AxonFlow is the runtime control layer for production AI systems.

As teams move from AI demos to real workflows, the blocker is no longer model quality. It is runtime authority: deciding what an AI system is allowed to do before it touches tools, APIs, data, or infrastructure.

Most AI pilots stall when platform, security, and compliance teams cannot answer basic questions:

Who allowed this action?
Which policy made the decision?
Was this step blocked, approved, paused, or resumed?
Can we prove it later?

AxonFlow operates in the execution path and records why AI actions were allowed, blocked, paused, or resumed while the workflow is running. It gives platform teams a central control layer instead of asking every application team to hand-roll policy logic inside prompts, wrappers, hooks, or framework-specific code.

What AxonFlow delivers:
- Runtime decisions for AI actions: allow, block, ask a human, or fulfill policy obligations
- Decision Mode for platform teams that want a central policy decision point across gateways, agents, MCP servers, and workflows
- Sub-10ms policy enforcement across models and tools, including RBAC/ABAC, DLP, and sensitive-data controls
- Permission-aware retrieval with IAM, SSO, and least-privilege access
- Human-in-the-loop approvals, retry-aware execution records, fallbacks, and workflow control
- Auditability with decision trails, traces, replay, and telemetry across cost, latency, and quality
- Deployment for regulated environments: SaaS, in-VPC, or air-gapped on-prem

AxonFlow is a Delaware C-Corp with the team split between Amsterdam and Seattle, serving enterprise teams across finance, healthcare, travel, and SaaS.

AxonFlow

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AxonFlow

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