Voertx reasons across models, runtimes, kernels, memory, accelerators, networks, power and cooling to improve performance, cost, energy and reliability as one coordinated system.
Voertx does not apply isolated tuning rules. It builds a live view of the system, evaluates competing actions, executes within policy and learns from the result.
Collect live performance, workload, infrastructure and environmental state.
Build a current system graph linking workload behavior to hardware and physical constraints.
Identify binding constraints, opportunity windows and the most valuable control surfaces.
Test multiple candidate plans in shadow mode before making a production change.
Select the safest Pareto-efficient action under current business priorities.
Coordinate policy-controlled changes across specialized agents and execution domains.
Compare measured performance against the predicted result and detect regressions.
Use the outcome to improve future candidate generation, confidence and control policies.
The interface centers on objectives, constraints, candidate actions, predicted outcomes and verified results rather than raw telemetry alone.
Objective: balanced efficiency
Move 18% of standard-tier inference to a lower-cost region, reduce decode power state and rebalance premium replicas.
Voertx evaluates tradeoffs rather than maximizing one metric in isolation.
A local improvement can create a global regression. Voertx evaluates dependencies across software, hardware and physical infrastructure before it acts.
Each agent proposes actions within a bounded domain. Voertx resolves conflicts, enforces policy and selects the coordinated system-level response.
Optimizes batching, routing, replica count, queue behavior and execution policy.
Evaluates fusion, operator choice, layouts, quantization and arithmetic intensity.
Manages KV cache, fragmentation, residency, compression and data movement.
Coordinates workload placement across nodes, racks, clusters and regions.
Balances power caps, DVFS, electrical headroom and workload priority.
Scores blast radius, rollback readiness, failure probability and confidence.
Candidate plans are simulated against current state, constraints and historical behavior before they can be promoted.
Good energy reduction, moderate latency risk.
Higher throughput, weaker premium traffic response.
Best combined cost, energy and SLA outcome.
Strong energy outcome, higher network exposure.
A single symptom, reasoned across signal and energy layers, resolved as one coordinated plan.
via telemetry & observability
Increased HBM stalls
NVLink backpressure
Cooling inlet temperature rising
Rack power nearing threshold
via energy & physical intelligence
Peak energy-price window active
Reduced grid import capacity
Battery reserve requirement binding
Rebalance replicas across available capacity · compress a portion of KV cache · route low-priority traffic to another region · reduce batch window for premium-tier traffic · maintain BESS reserve above policy floor
These figures are product scenarios illustrating the categories of impact Voertx is designed to pursue.
Generic integration categories — not implying formal partnerships.
Start with recommendations and simulation, then advance to human-approved or policy-controlled execution by action type and risk level.
Voertx can consume observability and energy intelligence from broader infrastructure systems, but its product identity is independent: it is the optimization, decision and execution engine.
A Ureka AI ProductConnect objectives, constraints and operating state into one continuously improving control system.