Real results from production AI deployments. Names and specific details anonymized to protect client confidentiality.
A YC-backed startup building an AI customer service agent for e-commerce companies. The agent handled product recommendations, order management, and basic support escalation. Two weeks from public launch with several enterprise pilots scheduled.
The team had run extensive automated testing but had concerns about edge cases and security that their tests couldn't cover.
3 security researchers identified attack vectors specific to customer service context: order manipulation, identity spoofing, prompt injection via product descriptions
Systematic testing across 12 vulnerability categories with 400+ adversarial test cases
Severity assessment, reproduction steps, remediation guidance, and executive summary
"SliceLabel found vulnerabilities in three days that our automated red team missed entirely. The prompt injection through product reviews was particularly creative—we never would have thought to test that vector." — Head of Engineering, AI Agent Startup
A Series B digital health company developing an AI-powered clinical decision support tool for primary care physicians. Required human-validated evaluation data for FDA Pre-Submission meeting to demonstrate safety and effectiveness.
Worked with company's medical team and regulatory consultants to design evaluation schema aligned with FDA clinical decision support guidance
Recruited 8 board-certified physicians (4 primary care, 2 internal medicine, 2 specialists in target conditions) with verified credentials
2-day physician calibration workshop followed by annotation with blind multi-pass review
Complete methodology report, annotator credentials, and IRR analysis for FDA submission
"The FDA reviewer specifically commented on the quality of our evaluation methodology. SliceLabel's documentation was more thorough than what many larger companies submit." — VP Regulatory Affairs, Healthcare AI Company
A fintech company deploying an AI-assisted underwriting model that generated preliminary creditworthiness assessments. Model outputs were reviewed by human underwriters but influenced final decisions. Needed fairness evaluation before regulatory audit.
"The explanation quality disparity was something our automated fairness metrics completely missed. That finding alone was worth the entire engagement." — Chief Risk Officer, Fintech Lender
LLM-as-judge and rule-based testing consistently miss creative attack vectors and subtle quality issues.
Systems that pass automated benchmarks often fail on rare but important cases that require human judgment.
Regulators increasingly expect documented human evaluation methodology, not just automated metrics.
Specialized evaluators identify issues that generalist crowdworkers and automated systems overlook.
With the right team and processes, rapid iteration and rigorous evaluation can coexist.
Every AI system has unique evaluation needs. Let's discuss what rigorous human evaluation could look like for your specific challenges.
Contact us: hello@slicelabel.com