Methodology: Track A — engineer saving based on 65% recovery of time spent on optimization, consistent with TurinTech internal project observations (tasks reduced from weeks to days; conservative estimate against an observed 75–80% recovery range). AI & API cost saving scales from 25% to 35% based on production workload count, informed by TurinTech-published results: −26% cost per token on vLLM · Qwen3-4B AWQ · Intel Xeon CPU (inference optimization), and −36.9% token spend reduction on CrewAI multi-agent workflows (agentic software). Compute saving at ~20% baseline, informed by Artemis inference results: +25% faster inference on Nvidia GPU (Whisper) implies ~20% reduction in GPU-hours for equivalent workloads. Track B — improvement range (3–36%) informed by published Artemis results: +3% vehicle routing throughput (planning & scheduling); +11.9% faster runs on dbt/Snowflake pipelines (data pipeline); +32.7% faster runtime on QuantLib C++ (latency-critical infra). Business outcome saving = annual cost base × improvement %. Artemis engagement cost estimated at 15% of Track A savings — actual contract value varies by scope. Figures are illustrative estimates based on your inputs; actual results vary by workload, team structure, and engagement scope. This calculator is for illustrative purposes only and does not constitute financial or professional advice. Contact TurinTech for a workload-specific assessment. · turintech.ai