Healthcare
Example professionals
- Physicians
- Registered nurses
- Pharmacists
- Clinical specialists
Example work
- Clinical model evaluation
- Medical annotation
- Safety review
- Benchmark creation
- Expert adjudication
For AI companies and research teams
Expert Data and Evaluation for AI
Stratum provides managed expert workforces for AI training, evaluation, data annotation, benchmark creation, and post training across medicine, law, finance, science, engineering, and software.
We are not a freelancer marketplace or traditional staffing company.
You define the expertise, qualifications, and capacity you need. We provide and manage the expert workforce.
You specify
- Domain
- Credentials
- Years of experience
- Geography
- Number of experts
- Hours required
- Project duration
- Task type
- Qualification criteria
We deliver
A qualified, managed expert cohort aligned to the specification, with replacement and quality monitoring included in the operating model.
When generic annotators are not enough
Many frontier and vertical AI systems fail in ways that look fluent. The error is not a missing label. It is a missed contraindication, an invalid legal inference, a spreadsheet that does not reconcile, or code that compiles and still solves the wrong problem.
General annotation is appropriate when the task is low-ambiguity and an incorrect answer is obvious. That is Level 1 work: classification, transcription, and simple preference tasks.
Vertical models and high-stakes evaluation usually sit at Level 3. A licensed clinician, attorney, finance professional, research scientist, or senior engineer is not being asked to decorate data. They are being asked to apply a professional standard to an output that can deceive a non-specialist.
Typical work that requires that standard includes:
- Identifying subtle domain errors
- Evaluating model reasoning
- Creating difficult prompts and tasks
- Writing authoritative reference answers
- Building evaluation rubrics
- Comparing model outputs
- Reviewing safety issues
- Validating professional work products
- Adjudicating reviewer disagreement
Generic annotation
Basic labeling
- Basic classification
- Simple labeling
- Low domain complexity
- Errors are usually obvious to a general reviewer
Expert evaluation
Professional judgment
- Domain-specific reasoning
- Rubric development
- Gold answer creation
- Adjudication when qualified reviewers disagree
Level 1
General annotation
Use when errors are obvious and domain knowledge is not required.
- Classification
- Transcription
- Simple preference tasks
Level 2
Skilled evaluation
Use when the work needs technical literacy or structured reasoning, but not a licensed professional.
- Technical review
- Structured reasoning
- Specialized content
Level 3
Professional expert evaluation
Use when an incorrect answer can look plausible to a generalist and the consequence is material.
- Clinical
- Legal
- Financial
- Scientific
- Advanced engineering
Expertise
Professional cohorts, specified by you
These are examples of professions and work types that can be sourced against a project brief. Inventory is assembled to the specification rather than sold from a public talent catalog.
Legal
Example professionals
- Attorneys
- Legal researchers
- Practice area specialists
Example work
- Legal reasoning evaluation
- Document analysis
- Benchmark development
- Jurisdiction-specific review
- Rubric creation
Finance
Example professionals
- Investment bankers
- Financial analysts
- CPAs
- Accounting professionals
- Private equity professionals
Example work
- Financial reasoning
- Spreadsheet evaluation
- Investment research
- Accounting analysis
- Model validation
Science
Example professionals
- PhDs
- Researchers
- Bioinformaticians
- Chemists
- Biologists
Example work
- Scientific reasoning
- Benchmark creation
- Research agent evaluation
- Gold answer creation
- Expert review
Engineering
Example professionals
- Mechanical engineers
- Electrical engineers
- Technical specialists
Example work
- Technical reasoning
- Engineering AI evaluation
- Domain-specific annotation
- Agent testing
Software
Example professionals
- Senior developers
- Software engineers
- Technical reviewers
Example work
- Code evaluation
- Debugging
- Agent evaluation
- Repository tasks
- Technical benchmark creation
Tell us what expertise you need
The buyer keeps control of methodology, tasks, rubrics, evaluation criteria, data, and quality standards. Stratum supplies and manages the people who can execute that methodology.
Client inputs
- Domain
- Credentials
- Years of experience
- Geography
- Number of experts
- Hours required
- Project duration
- Task type
- Qualification criteria
Our responsibilities
- Expert identification
- Credential verification
- Qualification assessment
- Onboarding
- Contracting
- Workforce coordination
- Replacement
- Quality monitoring
Output
A qualified managed expert cohort aligned with the project requirements.
You define the expertise and capacity. We provide and manage the qualified expert cohort.
Before sourcing experts, define the specification the project will be staffed and measured against.
- Domain
- Credential
- Experience
- Geography
- Task
- Volume
- Availability
- Quality threshold
- 1Client requirements
- 2Expert matching and qualification
- 3Onboarding
- 4Production
- 5Quality review
- 6Expert adjudication
Not another annotation marketplace
AI teams should not need to search freelancer profiles and recruit professional reviewers one by one.
Marketplace models push recruiting, vetting, contracting, and replacement onto the buyer. That can work for commodity tasks. It becomes expensive when the work requires a license, a practice area, a jurisdiction, or a scarce scientific specialty.
Traditional staffing is designed to place people into jobs. AI data and evaluation programs usually need time-bounded capacity: a defined expert profile, a qualification bar, a weekly hour commitment, and a way to replace or scale without restarting hiring.
The customer defines the expert specification and operational requirements. We provide the capacity. The customer retains the methodology.
We are not a freelancer marketplace or traditional staffing company.
AI training and evaluation work
The same expert cohort can support more than one workflow. The constraint is the professional standard, not a single task type.
- Data annotation
- Structured labeling or review of text, documents, conversations, or other artifacts so models can learn or be measured against a defined standard.
- AI model evaluation
- Systematic scoring of model outputs for correctness, usefulness, safety, and professional acceptability against a rubric.
- Supervised fine-tuning data
- Expert-authored prompts and reference responses used to teach a model a target behavior or domain style.
- Human feedback
- Structured reviewer input on model outputs, including comments, corrections, and quality signals used in post-training.
- Preference evaluation
- Pairwise or ranked comparison of candidate outputs so training or selection systems can learn which responses are preferred.
- RLHF support
- Human review workflows that produce preference data, critiques, or reward-model labels for reinforcement learning from human feedback.
- Benchmark creation
- Design of held-out tasks, scoring rules, and reference answers used to measure model capability over time.
- Rubric development
- Definition of the criteria, severity scales, and decision rules reviewers use so evaluation stays consistent.
- Agent evaluation
- Assessment of multi-step tool use, planning, and task completion rather than a single generated answer.
- Red teaming
- Adversarial testing that looks for unsafe, incorrect, or professionally unacceptable model behavior in realistic scenarios.
- Gold answer creation
- Authoring of reference answers or accepted resolutions that later reviewers and automated checks can score against.
- Reasoning evaluation
- Review of whether a model’s intermediate logic, citations, and conclusions are valid, not merely whether the final sentence sounds plausible.
- Expert adjudication
- Senior review that resolves disagreement between qualified reviewers and produces a final accepted label or score.
Expert quality you can measure
These are operating practices, not a claim about a proprietary scoring product. The exact tests, gold sets, and reporting cadence are defined with the client.
Credential checks
Confirm the claimed license, degree, or professional standing against the project specification before work starts.
Domain qualification
Use client-defined or jointly designed tasks to test whether a professional can apply judgment to the actual work, not only describe their resume.
Calibration tasks
Align reviewers on the rubric, edge cases, and severity scale before production volume begins.
Gold sets
Hold out expert-adjudicated examples to monitor drift and reviewer consistency over time.
Independent review
Where the task warrants it, send the same item to more than one qualified reviewer and measure agreement.
Senior adjudication
Escalate disagreements to a more senior or more specialized expert rather than averaging incompatible judgments.
How buying models differ
Marketplaces, staffing, and managed expert capacity solve different problems. None is automatically better for every program.
| Criterion | How talent is sourced | Who manages workers | Client workload | Best use |
|---|---|---|---|---|
| Freelancer marketplace | Buyer browses profiles and hires one by one | Mostly the buyer | High: recruiting, contracting, replacement | One-off commodity tasks |
| Traditional staffing | Candidates are placed into jobs | Employer after hire | High if the need is project capacity, not a seat | Standing roles, not time-bounded evaluation |
| Managed expert capacity | Vendor sources against a written specification | Vendor operates the cohort | Lower: client keeps methodology and acceptance | Expert AI data and evaluation programs |
| Criterion | Purpose | Typical expert | Output |
|---|---|---|---|
| Data annotation | Create or label artifacts for training or measurement | Matches the domain of the artifact | Labels, spans, critiques, or structured fields |
| Model evaluation | Score live or held-out model behavior | Same standard the product claims to meet | Rubric scores, preferences, error taxonomies |
| Benchmark creation | Build a reusable measurement set | People who can write difficult, scoreable items | Tasks, gold answers, and scoring rules |
What kind of experts do you need?
Share the profession, specialty, experience, location, headcount, hours, duration, and project description. We assemble the 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