Training Data for Large Language Models

Instruction Tuning Datasets

High-quality prompt-response pairs with tone calibration, format diversity, multi-turn conversation support, and chain-of-thought reasoning annotation. We build datasets that cover the full instruction spectrum — creative writing, analytical reasoning, code generation, summarization, and domain-specific Q&A.

RLHF Preference Ranking

Side-by-side comparisons with detailed rubrics across helpfulness, accuracy, safety, verbosity, and style. Trained raters evaluate model outputs using Bradley-Terry pairwise comparisons, rubric-based ranking, and best-of-N protocols — producing the preference datasets that power reward model training and Direct Preference Optimization.

Safety & Policy Labeling

Content moderation, toxicity detection, PII identification, bias auditing, and policy-aligned safety labels. Our red team raters generate adversarial prompts across 200+ attack categories — jailbreaks, prompt injections, social engineering, and context manipulation — to systematically surface model vulnerabilities before deployment.

Multimodal Grounding

Image-text pairing, visual question answering, chart/diagram understanding, spatial reasoning labels, video-language alignment, and document understanding annotation. We produce grounding data that teaches multimodal models to accurately perceive, reason about, and describe visual content.

Evaluation Sets & Regression Suites

Curated evaluation benchmarks aligned to your model’s capability matrix — not generic public benchmarks, but domain-specific eval sets that measure what actually matters for your use case. Regularly updated to track regression, detect capability degradation, and validate improvements across model versions.

Tooling, Schema & Rubric Design

Before a single datapoint is labeled, we design the rubrics, edge-case libraries, annotation interfaces, and inter-rater calibration systems that ensure consistent output at scale. This upfront investment in annotation infrastructure is what separates production-grade LLM data from noisy crowd-labeled datasets.

Instruction-Response Pair

RLHF Preference Pair

Safety Red-Team Label

Foundation Model Labs

Pre-training data curation, instruction tuning at 100K+ scale, RLHF for alignment, and safety evaluation for frontier models. We support teams building GPT-class, Claude-class, and open-source foundation models.

Enterprise AI Teams

Fine-tuning datasets for domain-specific assistants — legal document review, medical Q&A, financial analysis, and customer support. Custom rubrics aligned to your brand voice, accuracy requirements, and compliance constraints.

AI Safety & Alignment Teams

Red teaming, safety labeling, constitutional AI data, and policy enforcement datasets. We help identify and label harmful content, bias patterns, and adversarial prompts — building the safety layer that protects users and organizations.

Product & ML Engineering Teams

Eval sets and regression suites for production LLM features. Track model quality across releases with domain-specific benchmarks. Detect capability regressions before they reach users.