Variable capacity
Evaluation and data collection surge around model launches, red-team exercises, and benchmark refreshes. A fixed internal team is sized for the trough or the peak, rarely both.
Data annotation outsourcing
Data Annotation Outsourcing for Complex AI Projects
Outsourcing simple annotation is easy. Outsourcing work that requires professional judgment is not. Medical AI may require clinicians. Legal AI may require attorneys. Financial AI may require finance professionals. Scientific models may require researchers and PhDs.
We are not a freelancer marketplace or traditional staffing company.
What is data annotation outsourcing
Data annotation outsourcing is buying managed external capacity for labeling, review, or evaluation so the buyer does not recruit and manage each professional. For expert AI work, the object is a qualified cohort that can apply a professional standard, not a public freelancer listing.
How the work is bought
Data annotation outsourcing is the transfer of labeling, review, or evaluation work to an external team that the buyer does not have to recruit and manage person by person. For commodity tasks, that team can be a general workforce. For professional domains, it needs to be a qualified cohort: people who can apply a clinical, legal, financial, scientific, or engineering standard to data that looks plausible to a non-specialist.
Stratum provides managed professional expert capacity for projects where generic crowd annotation is insufficient. The buyer keeps the methodology. We provide the people and the operating layer around them.
When should data annotation be outsourced
Outsourcing is usually a response to capacity, scarcity, or time—not a claim that internal teams are incapable. The programs that benefit most have a clear expert profile and a reason the work should not sit on a small in-house queue forever.
Specialized knowledge
The next domain is not “more of the same labels.” It is oncology after primary care, tax after commercial contracts, or repository-level coding after snippet review.
Short-term programs
A six-week gold-set build does not justify a hiring plan. It does justify a defined cohort with a start date and an exit.
Benchmark creation
Held-out tasks need people who can write items that are difficult for the model and still scoreable by a professional standard.
Evaluation surges
Side-by-side model comparisons and regression suites consume reviewer hours faster than most research teams can spare.
Geographic or jurisdictional expansion
A legal or clinical product that enters a new jurisdiction often needs reviewers who actually practice there, not a translated rubric.
When data annotation requires domain experts
Medicine, law, finance, science, engineering, and software produce errors that can look complete to a general reviewer. A discharge summary can omit a contraindication and still read fluently. A contract clause can be locally invalid and still look standard. A valuation model can double-count cash and still format cleanly. A paper citation can fail to support the claim. Code can pass a docstring check and fail the actual algorithm.
If those mistakes would be obvious to a professional and invisible to a crowd worker, the project is no longer a labeling problem. It is an expert judgment problem.
| Criterion | In house | Managed outsourcing |
|---|---|---|
| Speed | Faster if the experts already sit on the team | Faster when the next specialty is not on payroll |
| Control | Highest if the same people own method and labels | High if the client keeps rubrics and acceptance |
| Capacity | Hard to surge without hiring | Designed for time-bounded or variable volume |
| Recruiting burden | Stays internal | Moved to the operating vendor |
| 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 |
Expert domains available
Domains below are the starting map, not a public inventory. Specialties are sourced according to the project requirements.
- Healthcare
Physicians · Registered nurses · Pharmacists · Clinical specialists
- Legal
Attorneys · Legal researchers · Practice area specialists
- Finance
Investment bankers · Financial analysts · CPAs · Accounting professionals · Private equity professionals
- Science
PhDs · Researchers · Bioinformaticians · Chemists · Biologists
- Engineering
Mechanical engineers · Electrical engineers · Technical specialists
- Software
Senior developers · Software engineers · Technical reviewers
AI training and evaluation work we support
Annotation is often the search term. The actual statement of work is usually broader: the same experts may write gold answers, score model traces, or adjudicate disagreements.
- Annotation of text, documents, and professional work products
- Supervised fine-tuning data authored by practitioners
- Preference data and ranked comparisons
- RLHF support where human feedback is the training signal
- Benchmark item writing and scoring rules
- Rubric development and severity scales
- Agent evaluation across multi-step tool use
- Red teaming in professionally realistic scenarios
- Reasoning evaluation, not only final-answer scoring
- Expert adjudication of disputed labels
Our data annotation outsourcing process
The process is designed so the buyer does not have to become a recruiting desk. It is also designed so quality is not postponed until the end of a large batch.
Step 1
Requirements
Capture the domain, credentials, geography, volume, duration, task type, data access model, and the quality bar the client will accept.
Step 2
Expert profile
Translate the brief into a sourcing profile. This is a specification, not a job post and not a public freelancer listing.
Step 3
Qualification
Verify credentials where required and run domain tasks that resemble production work rather than generic attention checks.
Step 4
Pilot or calibration
Align reviewers on the rubric, gold examples, and edge cases before the program takes volume.
Step 5
Onboarding
Place qualified experts into the client’s tools, confidentiality terms, and workflow conventions.
Step 6
Production
Operate the agreed capacity: hours, coverage windows, and task routing against the live queue.
Step 7
QA
Apply review layers, gold-set checks, and disagreement handling according to the quality plan.
Step 8
Replacement or scaling
Add, replace, or reduce experts without forcing the buyer to restart individual recruiting.
- 1Requirements
- 2Profile
- 3Qualification
- 4Pilot
- 5Production
- 6QA
Quality assurance for expert annotation
Expert work still needs a quality system. Credentials get a reviewer into the program. They do not guarantee that two specialists will apply a new rubric the same way on day one.
Multiple reviewers
Use dual review on items that are high impact, highly ambiguous, or used as gold.
Blind review where appropriate
Hide model identity or other reviewers’ labels when the comparison should not be influenced by source.
Agreement rates
Measure inter-reviewer agreement as a process signal. Low agreement can mean a weak rubric, a mixed cohort, or a genuinely hard item.
Gold sets
Seed production with adjudicated examples and refresh them when the product or rubric changes.
Escalation
Give reviewers a path to say “this is outside my specialty” instead of forcing a guess.
Expert adjudicators
Resolve remaining disputes with a more senior or more specialized professional, and feed the decision back into the rubric.
Data security and access controls
Security claims should match the operating model. Stratum can work inside client-controlled tools and environments where that is the safer design. Access is intended to be project-specific, least-privilege, and removed when the engagement ends.
This page does not claim SOC 2, HIPAA, ISO, or other certifications. Project-specific security requirements should be discussed before work starts. See the security page for the operating principles.
Data annotation outsourcing cost factors
There is no honest single price for expert annotation. A licensed specialist reviewing high-ambiguity items with adjudication is a different product from a general labeling queue. Cost is a function of the specification.
- Domain and how scarce qualified people are
- Credentials and whether active licensure is required
- Years of experience and seniority
- Geography and time-zone coverage
- Task complexity and time per item
- Number of review layers
- Adjudication rate
- Hours per week and program duration
- Security and environment constraints
- Project management and reporting load
Before sourcing experts, define the specification the project will be staffed and measured against.
- Domain
- Credential
- Experience
- Geography
- Task
- Volume
- Availability
- Quality threshold
How to choose a data annotation outsourcing company
The useful question is not “Who can provide annotators?” It is “Who can produce a stable expert cohort against this profile, with a quality system the research team can inspect?”
- Domain expertiseAsk for the exact professions you need, not a generic AI workforce claim.
- Worker qualificationInspect the tests. Resume screening is not qualification.
- Quality systemLook for calibration, gold sets, disagreement handling, and adjudication.
- TurnaroundCapacity is a function of hours and reviewer availability, not a slogan.
- Workforce continuityAsk what happens when an expert drops mid-program.
- Data accessPrefer vendors who can work in your environment when the data requires it.
- AdjudicationFind out who resolves hard cases and whether those people are more senior.
- Pricing transparencyRequire a breakdown by production, review, and management.
- Ability to run pilotsA vendor that cannot start small is asking you to underwrite their learning.
- Vendor managementConfirm who the operating counterpart is after the sale.
Frequently asked questions
- What does data annotation outsourcing mean here?
- It means a client buys managed expert capacity for annotation and related evaluation work. The client defines the methodology. Stratum sources, qualifies, contracts, and coordinates the professionals who execute it.
- Is this a freelancer marketplace?
- No. Buyers do not browse profiles, bid on jobs, or assemble a workforce one contractor at a time. The commercial object is a qualified cohort that matches a written specification.
- When is outsourcing a poor fit?
- Keep work in house when the task is the company’s core research method, when data cannot leave a tightly controlled environment and no remote access model exists, or when the team already has idle expert capacity and stable volume.
- Can experts work inside our tools and VPC?
- Yes, when the client provides access. Many programs are safer when labels never leave the client environment. The access model is part of the project design, not an afterthought.
- Do you publish a rate card?
- Not as a single number. Cost depends on domain scarcity, credentials, geography, task difficulty, review layers, adjudication, hours, duration, and security constraints.
- How do you handle reviewer disagreement?
- Disagreement is treated as a signal. The usual path is a documented rubric, a second independent review where warranted, and senior adjudication rather than silent averaging.
- What should we prepare before a first conversation?
- A draft expert profile, an example task, the expected weekly hours, the start window, and any hard constraints on geography or data access. Incomplete briefs are normal; empty briefs slow qualification.
- Can a program start with a pilot?
- Yes. A pilot is often the correct first step when the rubric is new, the domain is scarce, or the client has not yet seen how experts perform on the actual task.
- Who owns the data and the methodology?
- The client. Stratum does not take ownership of training data, prompts, rubrics, or model IP by virtue of supplying reviewers.
- Do you place people into full-time jobs?
- No. This is project capacity for AI data and evaluation work, not a recruiting service for permanent headcount.
Ready to outsource expert annotation capacity?
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