Stratum

How Expert AI Workforce Delivery Works

The commercial object is a managed expert cohort. The client keeps methodology, data, and acceptance criteria. We run the people system around that methodology.

We are not a freelancer marketplace or traditional staffing company.

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How does expert workforce delivery work

The client specifies the domain, credentials, geography, volume, and quality bar. Stratum sources, verifies, qualifies, onboards, and manages the cohort. The client keeps data, rubrics, methodology, and final acceptance. We are not a freelancer marketplace or traditional staffing company.

  1. Step 1

    Define the expert profile

    Turn the buyer’s need into a specification: domain, credentials, experience, geography, task, volume, availability, and quality threshold.

  2. Step 2

    Identify suitable experts

    Source from a professional network against that specification. This is matching to a brief, not posting a public gig.

  3. Step 3

    Verify credentials and experience

    Check the claims the project depends on—license, degree, role history—before anyone touches production data.

  4. Step 4

    Run qualification tasks

    Use items that resemble production. A person can hold the right credential and still fail a rubric they have never practiced.

  5. Step 5

    Onboard against client standards

    Place qualified experts into the client’s tools, confidentiality terms, style constraints, and escalation rules.

  6. Step 6

    Manage production capacity

    Coordinate hours, coverage, and task routing so the research team is not running an informal staffing desk.

  7. Step 7

    Monitor quality and replace or scale

    Watch agreement, gold-set performance, and throughput. Replace people who drift. Add capacity when the queue grows.

Managed expert delivery
  1. 1Profile
  2. 2Identify
  3. 3Verify
  4. 4Qualify
  5. 5Onboard
  6. 6Produce
  7. 7Monitor

What we need from the client

Incomplete briefs are normal. Empty briefs are not. The items below are the minimum that lets sourcing start without guessing.

  • Domain
  • Credential requirements
  • Experience
  • Geography
  • Headcount
  • Weekly hours
  • Project duration
  • Task type
  • Qualification process
  • Data access needs
  • Start date
The Expert Capacity Specification

Before sourcing experts, define the specification the project will be staffed and measured against.

  • Domain
  • Credential
  • Experience
  • Geography
  • Task
  • Volume
  • Availability
  • Quality threshold

Procurement templates

These are working HTML checklists for buyers. They are suggested frameworks, not industry standards and not a substitute for counsel.

Expert Workforce Specification Template

Write these fields before sourcing. Incomplete briefs are normal; empty briefs force the vendor to guess the professional standard.

  1. Domain and sub-specialty
  2. Credentials or licensure that are required versus preferred
  3. Years of experience and the setting that counts
  4. Geography, jurisdiction, or time-zone coverage
  5. Task type: annotation, evaluation, benchmark writing, preference, adjudication
  6. Item format and an example artifact
  7. Weekly hours and project duration
  8. Headcount and whether a backup bench is required
  9. Quality threshold: single review, dual review, gold-set rate, adjudication path
  10. Data class and required environment
  11. Start window and who accepts the first production batch

AI Evaluation Pilot Checklist

A pilot should prove that the rubric, the cohort, and the access model work on real items. It is not a discounted version of a vague program.

  1. Named task and success definition for the pilot, not for the company
  2. 10–50 representative items, including known hard cases
  3. Written rubric with at least one worked accept and one reject example
  4. Qualification task that resembles production
  5. Independent dual review on a defined subset
  6. Adjudication owner for remaining disagreements
  7. Access model tested with a non-sensitive stand-in if needed
  8. Replacement rule if a reviewer fails calibration
  9. Written decision: continue, revise rubric, or stop

Expert Adjudication Workflow

Adjudication is senior review of disagreement, not a third vote from the same pool. Record a reason that can change the guide.

  1. Two qualified reviewers label independently
  2. Agreement is measured; disagreement is queued, not averaged
  3. A more senior or more specialized reviewer sees both labels and the artifact
  4. The accepted label and a short reason are written down
  5. If the dispute was about the standard, the rubric is versioned
  6. If the dispute was about facts, the item may be retired or rewritten
  7. Gold items created this way are held out from training when used for measurement

AI Evaluation SLA Checklist

An SLA is useful when it names the work, not a slogan. Do not copy a 99 percent accuracy clause unless the task, rubric, and sampling method are defined.

  1. Scope: item types, domains, and what is out of scope
  2. Capacity: headcount, weekly hours, coverage windows
  3. Turnaround: definition of a completed item and the clock that applies
  4. Quality method: sampling rate, dual-review rate, gold-set use
  5. Disagreement and adjudication path
  6. Replacement and re-qualification after drift
  7. Reporting: what is sent, how often, and who can inspect labels
  8. Security: environment, data class, access revocation
  9. Change control: how rubric edits are versioned
  10. Acceptance: who signs the first production batch

What kind of experts do you need?

Share the profession, specialty, experience, location, headcount, hours, duration, and project description. We assemble the expert capacity.

Request Expert Capacity
  • Need 25 licensed nurses for a clinical AI evaluation
  • Need 15 attorneys in a specific practice area
  • Need 20 PhD scientists for benchmark creation
  • Need 30 senior software engineers for code evaluation