Distinct agents, assistants, or models being evaluated
Who writes tests and reviews outputs today, and what they cost you. Fractional headcount is fine.
Get a tailored estimate and see Galtea evaluate your actual AI outputs.
Book a consultation →Every figure comes from the inputs you just gave — no hidden numbers. Hours are priced at each role's loaded salary ÷ 1,760 productive hours/year.
products × releases/yr × test cases × min each ÷ 60, priced at the QA/domain blended rate.products × releases/yr × oversight hrs, priced at the ML engineer rate.annual interactions × sample % × min each ÷ 60, priced at the QA/domain rate.Benchmarks: annotation 2–5 min/case & manual-eval throughput (Shakudo, enterprise LLM evaluation at scale); Abanca outcomes (Galtea reference); 357% 3-yr ROI (Forrester TEI on AI observability). Mid-points are conservative and fully editable.
Figures are directional estimates grounded in public benchmarks and Galtea customer data (Abanca: 71% cost reduction, 600 hrs saved). This models operational savings only — it does not include regulatory risk costs (EU AI Act fines up to €15M or 3% global turnover) or reputation-related losses from quality failures.
Manual review doesn't scale with your product.
Most teams manually review only a small slice of production traffic — often under 5%. Covering 100% manually would take dozens of full-time reviewers, at which point the question stops being about cost and becomes one of feasibility. Galtea evaluates every interaction continuously and routes only the flagged edge cases to humans.
The bigger risk isn't wasted engineering time.
For regulated and customer-facing AI, the real exposure is the EU AI Act's high-risk obligations — fines of up to €15M or 3% of global turnover — plus the reputational cost of a quality failure in production. This calculator only measures the operational savings; it deliberately leaves the larger risk unpriced.