Scorecard solutions for data centers and AI infrastructure

Test whether a project can get power on time and what is most likely to delay in-service.

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.

Test whether HBM, advanced packaging, or allocation bottlenecks are likely to delay a GPU program.

Compare which regions can support the next expansion and what constraints are likely to block them.

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.

Forecast serviceable demand, headroom, and cost pressure before capacity or SLA problems show up.

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

Pressure test whether a project still underwrites after the main power, timing, utilization, and supply downside cases.

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