Solutions Across Entire AI Supply Chain

Estimate probability to in-service and choose sites by combining queue delays, transformer lead-times, tariffs, carbon, and on-site generation options.

Reveal tariff, PPA, and carbon-mix tradeoffs to minimize cost and emissions for AI workloads; plan procurement and demand-response strategies.

Compare liquid vs air cooling, water sourcing risks, and thermal envelopes to reduce PUE and outage risk for high-density AI racks.

Model allocation shortfall and lead-time risk across HBM and advanced packaging; plan mitigations with second-source and program governance.

Prioritize regions and capacity by aligning latency SLOs, demand forecasts, power availability, and chip supply to minimize cost of scale.

Estimate $/token and total cost across training, fine-tuning, and inference stacks; expose sensitivities to utilization and power price.

Understand how serving topology, cache rates, and hardware tiers affect latency, reliability, and cost so teams can meet SLAs within budget.

Compare fine-tuning, retrieval, and prompt engineering to choose the most cost-effective customization path for your use case.

Translate enterprise/app demand into compute curves; plan capacity, routing, and spend with confidence.

Quantify upstream/downstream exposure, supplier concentration, and time-to-recover to improve fill rates and schedule confidence.

Benchmark scenarios, run sensitivities, and size risks for boards and investors evaluating AI infrastructure programs.

Aggregate emissions and water-use metrics across power → cooling → compute layers to quantify ESG exposure and decarbonization ROI.

Quantify supplier concentration, lead-time volatility, and rupture risk to improve service levels and schedule confidence.