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Control

AI Gateway

Keep model choice flexible while governing what data and tools can be used.

  • Model routing
  • Tool allowlists
  • Spend and latency controls

What gets in the way

  • Teams call models and tools directly with inconsistent policy and logging.
  • Provider changes create application rewrites and fragmented cost controls.
  • Sensitive prompts, tool permissions, and failure behavior are hard to audit.

What this is designed to produce

  • One governed entry point for approved models and tools.
  • Policy-aware routing by task, risk, latency, quality, and cost.
  • Consistent telemetry, budgets, redaction, and fallback behavior.

Architecture, with its boundaries

  • Policy

    Makes policy an explicit, observable part of the AI Gateway system.Input boundary

  • Router

    Makes router an explicit, observable part of the AI Gateway system.Context boundary

  • Provider adapters

    Makes provider adapters an explicit, observable part of the AI Gateway system.Decision boundary

  • Telemetry

    Makes telemetry an explicit, observable part of the AI Gateway system.Action boundary

How delivery is staged

  1. 01

    Discover

    Map the ai gateway workflow, evidence, risks, owners, and baseline.
  2. 02

    Design

    Define boundaries, architecture, evaluation criteria, and human controls.
  3. 03

    Prove

    Validate one bounded workflow with representative data and accountable users.
  4. 04

    Operate

    Deploy with monitoring, recovery, change control, and an expansion backlog.

Questions that come up first

How does AI Gateway connect to our existing systems?

We map approved sources and actions through their supported APIs or controlled custom interfaces. A migration is not assumed; identity, permission, and transaction boundaries stay explicit.

Can we use our preferred model or cloud provider?

Yes. The architecture separates business context, evaluation, and tool policy from any single model. Provider choices remain subject to your security, residency, quality, and cost requirements.

Where does human approval remain?

Approval is designed around risk. External, sensitive, irreversible, low-confidence, or policy-exception actions stop for an accountable person before execution.

What is the first production milestone?

A bounded workflow with agreed inputs, controls, failure handling, evaluation criteria, and an owner. The target is dependable learning, not an inflated automation claim.

Start with clarity

If the business case isn’t there, we’ll tell you before you build.

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