Reliable Human Feedback
for Production-Grade AI

Your models are only as good as the human judgment behind them. We provide expert-led RLHF, systematic evaluation frameworks, and domain-specific benchmarking, built for teams shipping models to production.

Trusted by teams at

SuperAnnotate Sanctifai Alegion Moreton Bay Technologies Intentsify Emesent Rovio TicTag SND Good Luck Group

Why Model Quality Breaks Down

Most model evaluation misses what matters. Generic annotators overlook domain nuance, subjective evaluation drifts without calibration, and offline benchmarks don't reflect real-world performance. The result: models that look good on paper but fail in production.

Subjective Drift
Without expert calibration, human feedback becomes inconsistent. Your reward model ends up learning noise instead of signal.
Domain Blindness
Generic annotators can't evaluate medical reasoning, STEM accuracy, or code correctness. Domain depth requires domain expertise.
Benchmark Gaps
Standard benchmarks measure what's easy to test, not what matters. Custom evaluation frameworks catch the failures that matter to your users.
Bias & Safety Gaps
Undetected bias and safety failures only surface in production, where they're far more expensive to fix.

Three Pillars of Model Quality

Each offering is designed to slot directly into your existing model development workflow, not replace it.

01

Reinforcement Learning from Human Feedback (RLHF)

A training method where human evaluators rank and rate model outputs so the model learns to produce responses aligned with human preferences and values.

Preference ranking & comparison
Instruction-following evaluation
Safety & alignment signals
Domain-specific reward modelling
02

Model Evaluation & Benchmarking

Systematic testing of model outputs against custom criteria to measure accuracy, safety, and real-world performance across versions.

Custom eval framework design
Version-to-version comparison
Scoring rubrics & criteria
Regression monitoring
03

Domain-Specific Evaluation

Expert assessment by qualified professionals in fields like STEM, healthcare, and finance where accurate evaluation requires genuine subject-matter knowledge.

STEM & scientific reasoning
Healthcare & clinical review
Finance & compliance domains
Multilingual & Indian languages

Expert-Led, Not Crowdsourced

We don't flood your pipeline with volume from anonymous crowds. Every evaluator is vetted, trained on your specific guidelines, and embedded in a QA system designed for consistent, expert-level judgment.

1

Expert-Curated Evaluators

Specialists selected and screened for the exact domain, task type, and difficulty level your model requires.

2

Human-in-the-Loop by Design

AI-augmented workflows with mandatory expert review. Delivering throughput without sacrificing judgment.

3

Secure & Scalable Operations

NDA-protected teams working in controlled environments, scaling from pilot to production without quality degradation.

Why Teams Choose Us

Calibrated Consistency
Inter-annotator agreement tracked and maintained through continuous calibration sessions, not left to drift.
Domain Depth
STEM PhDs, senior developers, and licensed clinicians. Evaluators who understand the subject matter, not just the annotation interface.
Production Velocity
AI-assisted pre-screening with expert human review, scaling to thousands of evaluations per day without compromising quality.
Model Lifecycle Fit
We fit into your existing pipeline, from pre-training data validation to post-deployment regression monitoring.

Your Model Lifecycle, Our Evaluation Layer

We provide expert human evaluation at every stage, not just one-off annotation before launch.

Stage 1
Pre-Training
Training data quality assessment, domain coverage validation, and bias detection in source datasets before model training begins.
Stage 2
SFT
Supervised fine-tuning data creation and review: expert-written demonstrations, instruction-response pairs, and task-specific training examples.
Stage 3
RLHF Alignment
Preference ranking, reward model training data, safety alignment evaluation, and iterative human feedback loops during the alignment phase.
Stage 4
Pre-Deployment
Custom evaluation suites, red-teaming exercises, domain-specific benchmarking, and safety audits before your model reaches users.
Stage 5
Post-Deployment
Ongoing regression monitoring, production output sampling, user-reported issue validation, and continuous model quality assurance.

Built for Teams Shipping Models to Production

Whether you're a platform scaling annotation operations or a lab fine-tuning your next model version.

Production Owners at AI Labs

You're responsible for model quality at scale. You need consistent, expert-level human feedback without managing a workforce yourself.

Applied AI Leads at Startups

You're building fast and need RLHF that keeps pace. Expert evaluation without the overhead of recruiting and training your own evaluators.

MLOps Platform Teams

You're serving multiple AI teams and need a reliable expert data layer that integrates with your platform, not another vendor to manage.

View All Case Studies

Ready to Discuss an Evaluation Setup?

Tell us about your model, your evaluation challenges, and your quality bar. We'll design the right expert operation for your needs.