Nextmark

Enterprise

Build better AI models
with the right human expertise.

Nextmark runs expert hiring, structured assessment, and end-to-end quality control for enterprise AI training programmes. We understand what your model needs — and we build the contributor programme to get you there.

Talk to our team

The bottleneck in most AI training programmes is not compute. It is finding people who genuinely know the domain and can produce data that is precise enough to actually improve the model.

12+

Domains covered

3-layer

QC framework

48h

Calibration sprint

100%

Confidential

What we do

End-to-end support for your training programme.

01

Expert hiring, built for AI.

Finding people who can train AI models is not the same as recruiting for a standard role. Domain expertise is table stakes — you also need contributors who can articulate their reasoning precisely, work within structured task formats, and maintain quality under volume. We run the full recruitment pipeline: sourcing, screening, credentialing, and onboarding. You receive a pool of contributors who are ready to work on day one.

How it works in practiceWe source from practitioner networks across medicine, law, engineering, finance, and the sciences. Every contributor passes a multi-stage screening process before they ever see a task.
02

Structured assessment at scale.

We design and administer the assessment layer for your training programme. This means building task specifications, calibration sets, and inter-annotator agreement frameworks suited to your domain. We have run assessments across clinical reasoning, legal analysis, technical code review, financial modelling, and multilingual tasks. Each engagement starts with a calibration sprint — a small batch of tasks run with close supervision — so quality targets are validated before you scale.

How it works in practiceOur assessment infrastructure handles thousands of contributors across multiple concurrent projects. Throughput, accuracy, and consistency metrics are tracked per contributor, per task type, and per project phase.
03

Quality control across every layer.

Raw annotations degrade model performance if quality is inconsistent. Our QC layer operates at three levels: per-task review by senior domain experts, statistical quality monitoring across contributor cohorts, and structured feedback loops that improve contributor performance over time. We hold every contributor to documented standards and remove underperformers before they affect your dataset.

How it works in practiceIndependent QC reviewers are matched by domain — a physician reviews physician-produced annotations, not a generalist. We maintain strict separation between task completion and quality review.
04

Full project understanding.

We work with your ML team from the beginning. That means reading your model card, reviewing your RLHF or RLAIF methodology, understanding the specific failure modes you are trying to address, and designing the contributor workflow to produce data that actually moves the needle. We do not drop a generic annotation pipeline on your problem. We build one around your model architecture, your domain, and your quality bar.

How it works in practiceOur project leads have worked across pretraining, instruction fine-tuning, preference data collection, and safety evaluation. If your team has a specific benchmark in mind, we calibrate to it.

Why Nextmark

We work the problem, not a playbook.

We read the model card.

Before scoping any engagement, we read your training documentation. We ask about your eval suite, your known failure modes, and your data strategy. We build programmes around your actual problem — not a template.

Domain-matched everything.

A physician reviews physician work. A securities lawyer reviews legal tasks. Domain matching applies to sourcing, screening, task assignment, and QC. Generalist reviewers create generalist data.

Quality over throughput.

We do not optimise for annotation speed. We optimise for data that improves your model. That means slower, more deliberate work with higher inter-annotator agreement and a QC process that removes bad data before it ships.

Common questions

How quickly can you stand up a project?
For most projects, we can have a calibrated contributor cohort ready within two to three weeks of kickoff. The calibration sprint itself takes five to seven business days, after which we run the main programme at whatever throughput your timeline requires. Rush timelines are possible for some domain types — reach out to discuss.
What domains can you cover?
Our current network covers medicine (all major specialties), law (US, UK, EU, and several emerging markets), software engineering and security, financial analysis, academic research across STEM and humanities, and language/translation work in over 40 languages. If your domain is highly specialised, we can discuss whether we can source appropriately credentialed contributors for you.
How is contributor confidentiality handled?
All contributors sign a multi-layer NDA before any project access is granted. The NDA covers the identity of the end client, the nature of the task, and all data produced. Contributors do not know which model their work feeds or which company commissioned it. We maintain full separation between contributor identity and client data.
Can you work with our internal annotation tooling?
Yes. We have integrated with Labelbox, Scale, Surge AI, custom internal platforms, and direct API workflows. Our contributors are tool-agnostic and can be onboarded to your interface. Where you do not have existing tooling, we can stand up a lightweight task delivery system designed for your specific task type.
How do you price?
Pricing is structured per project rather than per annotation. We quote based on the domain, complexity of the task type, required throughput, and QC intensity. We do not do per-annotation commodity pricing because it creates the wrong incentive structure for quality. Reach out and we will scope your project and provide a fixed-fee or milestone-based proposal.
Do you work with early-stage AI companies?
Yes. Some of our best long-term relationships are with teams that engaged us at the pre-product or seed stage. Early engagement means we can help you design your data strategy, not just execute it. We are not gated on company size, funding stage, or the maturity of your model.

Ready to build with better data?

Email us at enterprise@nextmark.ai or use the link below.

Get in touch →