Vectris Labs is building runtime control software for AI inference economics.

Our work is designed to help infrastructure teams identify, govern, and certify recoverable capacity inside existing GPU fleets. Modern inference workloads are increasingly shaped by neural working state: KV cache, activations, memory traffic, power, latency, and continuous user-scaled serving load.

Vectris focuses on certified useful compute recovery. ANE is the bounded runtime control layer. FEE is the measurement and certification layer that evaluates whether runtime actions produced physical and economic benefit through workload-specific baseline-versus-controlled testing, quality gates, overhead accounting, hardware-counter review, and capacity reporting.

Vectris is currently focused on validation architecture, strategic-partner diligence, and controlled technical proof-of-value work with infrastructure teams. We do not frame early work as universal production proof. Results depend on workload, hardware, quality gates, overhead, and counter-validated testing.

Built for GPU clouds, inference platforms, enterprise AI infrastructure teams, and silicon / data-center partners evaluating cost per token, watts per token, memory traffic, throughput per GPU, and recoverable capacity.

Technical conversations by request.

Vectris Labs

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Vectris Labs

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