AI compliance platform · Financial services

Prove your AI is compliant.To any examiner.

From prompt to examiner — one audit chain. The end-to-end AI compliance system for finance: inline policy enforcement, agent and MCP governance, and a tamper-evident audit trail — inside your perimeter.

Direct control mapping:SR 11-7NYDFS 23 NYCRR 500FINRA 24-09MNPI & PII detectionmodel risk inventory

AI traffic interception · proxyready

prompt → "

Custom policies

organizational rules · allow/deny

PII / MNPI

detect · redact at request layer

Agent / MCP interception

tool calls · agent calls inspected

Budget

per-project|customer|department cost enforced

LLM provider

Anthropic · OpenAI · Azure

downstream

tamper-evident audit chain

every request sealed & hash-linked — even blocked ones

allowed

9f3a1c

blocked

7e02b4

Examination package

sealing…

What makes AI traffic unique

A regulated data flow nothing in your stack was built to govern.

Every prompt and response is a new, non-deterministic decision that carries your most sensitive data across a third-party boundary. Your existing controls weren't designed to see it — let alone prove what happened to an examiner.

Non-deterministic by nature

The same prompt returns different output every time. You can't sign off once and assume it holds — every call is a new decision that has to be governed and recorded.

Your most sensitive data, in motion

MNPI, client identifiers, and account data flow into prompts and leave in responses. The regulated data you guard at rest now moves on every single call.

It crosses a trust boundary every time

Each request hands data to a third-party model outside your walls. That hand-off — who saw what, and what came back — is exactly what an examiner asks about.

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 semantics where the actual risk — and the actual evidence — lives.

AI traffic is a regulated data flow — and examiners have started asking about it.

SR 11-7 now reaches model risk in LLMs; NYDFS 500 and FINRA 24-09 expect a record of what your AI did and how it was governed. Nothing in the standard stack produces one.

What it does

Comply. Govern. Optimize.

Three integrated capabilities, one deployment. Compliance leads — every policy decision and agent action feeds a tamper-evident audit trail — and each pillar runs at the proxy, in your dedicated infrastructure, managed or self-hosted.

01 / Comply

Audit trail your examiners will accept.

Every request, response, policy decision, and human review is captured to immutable storage in your environment. Controls map directly to named regulations—not generic ‘best practice’.

Tamper-evident hash chain
WORM archive
6-year retention floor (Fully Managed, FINRA 24-09)
Examination package export
Reviewer & sign-off workflows

02 / Govern

Policy enforcement at the request layer.

Three-tier detection (regex, ML classifier, LLM judge) inspects every prompt and response in flight. Block, redact, or log based on policy—per team, per app, per model.

Model allow / deny lists
PII, MNPI, PHI detection
Prompt injection & jailbreak
Per-tenant policy isolation
Block, redact, or shadow mode

03 / Optimize

Cost correlated to outcomes & risk.

The only platform that ties LLM spend to business outcomes and compliance events in a single view. Risk-adjusted spend by team, app, and model—not just raw token bills.

Per-team budgets & enforcement
Outcome ingestion API
Model routing recommendations
Anomaly detection on spend
Compliance events as outcomes

See every inspection stage in the data path

What makes Meilynx unique

Examiner-grade, not log-grade.

Most tools can log what your AI did. Meilynx proves it — with a tamper-evident record an examiner can independently verify, inside infrastructure that is yours alone.

Capability	Meilynx	DLP / CASB	Build it yourself
Inline enforcement at the request layer — before the call leaves your perimeter	Yes	Partial	Partial
Reads prompt & response content — PII and MNPI, not just files or metadata	Yes	Partial	Partial
Tamper-evident WORM record — not just a log export	Yes
Curated examiner packages — SR 11-7 · NYDFS 500 · FINRA 24-09 · SOC 2	Yes
Integrity an outside auditor can re-verify, independently	Yes
Per-customer isolated infrastructure — never shared with another institution	Yes	Partial	Partial

Category comparison, not a product-by-product rebuttal. Most tools can log what your AI did; the bottom three rows are where examination evidence is either produced — or it isn't.

Isolation by design

Your data never shares a boundary.

Your proxy runs inside infrastructure dedicated to your organization. Raw prompts and responses never leave your perimeter — only hashed metadata does, never payload. Your financial data never sits alongside another institution's. That isn't a setting; it's the architecture.

Verify us — don't take our word

Integrity you can re-check yourself.

The audit trail is tamper-evident and its integrity is independently verifiable. An examiner or your own auditor can re-compute the hash chain with an open verifier, at any time — the proof doesn't depend on trusting Meilynx.

A record, not a snapshot

The compliance system of record.

Beyond a point-in-time attestation, Meilynx is the durable record of what every model and agent did, when, and under which policy — one you own and can stand behind, long after the examination is over.

Fixes, not just findings

Close the control, not the ticket.

Findings come with recommended fixes and remediation tracking, so a flagged control becomes a closed one with an evidence trail — not another alert sitting in a queue.

Outcomes by role

Built for the people, answerable to examiners.

Concrete artifacts you can hand to a regulator, an examiner, or a board — not slideware.

CISO

Tamper-evident audit chain. Examiner-ready evidence, not screenshot collections.

Audit chain

CCO

Auto-generated SR 11-7 model inventory and NYDFS certification package, from live traffic.

Model inventory

CTO

One-line environment change. Zero-trust LLM access with policy-as-code your compliance team can read.

Policy-as-code

CFO

Per-team budgets with hard cost caps. Every dollar attributed before month-end.

Budget caps

See it running

Comply, govern, and optimize — in one view.

Mocked from real product surfaces. Book a 15-minute walkthrough to see live data.

Comply

Evidence an examiner will accept.

Tamper-evident, hash-chained audit trail — examiner-verifiable, not screenshots
Curated examination packages — SR 11-7 · NYDFS 500 · FINRA 24-09 · SOC 2
Controls mapped to named regulations, with remediation tracking
6-year WORM retention floor (Fully Managed, FINRA 24-09)

compliance · posture & audit chain

SR 11-7model inventory · controls
Met
NYDFS 500§500.6 audit trail evidence
Met
FINRA 24-09Rule 4511 · supervision
Met
SOC 2 Type Ifieldwork scheduled
In review

seq 1042

seq 1043

seq 1044

hash-linked · WORM archive · examiner-verifiable

Illustrative example. to see the real platform.

Enforce

Governance rules that run in production.

Model allow/deny lists and token limits per workflow
Cost caps and budget enforcement in real time
Built-in governance rules — PII · MNPI · PHI, model access, cost, agent safety, schema — extensible via WASM and webhook validators
Draft, review, and publish rules with confidence

governance · live intercept log

PROMPT
PII detectedSSN pattern in user message
BLOCKED
RESPONSE
Data leakage blockedAPI key exposed in model output
BLOCKED
PROMPT
Harmful contentPolicy violation · jailbreak attempt
BLOCKED
RESPONSE
Off-topic driftResponse outside allowed scope
WARN

Illustrative example. to see the real platform.

Observe

Cost, performance, and outcomes.

Cost analytics tied to business outcomes, not just token counts
AI health monitoring with anomaly detection (proprietary ML)
Custom KPIs by workflow, team, and customer segment
Optimization recommendations and impact simulation

analytics · cost & outcomes

AI spend / mo

$12,840

↓ 8%

Success rate

94.2%

↑ 2.1%

Cost / outcome

$7.06

↓ 12%

cost by workflow $/outcome

chat-support
$14.04
doc-summary
$9.12
code-gen
$6.28
search
$4.09

Anomalydoc-summary cost +34% vs. baseline

Illustrative example. to see the real platform.

Works with your stack

OpenAI

Anthropic

Azure OpenAI

Google

Bedrock

Vertex AI

Okta

Entra ID

Splunk

Datadog

Architecture

Two planes. One trust boundary.

Each customer gets a dedicated data plane managed by us inside isolated per-customer infrastructure, or run by you on your own. Either way it owns your audit trail. The control plane is a shared SaaS that distributes signed governance bundles and aggregates telemetry metadata — never raw payload. The proxy is going Apache 2.0 at SOC 2 GA; the binary you run is the binary you can read.

Your environment

Managed or self-hosted · isolated either way

Trust boundary

Application
Your apps & agents
Meilynx Proxy
Validators · streaming · audit emission
Audit Trail
WORM archive · hash chain · examination export

Raw prompts & responses never leave this boundary.

Per-customer isolated data plane in every deployment mode

Telemetry

metadata · mTLS

Signed bundles

policy-as-code

Meilynx control plane

Managed SaaS

Policy authoring
Signed bundles · policy-as-code
Compliance console
Posture · waivers · examination packages
Telemetry rollup
Metadata only · token counts · rule outcomes

No raw payload data ever reaches the control plane.

Your environment

Managed or self-hosted · isolated either way

Trust boundary

Application
Your apps & agents
Meilynx Proxy
Validators · streaming · audit emission
Audit Trail
WORM archive · hash chain · examination export

Raw prompts & responses never leave this boundary.

Per-customer isolated data plane in every deployment mode

Telemetry

metadata

Bundles

policy-as-code

Meilynx control plane

Managed SaaS

Policy authoring
Signed bundles · policy-as-code
Compliance console
Posture · waivers · examination packages
Telemetry rollup
Metadata only · token counts · rule outcomes

No raw payload data ever reaches the control plane.

Deployment modes

Three modes. Same isolation. Different operator.

Meilynx runs in three configurations. The data-plane isolation invariant holds in all three — the difference is who operates the infrastructure.

Dimension	Fully Managed Meilynx operates per-customer infrastructure · ~1 day	Bring Your Storage Meilynx operates proxy · customer owns audit store · 3–5 days	Self-Hosted Customer operates everything · 1–2 weeks
WORM immutability	GCS Bucket Lock · locked Live	S3 Object Lock · in development In development	Customer-managed GCS Customer
Retention floor	6 yr prod · 30 d staging · 1 d test (FINRA 24-09) Live	Customer-set Customer	Customer-set (proxy default: 90 d) Customer
Encryption at rest	AES-256-GCM + per-customer CMEK Live	Customer-managed Customer	Customer-managed Customer
Integrity Pack	Available (GCS-backed) Live	In development In development	Customer-managed Customer
Verification surface	Hash chain · examiner-verifiable via GCS Live	Hash chain · customer-operated storage Customer	Hash chain · customer-operated Customer
Proxy operated by	Meilynx Live	Meilynx Live	Customer Customer

By the numbers

Production-grade. Examination-ready.

Operational characteristics that matter when AI workloads are critical infrastructure.

Latency overhead

Low

Built-in rules (regex, token, schema) add sub-millisecond overhead; guard models and webhooks run on configurable budgets

Provider coverage

4/ live

OpenAI · Anthropic · Google · Azure OpenAI

Time to deploy

1day

Managed proxy · 1–2 weeks self-hosted

Data residency

Dedicatedper customer

Isolated data plane in every deployment mode · raw payload never reaches shared systems

Regulation coverage

Built for regulated industries.

Controls map directly to named regulations—not generic best practice. The proxy already enforces the underlying controls; what ships next is preset bundles, not capability.

Available as presets today

Curated control bundles ship in the product. Drop in, scope to your environment, go.

SR 11-7NYDFS 23 NYCRR 500FINRA 24-09SOC 2 Type II

Configurable today, curated bundles in progress

All controls already enforceable via the policy engine. Curated, examiner-aligned bundles are sequenced next.

HIPAA Technical SafeguardsEU AI ActISO 42001NIST AI RMF

Industries served

Financial servicesEnterprise SaaSHealthcareLegalInsurancePublic sector

Frequently asked questions

Questions from CISOs, compliance, and engineering leaders.

What happens to my prompts and responses?

They stay in your dedicated, isolated infrastructure — whether Meilynx operates it (Managed) or you do (Self-Hosted). The proxy processes prompts and responses there; only hashed, aggregate metadata flows to the shared control plane for dashboards and analytics. Raw payload never reaches Meilynx-shared systems.

Is the proxy open source?

Yes — the proxy is going Apache 2.0 at SOC 2 GA. Design partners deploying with us today get source access under mutual NDA. Self-hosted operators run their own build of the same binary the Managed and BYOS modes run.

How long does deployment actually take?

All three modes deploy the proxy into infrastructure dedicated to your organization — the difference is who operates it, not whether your data is mixed with anyone else's. Fully Managed: ~1 day — the proxy runs in a per-customer environment we operate, and you change one base URL. Bring Your Storage: 3–5 days, with audit data landing in your storage. Self-Hosted: 1–2 weeks, including bundle signing keys and your own observability hookups. All three modes share the same proxy binary and policy engine.

Can I enforce different policies per team or workflow?

Yes. Governance rules are scoped by workflow, team, environment, or customer segment. You can set different budgets, model restrictions, and safety thresholds for each. We also built industry-specific presets to get you started quickly.

What if I use multiple AI providers?

Meilynx works across OpenAI, Anthropic, Google, and other providers. You get unified cost analytics and governance regardless of which models you use.

How do you handle agentic and multi-step workflows?

Meilynx traces full agent execution chains — including retries, tool calls, and sub-agent invocations — so you can attribute cost and enforce policies at the workflow level, not just per-call.

How does Meilynx fit our existing IdP and SIEM?

The control plane uses Google OAuth and email/password via Google Identity Platform today. Enterprise SAML / OIDC federation — Okta, Microsoft Entra ID — is on the roadmap. For audit event export, the proxy ships a pluggable exporter interface proxy-side in your environment — no payload data crosses the wire to a SaaS aggregator. A ClickHouse integration is in development; S3-compatible (Object Lock), Splunk, and Datadog are on the near-term roadmap.

Trust & compliance posture

SOC 2 Type I· Fieldwork scheduled

Open source proxy· Apache 2.0 · at SOC 2 GA · source access under NDA

GDPR-ready

HIPAA-ready

EU AI Act-aligned

Data residency· Per-customer isolated

Ready to evaluate Meilynx?

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