Expert AI Training Data
for Frontier Model Development
Your AI model is only as good as the data it learns from... delivered by senior engineers who understand the code they're evaluating.
The Expert Bottleneck
High-quality AI development is hitting a wall: the quality of human feedback. Standard crowd-working platforms fail when tasked with evaluating complex system design, deep reasoning, or multi-repo architecture.
Nomos Insights bridge the gap. We replace generalist labelers with Domain Expert Engineers who don't just follow a rubric they understand the underlying engineering complexity.
Four Ways We Help
AI Labs Build Better Models
From evaluation dataset creation to reasoning annotation and agentic benchmarks, delivered by engineers who genuinely understand the code they're reviewing.
Coding Evaluation Dataset Creation
We create complete evaluation task sets (prompt, golden answer, and rubric) designed to test model reasoning on non-verifiable coding challenges involving multi-file contexts, architectural decisions, and real-world complexity.
Reasoning Trace Annotation
Closed-source models don't expose their internal reasoning. We reconstruct and annotate missing thought processes by reviewing agent trajectories on SWE-bench-style tasks derived from real open-source repositories.
Agentic Benchmark Design
We create benchmarks that evaluate the entire problem-solving process. Not just whether a model produces correct output, but whether it understands requirements, asks the right questions, and follows sound engineering practices.
RLHF Data & Model Evaluation Support
Beyond task creation, we support the broader training data pipeline with expert feedback workflows: preference data collection, adversarial testing, and quality assurance on existing datasets.
Structured Delivery,
Built for Fast-Moving Projects
Four clear stages, each with defined outputs: from initial scoping to scaled delivery.
Scope & Align
We start by understanding your evaluation framework, target model capabilities, language and category requirements, and quality standards. We integrate seamlessly into existing platform workflows.
Execute with Rigor
Our engineers produce tasks through structured creation workflows with built-in review cycles. Every prompt, golden answer, and rubric is reviewed against our quality checklist before submission.
Iterate on Feedback
We incorporate reviewer feedback rapidly, whether from your internal team, external reviewers, or automated quality checks. Our iterative refinement process is designed for the fast-paced cadence of AI training projects.
Deliver at Scale
With a dedicated team of 9+ engineers, we maintain consistent throughput across concurrent projects without sacrificing quality. We operate across overlapping time zones with most US and European clients.
Engineers Evaluating Code.
Not Annotators Labeling It.
We write code. We don't just label it.
Most AI training data providers recruit annotators and train them on coding guidelines. We start with competitive programmers and senior engineers, then apply that expertise to evaluation tasks. The difference shows in rubric quality and reasoning depth.
We've done the hard quality work already.
Through 1,000+ tasks, we've built internal standards that catch subtle issues most teams miss: non-atomic criteria, rubrics that create unfair cascading failures, golden answers that penalize valid alternative approaches.
An engineering team, not a staffing agency.
We're a cohesive team with shared context, established review processes, and institutional knowledge from hundreds of completed projects. Not individual freelancers matched to tasks.
A Market Shifting Toward Expert-Level Data
The AI training dataset market is projected to grow from approximately $3.5 billion in 2025 to over $16 billion by 2033. As frontier models shift from simple data scaling to post-training techniques where human feedback quality matters more than volume, the demand for expert-level training data is accelerating faster than the supply of qualified providers.
Whether You're an AI Lab or
a Platform Partner
Whether you're an AI lab looking for a direct engineering partner, or a platform like Mercor, Turing, or Alignerr looking for a high-quality execution team, we'd like to hear about your project.