Enterprise Data AI — Semantic Layer
We put a governed control point between your applications and your models, inside your own tenancy, so every AI request is identified, budgeted, cleaned and recorded.
Assess your AI Governance
Enterprise Data AI — Semantic Layer
Every team connects straight to a provider with its own key. You get one bill per provider, per month, with no link to teams or products. One agent task can be dozens of requests to a model, and a runaway loop stays invisible until the bill arrives.
Whether a prompt is screened, or personal data is removed before it reaches a model, depends on who built the application. Provider keys sit in every app, so rotating or revoking them is a manual chase.
GDPR and the EU AI Act expect you to know which AI systems you run and what data flows into them. When an auditor or customer asks, the answer is a reconstruction, not a record.
One governed layer between the applications that use AI and the models that serve them.
Chat assistants, agents, coding tools, business apps and supplier products keep the standard AI interface they already use. Moving one behind the gateway is an address and a credential change.
Applies your rules in a fixed order: identity, budget, data cleaning, safety screening, routing, recording. It runs in your own cloud tenancy or on your own infrastructure.
Cloud AI services in your tenancy, any provider you approve, and models on your own hardware. Swap or add them behind the gateway without touching a single application.
Every rejected request stops at a rule, not at a paid model request. The cheapest outcome is always the safe one.
Every application, team and product is identified before a request is served, using the identity provider you already run.
Usage ceilings and money limits per team or product, with the remaining allowance visible to each team.
Personal and confidential data is detected and removed from prompts before they leave your perimeter, the same way for every app.
Harmful content and manipulation attempts are blocked before a model ever sees them, so a blocked request costs nothing.
Each request goes to the right approved model, cloud or your own. Repeated questions are answered from cache at no cost.
Every request is metered and logged with who, what, which model and how much. Prompt content is never stored in usage records.
Map current AI use and credentials. Agree target controls.
One or two real apps behind the gateway. First cost reports.
Remaining apps moved at your pace. All AI use governed.
Handed to your operations team, or run by IWConnect 24/7.
A managed cloud service inside your own tenancy, with private networking and the monitoring you already run.
The whole solution inside your perimeter, including on-premises. Same controls and rules as the enterprise form.
Teams and apps identified through the identity provider you already use, with approved-model lists per team.
Applications written once against a standard interface, with any approved model behind the gateway.
Every control is a switch, set per environment, per team or product, and per request where useful.
Delivered from a reusable kit with everything your team needs to run it, or as a managed service.
| The question leadership asks | Point-to-point AI today | With the gateway |
|---|---|---|
| Who is using AI, with which models? | Reconstructed from invoices and interviews | Known per request, team and product |
| What does it cost per team or product? | One bill per provider, allocated manually | Metered per request, budgets enforced before spend |
| Did personal data leave our perimeter? | Depends on each app, usually unknown | Removed before the model sees it |
| What was blocked, and did we pay for it? | Blocked by the provider after billing | Blocked at the gateway, no cost |
| Can we prove it to an auditor? | No single source of truth | One complete record in your environment |
AI governance assessment. We map current AI use across teams and agree the first set of controls with your technology, security and finance stakeholders.
Request AI Gateway Assessment
Practical answers about AI governance, cost control, data protection, deployment and running the gateway.
A single, governed control point between your applications and the AI models they use. Every request is identified, budgeted, screened, cleaned and recorded, and the gateway runs inside your own tenancy or infrastructure.
No. Applications keep the standard AI interface they already use. Moving one behind the gateway is an address and a credential change.
Either as a managed cloud service inside your own tenancy with private networking, or fully inside your perimeter, including on-premises and hybrid setups. The controls and rules are the same in both forms.
No. Every request is metered and logged with the team or application, the model and the cost, but prompt content is never stored in usage records. The records stay in your tenancy.
Cloud AI services in your tenancy, any provider you approve, and models running on your own hardware. You can swap or add models behind the gateway without changing a single application.
The rollout starts with an assessment of current AI use, followed by a pilot that puts one or two real applications behind the gateway and produces the first cost reports.It usally takes 6 to 8 weeks.]
The gateway gives you a complete record of which AI systems you run, who uses them and what data flows into them, which is the evidence GDPR and the EU AI Act expect you to be able to show. Compliance also depends on your wider policies and processes.
Yes. The assessment maps current AI use and agrees the first set of controls, and the pilot puts one or two real applications behind the gateway. The remaining applications move at your pace.
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