Nomvera AI gives frontier model companies access to a pre-vetted, academically credentialed workforce - sourced exclusively from U.S. graduate programs - for training and evaluation, faster and with greater domain fidelity than any crowdsourced platform.
Calibration to your rubric before we ship a single file.
Platform-sourced contributors complete lightweight assessments and enter complex RLHF workflows that require genuine domain expertise. The cognitive ceiling of a general crowd directly limits the ceiling of your model.
Research leads at frontier labs are re-running annotation tasks, running secondary QA layers, and absorbing rework costs that compound at scale. The problem isn't tooling - it's the caliber of the human in the loop.
Scale AI's acquisition by Meta triggered immediate customer flight from OpenAI and Google. Organizations that routed their most sensitive training data through a single platform are now urgently reassessing who holds access to their model development decisions.
Tens of thousands of U.S. master's graduates in STEM, linguistics, and domain sciences complete their programs each year. The infrastructure to deploy them into structured AI training workflows hasn't existed - until now.
We don't staff a platform. We staff a project team - vetted, briefed, calibrated to your rubric, and accountable for delivery. Every engagement is managed by Nomvera from brief to batch. NDA-ready, isolated project teams.
Built for frontier labs and enterprises - not for commodity, high-volume image annotation.
Comparative ranking, preference labeling, and response evaluation for language model fine-tuning. Our annotators engage with multi-step reasoning chains, code sequences, and domain-technical outputs that require graduate-level comprehension to evaluate correctly.
Structured adversarial testing, capability benchmarking, and safety evaluation across reasoning, instruction-following, and factual accuracy. Red-teaming projects are staffed with contributors whose domain background matches your model's intended deployment context.
STEM, legal, medical, financial, and multilingual labeling by contributors with verified academic backgrounds in the relevant field. Domain projects get domain contributors - not generalists reassigned from unrelated tasks.
Structured prompt engineering, seed dataset construction, and synthetic dialogue generation for organizations building specialized fine-tuning corpora from the ground up. Every piece of generated data carries a credential chain - you know who built it and why it's qualified.
Inbound inquiry leads to a discovery call with your Nomvera client lead. You submit a project brief (domain, task type, volume, timeline). Nomvera performs a feasibility assessment against our standing contributor pool and returns a proposal within 48 hours - team composition, credential overview, quality thresholds, and delivery timeline. NDA execution and project kickoff follow.
Selected contributors complete a calibration exercise against gold-standard outputs in your task category. Inter-annotator agreement is calculated before a single production task begins. Contributors below threshold are rotated out before they cost you quality.
Weekly batch delivery with internal quality control review before every handoff. Outlier flagging, agreement score tracking, and structured feedback loops happen on our side - you review output quality, not individual contributor management.
High-performing first engagements convert to standing retainer relationships with reserved contributor capacity, priority domain sourcing, and monthly business reviews. The team that knows your rubric stays on your account.
| Crowdsourced Platforms | Nomvera AI | |
|---|---|---|
| Workforce Credentials | ✕ General population, self-reported expertise | ✓ Verified U.S. graduate-degree holders, transcript-reviewed |
| Onboarding Timeline | ✕ Days to weeks of platform setup and training | ✓ Pre-vetted pool ready to deploy - 10 business days to first delivery |
| Data Governance | ✕ Shared annotation pools, platform-level access | ✓ Isolated project teams, no cross-client data exposure |
| Vendor Independence | ✕ Scale AI now Meta-affiliated; conflict of interest documented | ✓ Independent, no strategic investment from model developers |
| Account Accountability | ✕ Client manages individual contributor quality themselves | ✓ Nomvera owns quality - clients manage the relationship, not the crowd |
| Specialized Domain Depth | ✕ Expert matching by self-assessment | ✓ Contributors calibrated on benchmark tasks before production begins |
"We re-ran the same RLHF tasks three times with another vendor before we got acceptable inter-annotator agreement. With Nomvera, calibration happened before we saw a single production file."
"Knowing that every annotator on our legal reasoning project held a relevant graduate degree wasn't a nice-to-have. It was non-negotiable. Nomvera was the only vendor that could verify it."
"After the Scale-Meta situation, we needed a vendor that wasn't structurally conflicted. The data governance guarantees Nomvera offers are the most concrete we've seen in the market."
The AI training dataset market is on track from $2.82 billion in 2024 to $9.58 billion by 2029. The category expanding fastest is RLHF and human evaluation for large language models - precisely the work that requires the kind of domain fluency a crowd platform cannot reliably deliver.
The vendor reshuffling triggered by the Scale AI acquisition has left billions in annual spend without a committed provider. The organizations losing their incumbent have one criterion above all others: a partner they can trust with their data and their model.
Tell us what you're building. We'll tell you what your evaluation data should look like - and who should be creating it. Get a proposal within 48 hours. Pricing you can defend to your VP.