Train Frontier Models on Credentialed Expert Preferences
Replace crowd workers with verified physicians, lawyers, and engineers. RLHF data from people who actually understand the domain.
Why Frontier Models Fail Without Expert Preferences
Frontier models recognize patterns but often miss clinical, legal, and expert-level nuance. When feedback lacks domain expertise, models learn mediocrity instead of accurate reasoning.
Quality Ceiling
- Crowd agreement on nuanced tasks: 40–50%
- Expert agreement on same tasks: 80%+
- Your model trains on noise or clarity?
Compliance Risk
- Crowd-trained models fail regulatory audit
- Expert-trained models pass on first submission
- Cost of failed audit: $2M+ delay
Competitive Moat
- Competitors using crowd RLHF plateau quickly
- You using expert RLHF reach next level
- Market shifts to quality-first, not speed-first
Our RLHF Process: Built for Rigor
We've engineered a preference-data pipeline that prioritizes quality at every step. No shortcuts. No crowd workers. Credentialed experts. Rigorous calibration. Transparent methodology.
Step 1: Preference Pair Collection
Expert annotation starts with a clear task: "Between these two model responses, which is better?"
The difference from crowd:
- Crowd: Prefers the longer response (surface-level)
- Expert: Prefers the response with correct clinical reasoning (substance)
We collect 100s or 1000s of preference pairs depending on your model size. Each one shaped by domain expertise.
Step 2: Rubric Design & Calibration
Before annotation begins, all experts must agree on a shared standard. We co-design a rubric with your team:
- 4–6 evaluation criteria specific to your domain
- Real examples of what "better" means
- Calibration round: All experts grade the same examples
- IRR measurement: We don't proceed until agreement ≥ 0.78
This is the step most teams skip. We don't.
Step 3: Large-Scale Annotation & QA
Once calibrated, annotation scales fast. But quality doesn't slip. Why?
Tiered review system:
- Tier 1: Expert annotates
- Tier 2: Senior QA reviewer spot-checks 20%
- Tier 3: Adjudicator resolves disagreements
- Tier 4: Analytics monitors agreement rates in real-time
If agreement drops below threshold, we pause, investigate, re-calibrate, restart.
Step 4: Secure Delivery
Your preference data delivered in your format:
- JSON/CSV with metadata
- Encryption in transit & at rest
- HIPAA-ready infrastructure
- DPA & subprocessor transparency
- No data shared for training our models
Ready to see how this works for your domain?
What Sets Our RLHF Apart
Credential-Verified at Scale
Every annotator verified via SAIRA (our verification engine). Medical licenses checked against state boards. Bar admissions verified. GitHub histories audited. No crowd workers. No anonymous annotators.
Published Quality Standards
We publish our quality framework publicly. IRR targets. Calibration process. Dispute resolution. You can audit us. See the methodology. Verify the numbers.
Fast Onboarding, No Quality Drop
Pre-vetted network means onboarding happens in days, not months. 50 physicians ready in 3 days. No training curve. No ramp-up period. Production annotation by day 5.
Domain-Specific Annotation
Medical RLHF uses physicians. Legal uses attorneys. Code uses engineers. Wrong domain = wrong preferences = wrong model. We match expert type to domain automatically.
Enterprise-Grade Security
SOC 2 Type II audited annually. HIPAA-ready. GDPR compliant. ISO 27001 certified. Zero-trust architecture. Your data never leaves our secure infrastructure.
No Hidden Costs
Three pricing models: per-pair, retainer, or custom. No surprise fees. No vendor lock-in. You know exactly what you're paying and why.
Quality Dashboards
Real-time agreement rates. Quality metrics per domain. Annotator performance tracking. Alerts if quality drifts. You see everything happening.
Scale Without Hiring
Need to 5x your annotation volume? We can. Our network scales with demand. No long hiring cycles. No training overhead. Just more experts, same quality.
Your model is only as good as the experts behind your data
AI Labs & Frontier Models
Frontier labs train on expert RLHF. Medical, legal, agentic, multilingual we have the experts.
Healthcare AI
Medical AI requires physicians. Regulatory compliance depends on it. Audit trails demand documentation of rigorous RLHF.
Legal AI
Legal reasoning needs attorneys. Case law requires expertise. Compliance regulations demand proven RLHF process.
FinTech & Financial AI
Financial reasoning, risk assessment, trading logic. CPAs and financial analysts verify correctness.
Robotics & Autonomous Systems
Safety-critical systems need engineers. Autonomous vehicles require rigorous testing and verification.
Government & Public Sector
Government AI faces unique compliance, transparency, and audit requirements.
Enterprise & SaaS
Enterprises building AI features need domain experts specific to their vertical.
Your RLHF Journey: From Kickoff to Production
Phase 1: Scoping & Discovery (Days 1–2)
What Happens:
- Initial call: understand your model, domain, timeline
- Rubric design workshop with your ML team
- Domain expert sourcing plan (which experts do we need?)
Your Deliverable: Approved rubric + expert sourcing plan
Sourcebae Deliverable: Candidate expert list for your review
Results: What RLHF with Credentialed Experts Delivers
3.2x
Average performance lift on downstream benchmarks Source: Medical lab case study
0.82+
Cohen's kappa (expert) vs. 0.45–0.55 (crowd) Higher agreement = stronger preference signal
3 days
Time from decision to first expert producing annotations Note: vs. 2–3 months for internal hiring
95%
Regulated AI models trained on expert RLHF pass audit on first submission
Trust Built on Transparency
Pillar 1: Calibration
- All experts calibrate on gold sets before production
- IRR measured using Cohen's kappa
- Minimum threshold: 0.78 (we don't proceed if lower)
- Real data: Medical RLHF achieves 0.82+ consistently
Pillar 2: Tiered Review
- Tier 1: Expert annotation
- Tier 2: 20% QA spot-check
- Tier 3: Adjudication for disagreements
- Tier 4: Real-time analytics monitoring
Pillar 3: Continuous Monitoring
- Real-time agreement rate tracking
- Per-domain performance dashboards
- Alert system if quality drifts
- Weekly quality reports
Pillar 4: Methodology Transparency
- We publish our full quality framework
- You can audit our process
- No black boxes
- Third-party auditable
Cert 1: SOC 2 Type II
- Annual third-party audit
- Access controls, encryption, monitoring
Cert 2: HIPAA
- PHI handling certified
- Business Associate Agreements available
- Medical data infrastructure
Cert 3: GDPR
- EU data handling compliant
- Data Processing Addendum (DPA)
- Right to deletion, data portability
Cert 4: ISO 27001
- Information security management
- Annual recertification
- Industry standard compliance
Security Highlights:
- Zero-trust architecture
- Encryption at rest & in transit
- No data sharing for model training
- Secure facility VDI options
- Subprocessor transparency
The Complete RLHF → Evaluation → Safety Pipeline
Service 1: Model Evaluation
After you train on RLHF preferences, you need to validate the training work. Our domain experts evaluate your model outputs on your most critical criteria.
Use When: Model is trained; you need audit-ready proof of quality
Service 2: Red Teaming & Safety
Adversarial testing by experts. Physicians look for clinical errors. Lawyers look for bad legal advice. Engineers look for security bugs.
Use When: Model is evaluated; you need safety validation before production
Service 3: Expert Data Collection
Beyond RLHF—need raw expert data? Domain-expert surveys, interviews, knowledge extraction. Structured data collection from verified experts.
Use When: You need expert knowledge but not preference pairs
Built to Integrate With Your Workflow
RLHF Pipeline Integration: Your preference data integrates with standard training pipelines (Hugging Face, PyTorch, etc.). We deliver JSON/CSV + metadata. Your engineers handle integration. Format Support: JSON (our default), CSV, Custom XML, Direct API
QA Tool Integration: Preference pairs tagged in your QA systems. Metadata (annotator, IRR, flags) included for tracking.
Analytics Integration: Quality metrics exportable to your dashboards (Tableau, Looker, etc.)
Security Integration: DPA compliant. Subprocessor list available. Data transfer via secure channels (SFTP, encrypted S3, etc.)
Frequently asked questions
RLHF = Reinforcement Learning from Human Feedback (humans pick preferences). RLAIF = Reinforcement Learning from AI Feedback (AI picks, humans verify). RLHF is the gold standard for expertise-dependent domains (medical, legal). RLAIF is faster for generalist tasks.
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