One AI gateway. A different boundary for every workload.
Keep confidential AI on infrastructure you control. Use approved public models where they make sense. Give applications one compatible interface and IT one place to govern both.
“Local or public?” is the wrong enterprise AI question.
A legal team may need matter documents to stay inside the firm. Customer support may benefit from a capable hosted model. Development may use another provider entirely. A blanket decision either exposes too much or limits too much. The useful boundary follows the workload, not the organisation chart.
One application interface, controlled model placement.
Every application makes its own AI decision.
- Provider credentials spread through applications and teams.
- Sensitive and ordinary requests follow the same path.
- Usage, cost, and traces are fragmented.
- Changing providers becomes application work.
Applications use one approved AI surface.
- Local inference and public providers sit behind one gateway.
- Routes match approved models to defined workload classes.
- Credentials, budgets, guardrails, usage, and audit stay central.
- Provider changes do not require a new application integration.
Turn data boundaries into routing policy.
- 01
Classify workloads
Separate privileged, regulated, internal, and ordinary use cases according to the organisation's own data policy.
- 02
Connect models
Register approved public providers with customer-owned credentials and deploy local inference on controlled GPU capacity.
- 03
Define routes
Expose allowed models and routes through an OpenAI-compatible gateway with budgets and guardrails.
- 04
Review the evidence
Attribute usage, inspect run traces, and review gateway, provider, model, guardrail, agent, and tool-approval events.
AI becomes another governed operating domain.
Model access uses the same organisation, policy, secrets, network, and audit foundations as the infrastructure below it.
Local inference
Run approved open models through vLLM or Ollama on GPU-backed infrastructure controlled by the customer.
Public model choice
Connect hosted providers with customer-owned credentials and make only approved models available to applications and teams.
Compatible gateway
Give internal applications an OpenAI-compatible endpoint so placement and provider policy can change behind a stable integration.
Agents and workspaces
Define versioned agents with scoped tools and secrets, shared memory, asynchronous runs, and private AI applications.
Usage and audit
Apply spend budgets and usage attribution, inspect traces, and retain AI events alongside platform events.
Anchras provides the enforcement and evidence surface; the organisation defines the classifications. Applications must use approved routes and teams must decide which data may reach which models. The platform makes that decision implementable, visible, and reviewable.
Confidential legal work stays local. General work keeps the wider model choice.
A law firm's document-analysis application can target a locally hosted model for privileged files. A separate customer-support workflow can use an approved public model. Both use the same gateway contract, while routes, credentials, usage, and audit remain under the firm's policy.