Talent
Vetted Professionals
Universities Represented
Expert Earnings Paid
A community who understands nuanced medical judgment, not just annotation instructions.
BAA-ready infrastructure, PHI de-identification protocols, and role-based access controls designed for compliance.
Every expert is assessed on our proprietary core AI curriculum, in addition to custom training developed for your project, ensuring you work only with the top of the class.
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Initial analysis of your model's gaps and challenges. Mutually define success metrics and evaluation priorities.
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Dedicated clinical team calibrates on sample tasks. Establish quality baselines and deliver initial annotations with full quality metrics to validate approach before committing to scale.
Once validated, ramp to full production volume with continuous quality monitoring. Dual review on statistically significant samples, weekly calibration sessions, and real-time dashboards to ensure consistency.
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Regular feedback loops capture edge cases and evolving safety concerns. We track inter-rater agreement trends, maintain error taxonomies, and help you measure annotation impact on model performance.
Challenge
Transform complex multi-cancer clinical trial protocols into consistent, structured eligibility criteria that a verifier model could evaluate.
Solution
Scaled a team of M1-M4 medical students trained on a standardized workflow to interpret oncology datasets and handle response discordance through consensus review.
Outcomes
• 94% inter-rater agreement across 6 cancer types
• Standardized data format enabled single survey template
• 72-hour turnaround with zero-cost expert replacement
• Model accuracy improved from 67% to 91% on eligibility determination
Challenge
Train a PHI detection model using thousands of real clinical documents, requiring fast, compliant annotation from reviewers who understand clinical context and terminology.
Solution
Built a 300-person team of US-based medical students using a secure iOS workflow. Reviewers highlighted and tagged all PHI entities in clinical notes and transcripts with entity-level classification and clinical context preservation.
Outcomes
• 98% PHI detection recall with clinically-aware annotators who caught context-dependent identifiers
• 3x faster throughput via mobile-first workflow enabling flexible scheduling
• Zero compliance incidents with US-based, HIPAA-trained workforce and BAA-compliant infrastructure
• <48 hour backfill for dropped annotators maintained consistent delivery
Challenge
Create a durable reasoning benchmark that exposes failure modes in frontier models without becoming obsolete after one training cycle—requiring prompts that are human-solvable but consistently difficult for LLMs.
Solution
Developed adversarial evaluation methodology using backward-engineered prompts from stable facts across academic, historical, and legal domains. Each prompt required 3/3 failure rate across leading models, verified ground-truth answers with citations, and detailed scoring rubrics before dataset inclusion.
Outcomes
• 100% prompt durability - all questions remained challenging across 4+ model generations
• 85%+ human solve rate vs. <33% initial model accuracy, validating adversarial effectiveness
• Production-ready evaluation suite with answer keys, rubrics, and automated verification infrastructure
• Enabled systematic tracking of reasoning improvement across model releases
