RLHF Services

Optimize your LLMs with Reinforcement Learning From Human Feedback (RLHF) & Supervised Fine-Tuning (SFT)

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Get access to talented and diverse subject matter experts for optimizing your AI Models.

High-quality outputs enhanced by working with experts in their domains…

Developers

Linguists

Finance Experts

STEM Specialists

Marketers

Lawyers

Medical Doctors

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Flexible Labeling Formats

Stack Ranking

Quantitative Scoring

NER Labeling

Expert-powered
datasets across industries

Building the Future of Coding Copilots.

Problem

Traditional AI coding assistants struggle with context and best practices, leaving developers with generic suggestions.

Solution

Leverage RLHF to empower experts to verify and refine code generated by your large language model (LLM). This human feedback continuously improves the LLM's ability to produce context-aware, high-quality code snippets tailored to specific needs.

Strengthening Legal AI Assistants.

Problem

Legal AI assistants can benefit from additional verification to ensure outputs align with complex legal nuances.

Solution

Integrate RLHF to allow certified lawyers to review and verify outputs from your legal AI. This expert feedback ensures adherence to legal regulations and strengthens the accuracy and reliability of the assistant's recommendations.

Improving Healthcare AI Models.

Problem

Healthcare diagnosis models require continuous improvement to maintain accuracy.

Solution

Involve best-in-class medical professionals in ranking the accuracy of existing outputs from your diagnosis models. This expert feedback allows for targeted refinement and optimization, leading to more reliable and accurate diagnoses.

Elevating Personal Assistant AI.

Problem

Evaluating the effectiveness of personal assistant AI is crucial for user satisfaction.

Solution

Implement RLHF to allow target consumers to directly evaluate the outputs they receive from your personal assistant AI. This real-world feedback helps identify areas for improvement and ensures the assistant evolves to better serve user needs.