Tailored AI Gateway · built for your organization A controlled AI gateway,
A controlled AI gateway,
built for you in weeks
A central AI gateway, tailored to your organization: designed around your systems, policies and budgets, deployed in your own cloud and handed over as code you own.
Every AI request, checked
Your people, apps and agents
AI Gateway, inside your own cloud
Login and access
Data redaction
Budgets per team
Audit trail
Any model you choose
Runs on Microsoft Azure, AWS or Google Cloud
Runs on all three major cloudsMicrosoft Azure, AWS or Google Cloud: your gateway is built for the cloud you already use.
Controls tailored to youSafety rules, data redaction, budgets and audit, set up for your policies, your regulators and your teams.
Live in weeksAI governance enforced in daily use within weeks, compared with six months or more when you build it yourself.
You own the deployment. Every gateway is built for one client, on an open-source foundation, with our delivery partner. It is not a hosted service: it runs in your cloud, with your keys and your data.
How it works
Security, Governance and Budget Control
Your policies decide what to enforce. The gateway enforces it on every live request, between your applications and the AI models they use.
Your applications Enterprise chat portal Analytics copilots Autonomous agents Your next app
AI GatewayOne set of rules
Cloud
AI models
OpenAI
Self-hosted models Security, Governance and Budget Control Every AI request passes the same five controls. Click a control to see what it enforces.
Model routing
Routing each task to a suitable model
Every AI call takes one route in. The gateway sends each task to the model that best balances quality and cost under your rules.
50+ apps, agents and assistants
App 1 Agent 4 App 12 Team bot App 27 Agent 7 App 40 Agent 12 App 50 + 30 more
Every AI call, one route in Control plane Scores every task on capability, cost, speed and data sensitivity
Blocked: attempts to trick the AI Blocked: raw personal data
Routing is a policy decision Every task is scored on capability, cost, speed and data sensitivity, and re-routed as better or cheaper models arrive, without changing your apps. Click a model tier to see when the gateway chooses it.
The reality
Common problems with unmanaged AI use
01
Sensitive data leaves the companyClient records, contracts and source code are pasted into public AI tools and end up in vendor logs you do not control.
02
Invisible AI useUnapproved AI tools and agents spread team by team. Security finds out afterwards, if at all.
03
AI costs out of controlNo budgets, no tiers and no checks before each AI call. Pilot costs can quietly become a large line in the IT budget.
04
Nothing to show the auditorWho used AI, on what data and under which controls? Often nobody can answer with evidence.
05
Locked into one modelApps wired to a single vendor absorb every price rise and every model that vendor retires.
06
Governance rebuilt for every appEach new AI project rebuilds login, redaction and logging in its own way, or skips them.
Each of these problems appears in the incidents and surveys below.
In the news
Recent public AI incidents
BreakageAn AI agent wiped a production databaseDuring an explicit freeze on code changes, Replit’s coding agent deleted live records for more than 1,200 executives and 1,190 companies at SaaStr, then told the founder a rollback was impossible. It was not. The agent had unrestricted access and no enforced guardrails.Fortune, The Register · July 2025
Cost overrunUber used its 2026 AI budget by AprilUber spent its entire annual AI coding budget in four months.TechCrunch · 2026
Data sovereigntyA court froze everyone’s deleted chatsIn NYT v. OpenAI, a US federal court ordered OpenAI to keep all consumer and API chat logs, including deleted ones, and later to hand over 20 million conversations. Only customers with zero-data-retention agreements were exempt.Bloomberg Law, OpenAI · 2025 to 2026
The market need
Why AI risk is a board issue
EU AI ActBans on prohibited uses and the rules for general-purpose AI models already apply. Duties for high-risk uses start in December 2027, with fines of up to 7% of global turnover.
United StatesCalifornia AI laws took effect in January and August 2026, and Colorado’s rules on automated decisions follow in January 2027. GDPR, HIPAA and US bank model-risk rules already apply to AI decisions today.
Policies alone are rarely enough. People take the convenient path: they paste, they do not ask. Controls work best where they cannot be skipped.
Our answer
A gateway for every AI app
Set up the gateway once. Every new app and agent inherits every control from the start.
A gateway built for your networkEvery AI call runs through one control point inside your own cloud network. It works with the major model services: Azure OpenAI, AWS Bedrock, Google Vertex and Anthropic.
Personal data removed before it leavesPrompts are checked inside your network. Social security numbers, client records and market-sensitive information are removed before any request reaches a model or a vendor’s logs.
Identity, budgets and guardrailsSingle sign-on and role-based access on every AI entry point, budgets per role and team checked before each call, and a standard set of safety rules tuned to your regulators.
An audit trail ready for regulatorsWho asked what, when, at what cost and under which controls: one complete record, with no raw personal data stored.
Yours to keep
Open-source foundationNo license fees and no vendor lock-in.
Tailored configurationReady for your cloud and your industry.
Deployed in your own cloudNo data goes to us; requests reach only the model providers you approve.
We configure, you ownWe hand over the keys and your team runs it.
Extensible by designNew models, safety tools and tool connections plug in as configuration.
Client work
Tested on client work
Tested with regulated clientsUsed on client engagements handling investments, market-sensitive information, client personal data and health data, and already built on Azure, AWS and Google Cloud.
Attack tests with evidenceOur testing framework fires thousands of attempts to trick the AI or leak personal data at every safety rule, scored automatically by a second AI model.
Mapped to recognized standardsEvery control maps to the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act and the OWASP Top 10 for AI applications, with a compliance scorecard to measure against.
Cost controls built inModel tiers, routing and budgets checked before each call keep AI spending within budget.
Consistent with Gartner’s guidanceGartner’s AI trust, risk and security framework (AI TRiSM) treats checking AI use while it runs as a core part of governance. The gateway does this on every request.
The engagement
Live fast, governed from the start
1
Weeks 1 to 2AssessWe list every place AI is used, sort it into four risk tiers and score you from 0 to 4 across ten control areas, based on evidence rather than interviews.
2
Weeks 3 to 5DeployWe set up the gateway in your own cloud network: single sign-on and role-based access, personal-data redaction inside your network, budgets and the safety rules for your industry. Apps are re-pointed, not rebuilt.
3
Week 6Prove and hand overAn attack test with evidence, a compliance scorecard against the standards your regulators name, and training for your team. Then your team runs it.
Weeks with us, instead of six months or more alone Proven building blocks speed up the work, but every gateway is configured for your own policies, regulators and systems. You keep the whole stack when we leave.
Next step A two-week AI usage assessment You get your maturity score, your risk tiers and a deployment plan.
Book the assessment