses, handed to a third-party model outside your walls on every call. Who saw what, and what came back, is exactly what an examiner asks.
Agents now act on their own
Tool calls, retries, and sub-agents take actions no human reviewed. Each one is a decision you have to be able to reconstruct and explain after the fact.
Your existing controls can't see it
DLP sees files, SIEM sees logs, APM sees latency. None of them read the prompt and response content where the risk, and the evidence, actually live.
AI traffic is a regulated data flow, and examiners have started asking about it.
SR 26-2 expects generative AI governed under model risk principles. NYDFS 500, FINRA 24-09, and the NAIC bulletin expect a record of what your AI did and how it was governed. Nothing in the standard stack produces one.
Why Meilynx
One system, not four.
A mid-sized firm shouldn't have to buy a model gateway, a DLP tool, an audit pipeline, and a model-inventory spreadsheet, then make them tell one story to an examiner.
Model gateway
routes, proves nothing
DLP tool
sees files, misses prompts
Audit pipeline
logs, unverifiable
Inventory spreadsheet
stale by exam day
The system of record
Meilynx
One system enforces policy, governs agents, and produces the examination-ready evidence: the layer that proves what your AI did, who governed it, and what backs the claim.
How it works
One position in the data path.
A single env-var change routes LLM traffic through the Meilynx proxy. From that one position it enforces policy pre-request and post-response, seals each decision into the audit chain, and attributes spend, while raw prompts and responses stay inside your perimeter.
Meilynx Proxy · Inside your perimeter
<50ms p90 · Shadow mode supportedHover a stage to inspect
Inbound
Request
Outbound
Provider
Audit trail
Analytical store · WORM archive · Cryptographic hash chain
Capturing · 6-year floor (Fully Managed)
Each request, response, and policy decision is captured to immutable storage in your environment. Examination-ready evidence that is never delegated to the control plane.
Meilynx Proxy · Inside your perimeter
Request
From your application
Policy
Model allow/deny · schema
PII / MNPI
Real-time detection
Cost
Per-request · budgets
Tools
Agent allow/deny
Provider
OpenAI · Anthropic · Azure · Google
Audit trail
Analytical store · WORM archive · Cryptographic hash chain
Capturing every call · 6-year floor (Fully Managed)
Shadow mode supported for safe rollout.
Rollout
Deployed in a day, examined on your schedule.
Fully Managed deploys in about a day; self-hosted in one to two weeks. Either way, the path from first request to examination package is the same three steps.
01
Change one env var
Point your LLM base URL at the proxy. Traffic flows through the data path the same day, with no SDK swap and no app rewrite.
02
Publish policies
Start from framework presets, draft in shadow mode, then publish signed bundles your compliance team can read.
03
Export evidence
The audit chain accumulates from the first request. Export a curated examination package whenever an examiner asks.
Architecture
Isolation is the architecture.
It is built into the deployment topology itself, in every mode.
- A per-customer isolated data plane in every deployment mode. Your data never shares a boundary with another organization's.
- Raw prompts and responses never leave your perimeter; only hashed, aggregate metadata reaches the shared control plane.
- The data plane owns your audit trail and its WORM archive.
Deployment modes
See the trust boundary and per-mode matrix
Who operates what in Fully Managed and Self-Hosted, and the isolation invariant that holds in both.
Capabilities
What the platform does.
Each capability stands on its own and feeds the same examination-ready audit trail.
Inline policy enforcementPre-request and post-response controls at the data path. One env-var change, no app rewrite.Examination-ready audit trailA tamper-evident, hash-chained record of every request that an examiner can verify.Agent & MCP governanceFull agent execution traced and tool calls allowed or denied at the boundary your traffic crosses.Cost & risk attributionEach dollar and each governance finding attributed to a team, a feature, and an outcome.Deployment modesManaged or self-hosted, with a per-customer isolated data plane in every mode.Framework coverageControls that map directly to SR 26-2, NYDFS 500, FINRA 24-09, the NAIC bulletin, and SOC 2, with the artifacts to prove it.
Framework coverage
Controls that map to named regulations.
Controls that map directly to the frameworks your examiner asks about.
By the numbers
Built for the data path.
Added latency, p95
<5ms
Measured under load, August 2026: 1.5M requests, zero failures, full detection stack on
LLM providers
4live
OpenAI · Anthropic · Google · Azure OpenAI
Retention floor
6yr
Fully Managed production, per FINRA 24-09
Change to deploy
1env var
No SDK swap, no app rewrite
Get started
See it on your own traffic
A 15-minute walkthrough of inline enforcement, the audit chain, and the examination package.
Response within 1 business day