Federated AI infrastructure

The platform for trusted autonomous AI.

Connected Autonomy provides infrastructure primitives for identity, execution, communication, and verification — so AI systems can operate safely across organizational boundaries.

Trust framework

Connected Autonomy Network

Verified

Fedi.Mesh

Identity

Fedi.Run

Execution

Fedi.Wire

Communication

Fedi.TrustVerification & attestation
Execution variance<1%
Trace coverage100%
Evidence chainCryptographic

Infrastructure primitives

4

Platform products

5

Trust pillars

Safety · Resilience · Reliability

Execution variance

<1%

Trust framework

Safety is not a feature. It is the reason the network exists.

Every product, every primitive, every operation in the Connected Autonomy network is measured against three pillars: safety, resilience, and reliability.

AI Safety

Autonomous systems operate within declared boundaries with human review points. Unbounded autonomy is unsafe autonomy.

AI Resilience

Systems degrade gracefully, preserve evidence under failure, and recover without manual intervention.

AI Reliability

Same inputs, same outputs, same evidence. Deterministic execution with published benchmarks and full traceability.

Questions

What teams ask before they start.

Clear answers for teams evaluating federated AI infrastructure.

What makes Connected Autonomy different from other AI platforms?

Connected Autonomy is a federated platform — it provides infrastructure primitives for identity, execution, communication, and verification across organizational boundaries. Products like Latitude, Muffy, and Counsel build on these primitives for enterprise, consumer, and professional audiences.

Does the platform replace our existing infrastructure?

No. The Fedi primitives are designed to sit alongside your existing cloud, data, and identity systems. Fedi.Mesh extends your identity. Fedi.Run governs execution. Fedi.Wire structures communication. They integrate — they don't replace.

How do you measure reliability?

We publish execution benchmarks quarterly with full methodology and datasets. Current benchmarks show sub-1% variance across 1,000+ samples with 100% trace coverage. The numbers are public because trust requires transparency.

Next step

See how the primitives map to your AI operations.

Request architecture review