STOKES AI: Revolutionizing the AI Landscape

Providing expert-level RLHF and SFT data and eval services to AI labs and research teams to train LLM’s and generative AI models.

To advance the frontier of truthful, reliable, and high-performing AI by delivering expert-crafted training data for RLHF and Supervised Fine-Tuning. We envision a future where AI systems reason more accurately, communicate more clearly, and align more effectively with human values—especially in high-stakes, STEM-intensive domains such as mathematics, engineering, and scientific problem-solving.

By combining domain-specific expertise with scalable, human-in-the-loop evaluation processes, our mission is to become a trusted partner for AI producers and AI research labs developing the next generation of intelligent models. In a world where overreliance on a narrow set of data sources leads to overfitting and bias, we offer a diverse, high-fidelity alternative—one that complements existing pipelines and strengthens the robustness and generalization of large language models.

We deliver high-quality, deeply-vetted data with strict quality controls and flexible workflows tailored to your AI model’s goals. Our team believes the best AI is trained on diverse, reliable sources, not just a single pipeline. Relying too heavily on one source for AI training risks overfitting and blind spots. With Stokes AI, you gain a trusted partner that strengthens your data strategy with precision, diversity, and human insight.

Our vision

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Preference Ranking

We evaluate and rank and compare AI-generated responses using structured frameworks, including Likert scales and multi-dimensional labeling. Each output can be rated on attributes like truthfulness, instruction following, verbosity, and harmlessness. This granular feedback helps models learn not just what the “best” response is, but why it’s preferred—pushing them toward more helpful, safe, and human-aligned behavior.

Adversarial prompting

Our team specializes in writing adversarial prompts that expose model vulnerabilities, such as to elicit hallucinations, logical inconsistencies, or policy-breaking behavior. These stress-test prompts are essential for identifying blind spots and edge cases, ensuring models behave reliably even in challenging or unexpected situations.

Domain-Specific EXPERTISE

Our team includes domain experts from fields such as mathematics, psychology, finance, and history. These reviewers evaluate AI responses within their area of expertise for factual accuracy, truthfulness, and conceptual depth. This ensures that model behavior remains robust and trustworthy when operating in technically or culturally nuanced areas that require more than surface-level understanding.

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