GenAI answers users can trust
Expert preference ranking that makes your AI reliable at scale.
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Frontier
AI Lab
Leading
Smartphone Maker
Major
Social Platform
Top GenAI
Assistant Maker
Leading
Search Engine
Leading Global
Ecommerce
Frontier
Robotics Studio
Iconic
Smart Eyewear
World-Leading
Creative AI Platform
Premium
Automotive AI
Top-20
US Mortgage Lender
Major
Crypto Trading Platform
Global
Banking-as-a-Service
Emerging
BNPL Fintech
Global
Card Issuer
Tier-1
FinCrime Portfolio
Market forces
Why preference quality defines alignment quality
The reward model learns what you tell it to value. If the raters don't understand the domain, neither will the model.
Benchmark Validity
42%
of test items in widely used NLP benchmarks contain label errors, meaning models are evaluated and rewarded against systematically wrong human judgments from the start.
Balloccu et al., 2024
Alignment Gap
Systematic
Sycophancy, where models agree with users even when incorrect, is a documented and systematic failure mode of RLHF. Published research demonstrates that RLHF reward models consistently learn to optimize for rater approval rather than correctness when rater expertise is insufficient.
Perez et al., Anthropic
Regulatory Pressure
Aug 2026
EU AI Act full compliance deadline. High-risk AI systems must demonstrate training data governance, including human oversight records, preference data provenance, and alignment methodology documentation.
EU AI Act Official Text
Proof in production
Preference signals with verifiable uplift
Preference signals tied to measurable, verifiable uplift in the reward model.
83.7%
Clinical plausibility score (up from 61% baseline) achieved through RLHF preference ranking by certified medical specialists on a clinical AI program.
5,000
Audio Q&A preference tasks completed across 8 music genres, covering melody recognition, harmonic analysis, lyric comprehension, and production quality assessment for music intelligence RLHF.
300K+
Voice preference evaluations completed across 9+ languages, covering naturalness, accent accuracy, prosody, and speaker consistency for a voice AI alignment program.
Across the GenAI lifecycle
Preference work across the alignment lifecycle
RLHF is not a single phase. Preference signals shape the model at pre-training curation, fine-tuning reward modeling, post-training alignment, and deployment red-teaming. Each phase requires different rater expertise.
Phase 01
Pre-Training
Expert raters evaluate content quality and factual accuracy before data enters the training corpus, distinguishing authoritative sources from plausible-sounding noise at a resolution generalist filters cannot match.
Phase 02
Fine-tuning
We rank model outputs against task-specific quality criteria including accuracy, relevance, and domain-appropriate reasoning, and specialists in the target domain produce reward signals aligned with real standards.
274K-prompt alignment program
We ran side-by-side preference ranking across 274,000 unique prompts, evaluated against the Harmless, Helpful, and Honest axes by domain specialists.
Foundational model program · Multi-domain specialist coverage
Phase 03
Post-training
Iterative preference cycles align the model to human values. Domain specialists evaluate whether underlying reasoning is sound, a distinction generalist raters systematically miss in high-stakes content.
Clinical AI plausibility scoring
Preference ranking by certified medical specialists raised clinical plausibility from 61% to 83.7%, clearing evaluation layers for regulated hospital deployment.
Clinical AI Venture · 14 hospital networks
Phase 04
Deployment and monitoring
We collect ongoing preference signals from production scenarios including adversarial prompts, edge cases, and policy-boundary tests, and continuous collection keeps the reward model calibrated as user behavior evolves.
Program spotlight
274,000 prompts. Three alignment axes. One program.
274,000 prompts ranked across three alignment axes inside a single program.
Client Program
Harmless. Helpful. Honest.
A foundational model program required side-by-side preference ranking across 274,000 unique prompts, spanning STEM, legal, clinical, and creative content, evaluated against Harmless, Helpful, and Honest axes simultaneously.
Firstsource assembled domain specialists per content category, calibrated to constitutional rubrics before first delivery. Each ranking required domain knowledge the model already lacked, which is precisely what made the preference signal worth training on.
Firstsource assembled domain specialists per content category, calibrated to constitutional rubrics before first delivery. Each ranking required domain knowledge the model already lacked, which is precisely what made the preference signal worth training on.
Program dimensions
274K
Unique prompts ranked
3
constitutional alignment axes
Side-by-side
ranking methodology
Multi-domain
specialist coverage
Where it applies
Where expert preference ranking drives production outcomes
Where that ranking decides whether alignment holds in production.
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AI labs and foundational models
Frontier models that pass safety benchmarks require preference signals from domain specialists who can distinguish plausible from correct across STEM, legal, and clinical content.
Learn more
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Technology
On-device assistants and multimodal systems need preference ranking by evaluators who understand the interaction context, not generalists optimizing for surface-level fluency.
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Robotics
Vision-language-action models learning manipulation tasks require human preference signals grounded in physical feasibility, not just language plausibility.
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Banking and financial services
Compliance, credit, and FinCrime models depend on preference ranking by credentialed financial professionals who can spot reasoning errors that generalist raters consistently miss.
Learn more
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How we deliver
Preference data is only as good as the people providing it
Domain-matched raters, calibrated rubrics, and QA that protects the reward signal.
Evaluate
H·H·H
Harmless · Helpful · Honest
We build constitutional rubrics with clients and calibrate them to their alignment targets, and raters are trained on domain-specific interpretations of each axis before producing a single preference judgment.
Gigsourcing
Domain
Specialists, not generalists
We assemble clinical, financial, legal, and technical raters per program via our Gigsourcing infrastructure, run calibration before first delivery, and monitor inter-rater agreement continuously throughout.
Quality Assurance
Multi-layer
Calibration at every stage
We run an independent reviewer pass, schema-compliance validation, inter-annotator agreement scoring, and client-gate sign-off before any preference batch ships, and rater calibration is continuous rather than one-time.
INSIGHTS
Latest from the Firstsource team
Insights from the field, real operations, real outcomes, and perspectives from the people making it work in live operations.
Contact us
Talk to an RLHF lead
Tell us what you're aligning. We'll scope the preference program.
- Domain and specialist requirements.
- Constitutional rubric design.
- Preference volume and throughput targets.
- Alignment axes and quality thresholds.
- Languages and locale coverage needed.