Expert-corrected data for the tasks your models still fail.

Pilot training data from paid business workflows: model failures, expert fixes, and tests that show why the fixes work.

Discuss a data pilot See a data example

Expert network live. Data capture and packaging planned.

Inside a data package

One task. The full correction.

From model failure to a testable expert fix.

Illustrative booking-app exampleProposed deliverable

01 Task + context

Prevent double bookings.

Two customers request the same provider’s fixed, non-overlapping 30-minute slot.

02 Model failure

Both requests get through.

Both pass the availability check before either booking is saved.

03 Expert fix

One booking per slot.

A unique database rule rejects the second booking.

UNIQUE (provider_id, slot_start)

04 Expected verification

One booking. One conflict.

Reset the slot, then send both requests together.

201 Booked409 Conflict

Different slots must still book successfully.

Includes Brief, reset steps, attempt, fix, tests, provenance, and permissions.

How tasks are scoped

Why it matters

The details that change the result.

Capture the constraints and expert decisions that a successful outcome depends on.

Real context

Slot rules, database behavior, and API requirements define success.

Critical exceptions

A single request works. Two at once reveal the failure.

Expert judgment

The expert explains the fix and ties it to a test.

Task decomposition

From project to testable task.

Each task needs a clear boundary, repeatable starting state, and acceptance test.

Separate reservations, cancellations, and notifications. Choose one outcome and state what is out of scope.

Proposed workflow. Only reproducible model failures qualify; context and tool errors are excluded.

Discuss a data pilot
Proposed capture workflowBooking-app example
01 / Task boundary
Booking application
AvailabilityFind open slots
Reservation conflictSelected: prevent double bookings
CancellationSeparate task
NotificationsSeparate task

One outcome at a time.

Select the reservation conflict.

Our sourcing advantage

Real experts. Paid work.

Our operating network connects expert businesses with paid customer work, giving us access to valuable tasks and specialist judgment.

The expert network and paid customer engagements operate today. Capture and data packaging are the next build.

  1. 01Operating today

    Expert businesses

    Experts are onboarded through an established playbook, with specialists accountable for delivery.

  2. 02Operating today

    Paid engagements

    Customer work has generated revenue and ongoing access to real requirements and decisions.

  3. 03Next build

    Capture + packaging

    Planned: permissioned capture, verified failures, and expert corrections packaged for labs.

The network operates today. The data pipeline is the next build.

Bring your expertise to real work.

Expert network

Join Our Expert Network

Interested in future projects and opportunities? Submit your information below to join our talent network. Our team may contact you about opportunities that match your background and experience.

Required fields

01Contact Information
02Professional Information
Area(s) of Expertise

Select all that apply.

Choose a file

PDF, DOC or DOCX · Up to 5 MB

03Communication Preferences

Please select how you would like us to contact you. These options are voluntary and are not a condition of applying or participating in opportunities.

By submitting this form, I acknowledge that I have read and understand the Privacy Policy (opens in a new tab) and Terms and Conditions (opens in a new tab) and consent to the collection and processing of my information as described in the Privacy Policy.

Rights & reproducibility

Clear rights. Replayable evidence.

Proposed pilot requirements, agreed with your lab before capture and delivery.

Provenance

Trace the source

Document the workflow, contributors, model setup, changes, and reviews within agreed disclosure limits.

Permitted uses

Confirm permission

Secure customer and expert consent. Agree training, evaluation, redistribution, retention, and exclusions.

Privacy

Protect sensitive data

Redact, transform, or exclude sensitive data. Recheck that the task still works.

Reproducibility

Replay the task

Include the brief, versioned code, dependencies, fixtures, tool access, and reset instructions.

Verification

Test the fix

Replay failure and correction against the same criteria. Include execution evidence and expert review.

Held-out evaluation

Prevent overlap

Split by project or task family. Keep evaluation tasks and reference answers out of training.

A paid pilot

Start with one capability gap.

Agree a focused dataset and a clear way to judge its value.

Pricing and dates follow source, rights, and engineering review. Training environments are scoped separately.

Your lab sets the target

Define tasks, model and tools, failure types, format, uses, volume, and budget.

We scope the package

Agree failures, expert fixes, tests, and provenance. Confirm source access and rights before committing.

Accept against shared criteria

Review relevance, completeness, rights, replayability, and correctness. Rework or reject examples that fall short.

Measure usefulness

Where feasible, compare baseline and updated models on separate held-out tasks under matched conditions.

For AI labs

Bring us a task your model misses.

Let’s scope a pilot around your research priorities.

Discuss a data pilot