Your developers adopted AI in weeks.

Your infrastructure hasn't caught up.

We install the governance layer that makes AI-assisted development auditable, visible, and faster. Hosted on your cloud, your identity stack, your terms.

SEE IT RUNNING Read the architecture →

LIVE ON AZURE — EU DATA GOVERNANCE

GATEWAY STATUS

$ gateway status --live ▸ routing GitHub Copilot → Azure OpenAI ✓ logged ▸ routing Claude Code → Anthropic API ✓ logged ▸ routing Gemini CLI → Vertex AI ✓ logged ▸ routing GitHub Codex → Azure OpenAI ✓ logged

INSTRUCTIONS CLAUDE.md synced from main → 14 repos MCP SERVER authenticated via Entra ID ✓ active COST TODAY $127.40 across 4 models ✓ budget

All 23 developers governed. 0 unlogged calls.

01 — THE GAP

This is a tooling gap,
not a people problem.

Your teams adopted AI because it makes them faster. That's the right call. But logging, access control, and shared standards weren't built yet. Close that gap and you get both: speed and visibility.

DEVELOPER → Copilot Enterprise → Claude via personal key → ChatGPT Enterprise → local .cursorrules → uncommitted CLAUDE.md → ad-hoc MCP server

NO CENTRAL
LOGGING Calls go direct.
No cost visibility.
No audit trail.

MODELS HIT GPT-4o (OpenAI direct) Claude 4 (Anthropic direct) Gemini Pro (Google direct) Azure OpenAI (maybe)

No Entra ID auth
No Azure Foundry
No security perimeter

02 — WHAT WE BUILD

Four gaps. Four things we build.

Instructions live outside version control.

CLAUDE.md, .cursorrules, system prompts — AI tools follow instruction files that shape every line of output. Most never get committed or reviewed.

WE MOVE THEM INTO THE REPO

CLAUDE.md

→

git repo

→

PR review

→

all teams

Instructions enter version control and flow through your existing Azure DevOps PR workflow — reviewed, versioned, auditable. One merge updates every team.

LLM calls go direct and unlogged.

Developers call multiple models through multiple paths. No record of what was sent, where, or what it cost. Copilot through GitHub, Claude through personal API keys, ChatGPT through enterprise — zero central visibility.

WE ROUTE EVERYTHING THROUGH ONE GATEWAY

Copilot Claude Gemini Codex

↓ all traffic

API GATEWAY — YOUR AZURE

↓ routed + logged

Azure OpenAI Anthropic Vertex AI

✓ Full cost visibility ✓ One audit log ✓ Zero dev friction

A single entry point on your infrastructure. Every call passes through it. One log, full cost visibility. Developers change nothing; the gateway sits in the path transparently.

Every team built its own integrations.

Teams connect AI agents to internal docs, Snowflake, Azure DevOps APIs independently. Each MCP server works. None share a security review. None authenticate through Entra ID.

WE REPLACE THEM WITH ONE KNOWLEDGE SERVER

team-A/mcp team-B/mcp team-C/mcp

↓ collapse into

MCP SERVER — ENTRA ID AUTH

→ Internal docs → Snowflake → Azure DevOps → APIs

One MCP server, authenticated through Entra ID, serves all teams. One security review. New content reaches every agent immediately.

PRs ship without AI-aware review.

AI generates code faster than humans can review it. Pull requests pile up or get rubber-stamped. Security issues, hallucinated dependencies, and pattern violations slip through.

WE ADD AI REVIEW TO YOUR PR PIPELINE

PR opened in Azure DevOps

↓

AI REVIEWER RUNS AUTOMATICALLY

↓

✓ Security scan ✓ Standards from CLAUDE.md enforced ✓ Pattern violations flagged ✓ Dependency audit

↓

Human reviews what matters

An AI reviewer runs inside Azure DevOps on every PR — checking for security issues, enforcing your standards, flagging what matters. Human reviewers focus on architecture, not line-by-line scanning.

03 — THE STACK

This isn't a proposal.
It's running infrastructure.

DEPLOYED ON AZURE

GOVERNED ARCHITECTURE — PRODUCTION LIVE

DEVELOPER LAYER

GitHub Copilot Enterprise

Claude Code / Cursor

GitHub Codex / Gemini

all calls routed through

API MANAGEMENT

LOGGING COST TRACKING RATE LIMITS POLICY

routed to approved providers

MODEL PROVIDERS

Azure OpenAI / Foundry

Anthropic (Claude)

Google Vertex AI

MCP KNOWLEDGE SERVER Internal docs · Snowflake · Azure DevOps · Confluence · SharePoint

IDENTITY & AUTH Microsoft Entra ID · RBAC · SSO · Conditional Access

SHARED INSTRUCTIONS CLAUDE.md · .cursorrules · PR-reviewed · Version controlled

AI PR REVIEW Azure DevOps pipeline · Security scan · Standards enforcement · Every PR

04 — ENGAGEMENT

Start where it makes sense.

Assessment

HALF DAY

Map your AI tooling landscape. Which tools, which teams, which gaps. Leave with a prioritized plan — not a slide deck.

→ Tool inventory across teams → Risk assessment → Governance gap analysis

Pilot

4 TO 6 WEEKS

Gateway, knowledge server, shared instructions — running for one team on a real codebase. Not a PoC. Production.

→ API Gateway on your Azure → MCP server with Entra ID → CLAUDE.md in version control → Cost dashboard live

Rollout

ALL TEAMS

Deploy across all teams. The governed path becomes the fastest path. After the pilot, we stay engaged — the AI landscape shifts monthly.

→ All dev teams governed → Continuous adaptation → Audit-ready from day one