Full technical capability taxonomy.
Every engagement is staffed from one or more of the practices below. Each practice is led by specialists matched to the credential and depth the task requires.
Human evaluators rank and rate model outputs, teaching the reward model what good looks like. Results in models that are more helpful, coherent, and aligned with real user intent across text, code, and reasoning.
Human-written demonstrations establish baseline model behavior. CoT training teaches structured, step-by-step reasoning for complex tasks. The foundation every well-aligned model is built on — before RLHF begins.
Expert evaluators for vision-language models, audio understanding, and multimodal reasoning. Performance tested against real-world tasks, not benchmark datasets. Coverage scales with your model's modality footprint.
Native speaker annotators for ASR, TTS, and conversational AI. Multilingual evaluation with cultural adaptation — not translation. Covers 50+ languages.
Annotators for text, image, audio, video, LiDAR, and structured data. Domain specialists for medicine, law, finance, coding, and science — where general annotators produce incorrect labels. Every label traceable.
Systematic adversarial testing by domain specialists. Jailbreaks, bias, harmful outputs, and safety violations across text, code, and multimodal systems. Structured findings with reproduction steps and recommended fixes.
Specialists verify model outputs against source documents, trace citations, and flag hallucinations with reproduction steps. Built for AI products where a wrong answer carries real-world consequence.
Model risk review, bias audits, and compliance documentation that stands up to enterprise procurement and regulatory inquiry. Built for AI products entering regulated markets.
Specialists and ontologists who design entity models, taxonomies, and relationship schemas for domain-specific AI. For products where meaning and context matter more than surface text.
Response quality scoring, safety evaluation, cultural adaptation analysis, and headroom analysis for AI agents and foundation models. Side-by-side evaluation, localization testing, and production drift detection. Includes agentic reasoning evaluation — multi-step tool use, planning trajectories, and end-to-end workflow quality.
Content quality reviewers, trust and safety specialists, and search quality raters who keep AI-powered products accurate and policy-compliant at scale. Ongoing operations programs that scale with your product.