Enterprise AI Governance
AGate — AI Mission Control for Your Enterprise
AGate is your enterprise AI governance layer — one governed path for every LLM request, from every team, across every cloud. See it, control it, scale it.
Your teams already use AI. With AGate, you can govern all of it.
10–20
Days to first deployment
2,500+
Users on one governed platform
100%
Of AI requests audited
80%
Faster AI use-case delivery
Trusted by enterprise AI leaders
BMO · AstraZeneca · Volkswagen · Raiffeisen Bank · NLB Group · Škoda · Magna · Husky · SGI · SOCAN · KWS · AMS Osram
They read the room, bridge interests across teams, and get our departments speaking the same language — alongside strong technical expertise.
Aleš Gorišek · AI Architect · NLB Group
Overview
Two Components, One Governed Platform
AGate is a GenAI control layer you deploy in your own cloud. It governs every model request and gives every team a compliant AI workspace.
LLM Gateway
One governed API endpoint for every AI provider.
AChat
Self-hosted enterprise chat your teams actually prefer.
Your tenant
Runs in your cloud; your data never leaves it.
The Problem
AI Is No Longer Just a Chat
AI is now taking actions inside your systems — but without a governed layer, four risks compound faster than IT can contain them.
Shadow AI
Teams adopt unapproved tools that spread beyond anyone’s view, moving sensitive data through channels security never sanctioned or logged.
Agent sprawl
Autonomous agents multiply faster than you can govern them, each with its own access, cost, and failure modes to track.
IT overload
Every team ships one-off AI rollouts, leaving IT to maintain a patchwork of integrations, credentials, and provider contracts.
Runaway cost
AI spend climbs with no caps, budgets, or alerts, so overruns surface only after the invoice has already arrived.
91%
Of organisations hit sensitive-data leakage risk
Evidence: Check Point, 2025
€5–20M
Potential fine per incident under NIS2 & the AI Act
Regulatory exposure estimate
30–50%
Of GenAI innovation time lost to compliance rework
Evidence: McKinsey, 2025
The Offer
Governance Delivered as Infrastructure
AGate gives you one endpoint for every model, a compliant chat workspace, and full audit and spend control enforced on each request.
LLM Gateway
Routing · Failover · Load balancing
- One API endpoint for every LLM provider — OpenAI, Anthropic, Azure, Google, AWS, Mistral and more
- Automatic failover and load balancing across models and regions
- Switch models in a single click without changing application code
OpenAI-compatible API + AWS Bedrock endpoint
AChat Workspace
Multi-model · Self-hosted · Branded
- Self-hosted, multi-model enterprise chat, branded as your own
- Teams switch between models in one click
- Connect internal systems via MCP tools for SharePoint, Jira and SAP
AChat + MCP tools (SharePoint, Jira, SAP)
Identity & Access
SSO · RBAC · Single key
- Single sign-on through your existing Entra ID or Okta
- Role-based access controls which teams use which models
- One API key per team — no credentials scattered across repositories
Entra ID · Okta
Audit & Spend
Audit trail · Budgets · Observability
- Every request logged for a complete, exportable audit trail
- Budget caps enforced per user or department
- Live usage and spend observability across every provider
Admin Portal · Grafana
Expected Outcomes
What Enterprises Get From Governed AI
Enterprises that standardise AI governance on AGate see faster delivery, full auditability, and controlled spend across every team and provider.
80%
Faster AI use-case implementation
Evidence: NLB Group (banking)
5
Months to a full production platform
Evidence: NLB Group / International Pharma
100%
Data security and auditability
Evidence: NLB Group
2,500+
People on one governed platform
Evidence: Global IT & Consulting
How We Work
Live in 10 to 20 Days
AGate deploys inside your cloud tenant and reaches production in weeks, not quarters — validated on real workflows before you scale it group-wide.
Deploy in your tenant
10–20 days
Install AGate via Terraform inside your own Azure or AWS tenant, connect your existing SSO, and point every AI provider at a single governed endpoint — with no migration of your current models or regions.
Validate on real workflows
Month 2
Switch on monitoring and per-team spend tracking, run failover tests across providers, and review your first audit logs against live traffic — confirming governance and cost controls hold before any wider rollout.
Scale across the org
Month 3+
Roll governed AI out to every team with role-based access and department budgets, then export an audit dossier that maps usage to NIS2, DORA, and GDPR for your compliance stakeholders.
Why Adastra
Built by a Team That Ships Enterprise AI
AGate is enterprise infrastructure built by Adastra — a global AI practice that has put production AI into regulated enterprises for over twenty years.
20+ years in production
Two decades delivering enterprise data and AI means AGate is shaped by real production constraints, not a lab — and the same team ships it every day.
Deep engineering bench
150+ AI engineers and developers stand behind the platform, so you get implementation depth and ongoing support, not just a licence and a docs portal.
Proven at enterprise scale
450+ AI projects delivered across 40+ countries, including banks and life-sciences firms with the strictest security, residency, and audit requirements.
Regulated-industry ready
Governance mapped to NIS2, DORA, MiFID, GDPR, and the EU AI Act, with EU data residency and on-premises options for the most sensitive workloads.
20+
Years in production
150+
AI engineers
450+
AI projects delivered
40+
Countries
Take control of your AI
Book a live working session with the Adastra team — no pitch deck, just your architecture and your questions.
Request a demoGet It Now
Take Control of Your Enterprise AI
Get your AI Mission Control running in 10 to 20 days. Book a live working session with the Adastra team — no pitch deck, just your architecture and your questions.
- Guided product demo with your data
- Architecture review for your use case
- Pricing and packaging tailored to you
- Deployment plan for your cloud tenant
FAQ
Questions Enterprises Ask Us
Straight answers to the questions leaders raise when evaluating an enterprise AI governance platform — deployment, providers, identity, coding assistants, pricing, and security.
Where does AGate run?
AGate runs in your own cloud tenant — Azure or AWS — or fully on-premises, so your data never leaves your environment. EU data residency is supported for regulated workloads, and sensitive models can stay on-premises with full sovereignty. You keep control of where every request and every log lives.
How long does deployment take?
AGate installs in your cloud tenant via Terraform in 10 to 20 days. You validate it on live workflows during the second month, then scale governed AI access across the organisation from month three. Most enterprises reach a production enterprise AI governance platform within a single quarter, not several.
Do we have to replace our existing AI stack?
No. Keep AI Foundry and your OpenAI deployments on Azure, and keep Bedrock and SageMaker on AWS. AGate adds a governance layer across all of them rather than replacing anything. It works with LangChain, LlamaIndex, and any OpenAI-compatible SDK, so your existing applications connect with minimal change.
Which AI providers and models are supported?
AGate supports OpenAI, Anthropic, Azure, Google, AWS, Mistral, and more — all behind one governed API endpoint. Teams switch models in a single click, and on-premises models are supported with full data sovereignty. New providers can be added later without changing how your applications call the gateway.
How do users authenticate?
Users authenticate through your existing SSO — Entra ID or Okta — with role-based access controlling who can use which models. Each team receives a single API key, so no credentials end up scattered across repositories or shared in plain text. Access, roles, and spend are all managed from one place.
Can developers use AI coding assistants through AGate?
Yes. Claude Code and Cline are tested and approved with AGate. They connect through the gateway’s OpenAI-compatible and AWS Bedrock endpoints, so developers keep their preferred tools while authentication, spend tracking, and load balancing are handled centrally. Coding-assistant usage stays governed and visible like any other AI request.
How is AGate priced?
Pricing and packaging are tailored to your team size and usage rather than sold as fixed tiers. Request a demo and the Adastra team will walk through your architecture and propose a model that fits your deployment, user count, and provider mix. There is no per-seat lock-in to work around.
Is AGate secure and compliant?
AGate is built for regulated enterprises: it is SOC 2 ready, GDPR-compliant, and EU AI Act ready, with governance mapped to NIS2, DORA, and MiFID. Every AI request is logged for a complete audit trail, and enterprise AI governance controls such as role-based access and spend caps are enforced on each call.