Expert-in-the-Loop
QA
The use cases where generalist review isn't enough — where the difference between a good output and a dangerous one requires a doctor, a lawyer, or a specialist who has spent a career in the domain.
Problem scenarios that require genuine expertise
Scenario: A health AI startup is training a clinical decision support model. RLHF preference data is rated by generalist annotators briefed on medical guidelines. Outputs that are fluent but clinically incorrect are frequently rated as high quality.
Fuzu Atlas approach: MD-verified reviewers inserted as the QA layer. Generalist annotation continues for format and fluency; medical experts review clinical accuracy separately. Two-layer review structure with distinct mandates.
Scenario: LegalTech SaaS uses an LLM to generate contract clauses. The team needs human QA on outputs but can't hire in-house solicitors for every jurisdiction their clients operate in.
Fuzu Atlas approach: Jurisdiction-matched solicitors and compliance officers on demand. Review tasks structured around specific clause types and risk categories. Findings logged with jurisdiction tag for model feedback loops.
Scenario: AI financial analysis tool generating earnings summaries and ratio interpretation. Outputs look authoritative but contain IFRS/GAAP framing errors that non-finance reviewers can't identify.
Fuzu Atlas approach: CFA charterholders and ACA-qualified reviewers validate financial accuracy. Error taxonomy built around common model failure modes in financial reasoning. Periodic calibration with client's finance team.
Scenario: AI tutoring platform generates science and mathematics explanations for students. Content is pedagogically sound at surface level but contains conceptual errors that only subject-specialist teachers notice.
Fuzu Atlas approach: Subject-specialist educators review for conceptual accuracy, age-appropriate framing, and curriculum alignment. Separate review tracks for different subjects and education levels.
How expert review is structured — not ad-hoc consulting
The difference between a one-off expert review and a repeatable expert QA layer is structure. Fuzu Atlas builds the structure.
Expert Matching
Domain, sub-specialty, and jurisdiction-matched experts pre-screened and credential-verified before assignment.
Review Protocol Design
Error taxonomy and review rubric co-designed with your team. Experts briefed on your specific model failure modes.
Structured Review
Outputs reviewed against defined rubric. Errors categorised, severity-rated, and logged with correction notes.
Feedback Loop
Review findings structured for model feedback. Error patterns reported at aggregate level for training improvements.
Related solutions & industries
Need domain experts in your QA pipeline?
Tell us the domain — we'll match the specialists and design a repeatable review structure.