How is this different from just using a provider's 'enterprise' tier?

A provider's enterprise tier is a real piece of the puzzle, but it doesn't give you a single audit point across multiple tools, teams, and providers, or the sanitization and access-control logic specific to your own data — that's the layer we build.

Can this integrate with our existing identity provider for role-based access?

Yes — SSO through your existing identity provider (Okta, Azure AD, etc.) is the standard way we scope what a given user or role is allowed to access through the middleware.

Does our data ever actually leave our infrastructure?

It depends on your architecture choice — private/self-hosted model deployments keep everything internal, while a sanitization layer in front of a third-party API is the right fit when a private deployment isn't necessary. We architect based on your actual sensitivity requirements, not a default.

Can we switch model providers later without rebuilding this?

Yes — that's the specific point of the middleware layer. Swapping providers or adding a new one is a configuration change behind it, not a rebuild of every integration your teams have wired up.

How long does this take to set up?

A focused proxy-and-sanitization layer for a defined set of use cases usually runs six to ten weeks. Broader rollout across many teams and data sources is scoped in phases after the initial audit.

Give your team real AI productivity without handing proprietary data to a black box.

The fastest way to lose control of proprietary data is letting every team plug their own AI tool into production systems with no oversight of what leaves the building. We build the middleware layer between your systems and the model providers you use — private endpoints where required, data sanitization before anything reaches a third-party API, and role-based access so an assistant only sees what a given user is actually allowed to see — so your team gets real AI productivity without your architecture becoming a compliance liability.

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What we build

Where this fits

Multiple teams are already piping data into consumer AI tools

Shadow AI usage with no oversight of what's being sent to a third-party model is usually already happening before anyone official signs off on it.

You're in a regulated industry and legal hasn't approved AI yet

Healthcare, finance, and insurance teams that want AI's productivity gains without the exposure of an ungoverned integration.

You need retrieval over proprietary data, not a public model's memory

Grounding an assistant in your internal documents and records without that data training a provider's public model.

A security questionnaire is blocking an enterprise deal

An enterprise customer's vendor security review flagged your AI integration, and you need an architecture that actually passes it.

You want to swap model providers without a rearchitecture

A middleware layer between your systems and whichever model you use means switching providers later is a config change, not a rebuild.

What's included

What we deliver

LLM proxy middleware

A central routing layer between your internal systems and model providers — one point of control, logging, and audit instead of every team calling a provider's API directly.

Data sanitization layer

PII and other sensitive fields stripped or masked before a request ever reaches a third-party model, not caught after the fact.

Private endpoint configuration

Private or network-isolated model deployments configured where your data sensitivity or compliance regime requires it, instead of public API calls by default.

Role-based access tokens

An assistant only sees and can act on what a given user's role permits — access control enforced at the architecture level, not assumed by convention.

Audit logging

Every request and response logged in a form your compliance or security team can actually review, not just a debug console nobody checks.

Vendor-agnostic architecture

Swapping between model providers, or negotiating a better contract later, is a configuration change behind the middleware — not a rebuild of every integration.

Governance-aware retrieval

Retrieval-augmented generation scoped to your existing document permissions, so an assistant can't surface a record a given user shouldn't see.

Our approach

How we run this engagement

Data & risk audit

Map what data is currently reaching AI tools, and what actually needs protecting.

Architecture design

Design the proxy, sanitization, and access-control layers around your real compliance requirements.

Build the guardrails

Implement middleware, logging, and role-based access with continuous testing.

Launch with monitoring

Ship with audit logging in place, and extend coverage as more teams adopt AI tools.

Tech we use

Built on a proven, modern stack

Node.js

Runs the proxy layer routing and logging every request between your systems and a model provider.

Python

Powers data sanitization and retrieval logic where heavier data processing is involved.

Claude API

A frontier model option evaluated alongside others for tasks that need careful, auditable reasoning.

OpenAI

An alternate model option, routed through the same middleware rather than integrated ad hoc.

Vector DBs

Retrieval scoped to your governance rules, grounding answers in your data without exposing what a user shouldn't see.

AWS

Private networking and infrastructure isolation where your compliance requirements call for it.

Questions

Things people ask before starting

Can't find what you're looking for? Reach out and we'll answer directly.

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Not necessarily — the goal is routing AI usage through a governed layer with logging and data sanitization, not blocking AI tools outright. We scope what needs to go through the middleware based on data sensitivity, not a blanket ban.