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3,000+ vetted experts, 10,000+ more on call

Elite experts for training frontier models

Doctors, lawyers, engineers and analysts who still practise, producing the demonstrations, rubrics, environments and evals that frontier models learn from.

  • SFT
  • RL rubrics
  • Agent environments
  • Computer use
  • Evals

The record, since May 2026

Tasks delivered
5,000+
Accuracy on delivered work
99.52%
Experts on the bench
3,000+
More on call
10,000+

The problem

Today’s models are good at producing something that looks like an answer. Real work is mostly judgement: what to check first, which number looks wrong, when a rule does not apply. That judgement lives inside practitioners, not on the internet. We get it out of them and into your data.

Why it is hard

An operations problem disguised as a research problem.

Finding two hundred practising radiologists is not the hard part. Keeping quality flat while the bench turns over, for nine months, against a rubric that keeps moving, is the hard part, and it is not the thing a data lab raised money to do.

That is the part we run.

The catalogue

Five things, and nothing else.

If a request does not land in one of these we say so, rather than learning on your budget.

01 / 05Swipe
01SFT

Supervised demonstrations

Prompt and response pairs, and the reasoning that got there, written by people who do the work for a living.

  • Prompt and response pairs
  • Chain-of-thought reasoning traces
  • Multi-turn dialogues
  • Per-item author provenance
02RL

Reinforcement learning and rubrics

Expert-written prompts with the grading framework to score them, so a reward model has something defensible to learn from.

  • Prompt sets by difficulty band
  • Rubrics with anchored levels
  • Graded completions
  • Pairwise preference data
  • Inter-rater agreement per batch
03Envs

Agent environments

API and MCP environments that behave like the real tool, with task sets and the checks that say whether an agent actually finished.

  • Containerised environments
  • MCP and REST surfaces
  • Seeded fixtures and reset scripts
  • Task suites with difficulty bands
  • Programmatic verifiers
04Trajectories

Computer-use trajectories

Recorded browser and desktop work, step by step, with the intent behind each step written down.

  • Screen recordings with event streams
  • Step-level intent annotations
  • Recovery and dead-end branches
  • Desktop and browser, Windows, macOS and Linux
05Evals

Evaluation and red-teaming

Blind expert scoring, and adversarial sets written by people who know where the real edge cases hide.

  • Held-out expert evals
  • Blind double-scoring
  • Adversarial and edge-case sets
  • Failure reports with expert commentary

The bench

10 domains, staffed by people who still practise.

A tax expert writing transfer-pricing traces files returns for a living. The moment someone stops practising, they stop being the thing the model needs.

  1. 01

    Medicine and clinical

    • Physicians
    • Nurses
    • Radiologists
    • Clinical coders
  2. 02

    Law

    • Litigation
    • Corporate and M&A
    • Intellectual property
    • Compliance
  3. 03

    Accounting and tax

    • Statutory audit
    • Direct and indirect tax
    • Transfer pricing
    • Controllership
  4. 04

    Finance

    • Equity research
    • Credit analysis
    • Trading operations
    • FP&A
  5. 05

    Software and data

    • Backend
    • Infrastructure and SRE
    • Analytics engineering
    • QA automation
  6. 06

    Engineering

    • Civil and structural
    • Mechanical
    • Electrical
    • Process and quality
  7. 07

    Supply chain and trade

    • Freight and logistics
    • Customs brokerage
    • Import and export documentation
    • Procurement
  8. 08

    Insurance

    • Underwriting
    • Claims adjudication
    • Actuarial support
  9. 09

    Pharma and regulatory

    • Clinical trial operations
    • Regulatory submissions
    • Pharmacovigilance
  10. 10

    Business operations

    • Recruiting
    • Customer operations
    • Sales operations
    • Bookkeeping

Process

From first call to a batch shipping weekly.

The pilot is paid, priced at the volume rate, and the output is yours whether or not it continues.

  1. 01

    Scope

    One call with whoever owns the data. We read your spec, your rubric and a sample batch, then tell you which parts we can staff this month and which we cannot. The second answer is the useful one.

  2. 02

    Pilot

    A small paid batch at the full quality bar, in whichever system you chose, through your QA gate. You keep the output whether or not it continues, and the price is the price it would be at volume.

  3. 03

    Staff

    Named experts, not a queue. You see the bench before it starts: who they are, what they have practised, how long, and who leads the squad. You can reject anyone without giving a reason.

  4. 04

    Run

    Fixed weekly throughput, one point of contact, and daily visibility into what shipped. Sampling and double-scoring run inside every batch rather than after it, so a drift shows up in days rather than at the end of the month.

  5. 05

    Audit

    Every batch ships with its agreement statistics, its reject rate, and the items we threw away and why. If a number moves in the wrong direction, you hear it from us before you find it.

Tell us the domain you are stuck staffing.

One call, and you get a straight answer about whether we can take it. If we cannot, we will say so on the call rather than three batches in.

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