Trusted by AI teams — from labs to enterprise

Your edge at the frontier of AI.
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Built by humans. Trusted by labs.

What we build for AI teams

Proprietary data, custom benchmarks, and human-expert evaluations for any company building serious AI — from frontier labs to enterprise ML teams.

Evaluation

LLM Evals

Rubric-graded evals for code, math, reasoning, instruction following, and safety — authored by domain experts.

SWE

SWE Benchmarks

SWE-bench-style agentic coding evals across real repos in Python, TypeScript, Go, Rust, Java, and C++.

Security

Cybersecurity

Red-team prompts, exploit traces, CTF transcripts, and SOC triage data authored by practicing security experts.

Code

Code Generation

Multi-language code with executable verification, difficulty banding, and senior-engineer reference solutions.

Math

Math & Reasoning

Olympiad-grade problems and Lean / Coq formal proofs with step-level annotation and verification.

Reasoning

Reasoning Chains

Step-level correctness labels and first-error localization — ready-made training data for process reward models.

RLHF

Preference Data

RLHF / DPO / KTO preference pairs with explicit rubrics and inter-rater reliability reporting.

Agents

Agentic Traces

Browser, terminal, IDE, and tool-use traces from experts completing real multi-step work — every step labeled.

Safety

Red-Team & Safety

Adversarial prompts and jailbreak taxonomies refreshed continuously as new attack classes emerge.

Benchmarks

Custom Benchmarks

Bespoke leaderboards co-designed with your research team — spec, items, grader, and hosting end to end.

Annotation

Expert Review

PhD-grade and senior-engineer review at scale through Hatch — our vetted contractor network.

Pipelines

Continuous Evals

Recurring drops aligned to your release cadence with delta reports across checkpoints.

Backed by 5,000+ vetted experts across multiple domains. Every engagement scoped under an MSA + SOW with named experts, explicit rubrics, and per-item provenance.

From the teams we work with

How research teams describe working with us

Customer identities are abstracted at their request. Named references available under NDA.

“NobleStark stood up a 12k-item SWE-agentic eval for us in five weeks. Their senior engineers wrote every reference solution, and the rubric documentation made our internal calibration trivial.”

Sarah K.

Head of Evaluations, Frontier AI Lab

“We needed Lean 4 proof annotations from people who actually do formal mathematics. Hatch's expert pool delivered — IMO and Putnam medalists who could write and verify the chains, not just label them.”

Marcus J.

Research Scientist, Foundation Model Team

“The red-team coverage NobleStark builds gets refreshed faster than our model release cadence. New jailbreak classes show up in the eval drop before we see them in the wild.”

Emily R.

Safety Eval Lead, Top-5 AI Lab

“NobleStark stood up a 12k-item SWE-agentic eval for us in five weeks. Their senior engineers wrote every reference solution, and the rubric documentation made our internal calibration trivial.”

Sarah K.

Head of Evaluations, Frontier AI Lab

Marcus J.

Research Scientist, Foundation Model Team

“The red-team coverage NobleStark builds gets refreshed faster than our model release cadence. New jailbreak classes show up in the eval drop before we see them in the wild.”

Emily R.

Safety Eval Lead, Top-5 AI Lab

“Their cybersecurity data is the rare kind that's both deep and broad — CTF transcripts from people who actually solve them, vuln writeups with executable PoCs, and policy-graded refusals tied to a real threat taxonomy.”

David K.

Director of Applied Research, Hyperscaler

“An embedded pod from NobleStark has been part of our weekly model dev cycle for three quarters. The continuity matters — they learn our rubric evolution, not just the current spec.”

Lisa T.

Principal Eng. Lead, Agent Platform Team

“Every item ships with author identity, rubric version, and review trail. Procurement signed it off in days because the provenance story was already airtight.”

Alex M.

VP, Data Operations, Foundation Lab

David K.

Director of Applied Research, Hyperscaler

“An embedded pod from NobleStark has been part of our weekly model dev cycle for three quarters. The continuity matters — they learn our rubric evolution, not just the current spec.”

Lisa T.

Principal Eng. Lead, Agent Platform Team

“Every item ships with author identity, rubric version, and review trail. Procurement signed it off in days because the provenance story was already airtight.”

Alex M.

VP, Data Operations, Foundation Lab

“We compared NobleStark's preference data to two other vendors on a held-out reward model task. NobleStark's data trained the highest-agreement model with the lowest variance across seeds.”

Rachel G.

RLHF Lead, Alignment Team

“Their agentic trajectory captures aren't synthetic. Real engineers debugging real failures across real codebases, all replayable. That's the part we couldn't get from anyone else.”

James W.

Research Engineering Manager, AI Lab

“They built our custom code-review benchmark end to end: spec, item authoring, grader, leaderboard. The held-out integrity has held up across four months of public submissions.”

Sophia D.

Eval Infrastructure Lead, Foundation Model Team

“We compared NobleStark's preference data to two other vendors on a held-out reward model task. NobleStark's data trained the highest-agreement model with the lowest variance across seeds.”

Rachel G.

RLHF Lead, Alignment Team

“Their agentic trajectory captures aren't synthetic. Real engineers debugging real failures across real codebases, all replayable. That's the part we couldn't get from anyone else.”

James W.

Research Engineering Manager, AI Lab

“They built our custom code-review benchmark end to end: spec, item authoring, grader, leaderboard. The held-out integrity has held up across four months of public submissions.”

Sophia D.

Eval Infrastructure Lead, Foundation Model Team

Engagement

Two ways to work with us

Every engagement runs under a signed MSA and a scoped Statement of Work with named experts, explicit rubrics, per-item provenance, and an acceptance protocol your research team controls. Pricing is engagement-specific — talk to us for a quote.

Standard SOW

Most teams start here

Project-scoped engagement. You define the rubric and acceptance criteria — we deliver the dataset, eval, or benchmark.

Typical engagement4–10 weeks · fixed fee

Fixed-scope deliverable: dataset, eval suite, or benchmark

Domain-expert authors matched to your vertical

Pilot batch (typically 5%) before full production

Per-item provenance: author, rubric version, review trail

Inter-rater reliability metrics included in delivery

Acceptance protocol controlled by your research team

Typical turnaround: 4–10 weeks depending on scope

Embedded Expert Pod

Long-running

A dedicated team of vetted experts on retainer, integrated with your research org over a multi-quarter horizon.

Typical engagementMulti-quarter retainer

Dedicated 5–25 expert pod with a named program lead

Embeds into your Slack / Linear / Phabricator workflow

Continuous eval refresh aligned to your release cycle

Calibration tasks, qualifier rounds, ongoing QA

Quarterly capability reviews and rubric evolution

Single contracting entity for compliance, payroll, IP

Priority access to scarce verticals: cybersecurity, formal math, low-resource languages

MSA · DPA · Subprocessors · NDA per project

Tell us about your data needs

Tell us what you're scoping. A solutions engineer replies within one business day.

Your edge at the frontier of AI.code

What we build for AI teams

LLM Evals

SWE Benchmarks

Cybersecurity

Code Generation

Math & Reasoning

Reasoning Chains

Preference Data

Agentic Traces

Red-Team & Safety

Custom Benchmarks

Expert Review

Continuous Evals

How research teams describe working with us

Two ways to work with us

Standard SOW

Embedded Expert Pod

Tell us about your data needs

Your edge at the frontier of AI.code

Your edge at the frontier of AI.
code

Your edge at the frontier of AI.
code