ScaleWorkAgencyHuman intelligence for better AI
Expert-led data.
AI you can trust.
Managed teams of carefully qualified specialists who evaluate, annotate and improve the data behind high-performing AI systems.
Human-reviewed delivery with controlled access and accountable project leadership.
Evaluation layerHuman judgment
Managed workflow
Review in progress
AI response evaluationRLHF workflowsData annotationModel safety testingExpert task creation
Built for demanding AI programmes
One managed layer between your model team and the human work it depends on.
Managed capabilities
Human expertise across the AI development lifecycle.
From high-volume classification to specialist model critique, every programme is designed around the task, contributor profile and quality threshold—not a generic crowd.
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AI response evaluation
Rank, score and critique model outputs against clear, project-specific rubrics.
Data annotation & labelling
Create clean, consistent labels across text, image, audio, video and structured datasets.
RLHF services
Managed human-preference workflows for ranking, scoring, feedback and critique generation.
Model safety & quality testing
Test model behaviour across policy, harm, bias, hallucination and difficult edge cases.
Prompt & answer creation
Build domain-relevant prompts, reference answers and edited instruction datasets.
Expert AI training tasks
Bring coding, mathematics, language and professional-domain judgment into model training.
Freelance workforce management
Recruiting, vetting, onboarding, allocation, support and performance control—managed as part of delivery.
Operating model
Designed as a system.
Managed by people.
We convert your outcome into an operating plan with defined roles, instructions, calibration, production controls and delivery checks.
See how delivery works01
ScopeOutcome, data and risk
02
Recruit & qualifySkill and access gates
03
Train & calibrateExamples and thresholds
04
ProduceMonitored task delivery
05
Review & deliverFinal validation and reporting
Active quality signalStandards stay visible from pilot to final delivery.
Specialist workforce
The right judgment for the task.
Contributor profiles are built from the work backwards. Qualifications, assessments and review depth change with the risk and difficulty of the data.
Software
Developers who can read, test and improve code.
Languages
Fluent review with cultural context.
Mathematics
Reasoning checked by capable specialists.
Structured evaluation for difficult and sensitive outputs.
Explore expert tasksQuality assurance
Quality is controlled at every handoff.
We do not wait until final delivery to discover drift. Programme controls are designed before production and reinforced with observable contributor performance.
Explore quality & securityControl 1
Qualification tests
Confirm the skills the project actually needs.
Control 2
Calibration tasks
Align judgment before production begins.
Control 3
Layered review
Match review depth to risk and complexity.
Control 4
Performance scoring
Track patterns, not isolated anecdotes.
Control 5
Issue resolution
Escalate ambiguity and correct root causes.
Where we work
Use cases that need more than generic labour.
Generative AI
Preference, evaluation and supervised datasets for models that generate useful, relevant responses.
Software engineering
Code review, debugging, test creation and technical evaluation with qualified developers.
Language models
Multilingual evaluation, localisation, intent work and culturally informed quality review.
Mathematics
Problem creation, solution verification and reasoning-quality assessment by capable specialists.
Customer support AI
Conversation labelling, policy adherence, tone review and realistic support scenarios.
Safety evaluation
Policy testing, red teaming and structured identification of harmful or unreliable behaviour.
Representative engagement
Illustrative workflow—not presented as completed client work.
Coding-model evaluation
A specialist review programme for code quality, correctness and instruction following.
Rubric designSkills assessmentCalibrated productionReviewed dataset
For specialists
Use your expertise to help improve AI.
Join a selective contributor network for paid evaluation, annotation and expert-content assignments. Opportunities depend on project demand and qualification.
Field notes
Practical thinking for AI data teams.
Evaluation
How human evaluation improves AI models
A practical look at rubrics, calibration and the signals model teams can actually use.
7 min read QualityQuality control for AI data annotation
Why good guidelines are only the beginning—and how review systems prevent drift.
7 min read ProcurementSelecting an AI training data partner
The questions that reveal how a provider manages people, data, risk and accountability.
6 min readQuestions, answered
What teams usually ask before we begin.
If your question is project-specific, include it in your consultation request and we’ll address it directly.
What makes ScaleWorkAgency different from an open contributor platform?
We operate as a managed delivery partner. Contributor recruitment, qualification, calibration, allocation, review and issue resolution sit inside one accountable project structure.
Can you support a pilot before a larger programme?
Yes. A bounded pilot is often the best way to validate instructions, quality thresholds, throughput assumptions and the required contributor profile.
How do you handle sensitive data?
Controls are defined during scoping and may include restricted contributor selection, role-based access, confidentiality terms, minimum-access workflows, secure file exchange and retention rules.
Which languages and domains do you cover?
Coverage is confirmed for each project based on the exact level of proficiency or professional expertise required. We do not claim coverage until suitable contributors have been identified and qualified.