An AI evaluation and post-training data company specializes in finding, verifying, and managing human experts who can teach and test large AI models: scoring outputs, generating preference and demonstration data, auditing behavior, and surfacing failure modes that traditional crowd platforms miss. NewtonX sits between classic B2B research and pure data vendors: we bring research-grade recruiting, methodologies, and QA to model evaluation, post-training datasets, and independent quality assurance for AI labs and data providers.

Unlike generic marketplaces, we don’t just assemble “raters”; we identify domain specialists whose day jobs are in the fields your model must reason about, from oncology and credit risk to cybersecurity and industrial operations. That expertise is what turns raw human feedback into a reliable, decision-grade signal.

AI labs, foundation model providers, and AI-native scaleups plug NewtonX in wherever expert judgment is the bottleneck, in the domains where generic raters can’t tell a right answer from a plausible one. Sometimes that means quality evals: expert panels that score, compare, and red-team model outputs. Other times it means end-to-end post-training data delivery. We scope the project, recruit experts, manage throughput, and return datasets ready for SFT, RLHF, DPO, RLVR, and multi-step agent training. When you already have data, our QaaS (Quality-as-a-Service) framework provides a rigorous, independent audit to validate its integrity. In every engagement, you gain a single partner fully accountable for feasibility, quality, and scale, from a first pilot to a frontier-scale program.

Enterprise AI teams building and deploying agents plug NewtonX in wherever real-world performance is the open question. Most often that means agent testing: we stand up domain-realistic RL environments where your agent runs against expert-designed tasks, multi-step workflows, tool calls, and the edge cases that break in production, and domain specialists score every trajectory against rubrics that reflect how the work is actually done. Other times it means expert red-teaming, where specialists probe agents for domain-specific failure modes before they reach customers, or evaluation-set creation, where we author benchmarks and task suites tied to your vertical. In every engagement, you gain a partner accountable for whether your agent performs when the domain gets hard.