Expert-Led RLHF & Training Data for Frontier AI Models
Move beyond generic annotator feedback. Our trained specialists with deep domain expertise deliver the nuanced human preferences and safety evaluations that shape frontier language models, vision systems, and multimodal AI.
Problem Statement
Advanced AI requires expert human feedback. Generic crowdworkers often create noisy data that slows training and impacts model performance. BergLabs provides expert-led annotation, combining domain expertise with structured evaluation—from RLHF preferences to safety testing—to deliver reliable data for high-performing AI systems.
Detailed Capabilities
RLHF Annotation & Preference Data
RLHF requires a higher level of evaluation than standard annotation. Annotators assess model outputs across accuracy, reasoning, style, safety, and intent-providing calibrated preferences that act as reward signals. We support pairwise comparisons, multi-way rankings, and dimension-based scoring.
Our specialists understand that their feedback shapes model behavior and are trained to your exact standards-not generic crowdworkers. With clear guidelines and edge-case rules, BergFlow monitors agreement rates and escalates disagreements for senior review. We scale to your timeline, delivering reliable results 24/7 across time zones.
Our specialists understand that their feedback shapes model behavior and are trained to your exact standards-not generic crowdworkers. With clear guidelines and edge-case rules, BergFlow monitors agreement rates and escalates disagreements for senior review. We scale to your timeline, delivering reliable results 24/7 across time zones.
Supervised Fine-Tuning Data Preparation
Strong RLHF starts with high-quality SFT data. We handle data curation, correction, and formatting-ensuring prompts, completions, and metadata are training-ready.
For domains like healthcare or finance, our experts validate outputs against professional standards. We also prepare polished few-shot examples. This strong SFT foundation reduces training cycles and speeds up production readiness.
For domains like healthcare or finance, our experts validate outputs against professional standards. We also prepare polished few-shot examples. This strong SFT foundation reduces training cycles and speeds up production readiness.
Red Teaming, Safety Evaluation & Benchmarking
Production AI needs strong safety checks before release. Our red teaming uses trained specialists to find failure modes, risks, and misalignment—tailored to your domain, whether finance, healthcare, or beyond.
We build custom evaluation frameworks and benchmark your model against industry and academic standards. This ensures issues are caught early and supports responsible, confident deployment.
We build custom evaluation frameworks and benchmark your model against industry and academic standards. This ensures issues are caught early and supports responsible, confident deployment.
Custom Reward Modeling & RL Gym Environments
Beyond standard RLHF, we support advanced training methods like custom reward models. Our specialists define what “good output” means in your domain and create data to train reward models that capture nuanced quality signals.
For robotics and control systems, we design custom RL environments with defined behaviors and reward structures. Whether it’s language, vision, or robotics, we provide the expert feedback needed for advanced model training.
For robotics and control systems, we design custom RL environments with defined behaviors and reward structures. Whether it’s language, vision, or robotics, we provide the expert feedback needed for advanced model training.
How It Works
3-Step Process
Training & Calibration
AI training feedback needs expert preparation-not crowdsourcing. We first understand your model’s capabilities, goals, and use case, then review sample outputs and success criteria.
We select annotators with relevant domain expertise and train them on industry context, RLHF methodology, and calibration standards. This 1-2 week setup ensures high-quality, responsible training signals.
We select annotators with relevant domain expertise and train them on industry context, RLHF methodology, and calibration standards. This 1-2 week setup ensures high-quality, responsible training signals.
Preference Annotation & Continuous Recalibration
Once calibrated, annotators evaluate model outputs using BergFlow’s optimized interfaces-side-by-side comparisons, rankings, or detailed scoring. We track agreement rates, score patterns, and individual performance in real time.
Disagreements are flagged and reviewed by senior experts to keep signals consistent. Weekly calibration sessions address new edge cases and prevent quality drift in long-running projects.
Disagreements are flagged and reviewed by senior experts to keep signals consistent. Weekly calibration sessions address new edge cases and prevent quality drift in long-running projects.
Analysis & Feedback Integration
After annotation, we deliver more than raw preference data. Our team analyzes consistency, key quality drivers, and potential biases in annotator decisions.
We provide detailed reports on performance and agreement rates, ensuring the data meets your standards. You can integrate it into your RLHF pipeline with confidence in its accuracy and domain expertise.
We provide detailed reports on performance and agreement rates, ensuring the data meets your standards. You can integrate it into your RLHF pipeline with confidence in its accuracy and domain expertise.
Key Metrics & Differentiators
Expert Annotators, Not Crowdworkers
Every RLHF annotator brings relevant domain expertise—finance professionals for financial AI, physicians for medical AI, engineers for technical systems. This expertise produces training signal that generic annotators cannot match.
Systematic Calibration
Continuous calibration against gold standards and peer review catches preference drift before it corrupts your training data; prevents the annotation quality degradation typical in long-running projects.
Agreement Tracking & Analysis
We measure and report annotator agreement, identifying scenarios where expert judgment diverges; provides transparency into where your training signal is strongest and where it's ambiguous.
Domain-Specific Safety Evaluation
Red teaming conducted by specialists who understand your industry, regulatory environment, and risk profile; identifies failure modes generic evaluators would miss.
Training Signal Optimization
Our analysis helps you understand what your annotators' preferences reveal about quality in your domain, enabling smarter training objective design.
Frontier Model Experience
Our team has directly contributed to RLHF annotation for leading frontier models; we understand the rigor required and the impact of feedback quality on model performance.
- Expert Annotators, Not Crowdworkers: Every RLHF annotator brings relevant domain expertise—finance professionals for financial AI, physicians for medical AI, engineers for technical systems. This expertise produces training signal that generic annotators cannot match.
- Systematic Calibration: Continuous calibration against gold standards and peer review catches preference drift before it corrupts your training data; prevents the annotation quality degradation typical in long-running projects.
- Agreement Tracking & Analysis: We measure and report annotator agreement, identifying scenarios where expert judgment diverges; provides transparency into where your training signal is strongest and where it's ambiguous.
- Domain-Specific Safety Evaluation: Red teaming conducted by specialists who understand your industry, regulatory environment, and risk profile; identifies failure modes generic evaluators would miss.
- evaluators would miss. Training Signal Optimization: Our analysis helps you understand what your annotators' preferences reveal about quality in your domain, enabling smarter training objective design.
- Frontier Model Experience: Our team has directly contributed to RLHF annotation for leading frontier models; we understand the rigor required and the impact of feedback quality on model performance.
The quality of human feedback defines the quality of your AI systems. Frontier models need expert feedback-not generic crowdworkers. Partner with specialists to build aligned, high-performing AI.
