We go beyond speed and cost by treating inference as energy infrastructure. Every request is governed against energy, carbon, cost, and compliance before execution, with measurable control at the unit of work—not post-hoc or averaged.
Dali enforces governance upstream of execution. It evaluates energy conditions alongside cost, carbon, compliance, and security, and only routes work that satisfies all constraints. No drift, no post-hoc correction, no unmanaged energy demand.
Every request generates a complete record: energy, cost, latency, carbon impact, and compliance outcome. Finance sees cost per token. Sustainability reports grid emissions per inference. Security audits policy decisions. One source of truth, zero ambiguity.
Dali does not just route requests. It optimizes AI workloads against real grid conditions. Inference is shifted across regions, providers, and timing based on grid intensity, capacity, and energy availability. This reduces peak demand, aligns workloads with lower-carbon energy, and turns AI from a passive load into an actively managed system.
Typical AI compute and energy savings
30–50%
20–40%
Reduction in energy (kWh) and carbon achieved through grid-aware routing, model selection, and workload shifting.
Workloads shifted off-peak when policy allows
20–30%
Traceable decisions for compliance and finance
100%
AI at scale is an infrastructure problem first. Economics, carbon, and compliance must be visible and enforced in real time, not discovered in monthly reports.
Dali operates as a continuous pipeline from evaluation to execution: it evaluates each request, routes it across models and providers, enforces policy constraints, and audits every outcome. The result is a system where every inference is governed, measurable, and accountable.