Voice & speech operations
Language-specific qualification, equipment context, recording, playback/retry and human QA for voice acting and speech work.
Human operations for AI data
Operate the human layer of your AI data workflow.
Adatacore connects contributor qualification, controlled production, layered human review and traceable delivery. Our deepest production workflows today are voice, transcription and language-quality operations.
Qualified contributors
Assessment before mapped access
Assessment before mapped access
Layered human review
Corrections and escalation paths
Corrections and escalation paths
Traceable operations
Status and decisions stay connected
Status and decisions stay connected
Data operations console
Workflow active
QualificationAssessmentLanguage + track review
ProductionControlledProject-scoped work
QualityHuman QAReview before delivery
Active workflowIllustrative pipeline
Voice actingQA
TranscriptionL2 review
Language QAActive
Speech signalHuman data layer
Review decisions stay connected to the underlying work.Human-in-the-loop
Capabilities
Built around the workflows we can actually operate.
Adatacore is strongest today in voice, transcription and reviewer operations. Adjacent human-data workflows are scoped to concrete project requirements.
Transcription & language
Audio intake, Smart Split, segmentation, human correction, language guidelines, L1/L2 review and structured delivery controls.
Quality & reviewer operations
Review queues, changes requested, task auditing, quality context, application intelligence and escalation workflows.
Adjacent human-data workflows
Clearly defined annotation or evaluation work can be scoped around project guidelines, contributor roles and review requirements rather than advertised as automatically available at every scale.
We deliberately distinguish current platform depth from project-scoped capability. That keeps public claims aligned with what Adatacore can actually operate today.
Real platform evidence
Product controls already built into Adatacore.
Instead of invented customer metrics, these are examples of operating controls present in the platform today.
Track-specific applicationsLanguage + Voice Acting or Transcription assessment flows before review.
Project-scoped accessContributor and staff permissions can be limited to relevant projects and capabilities.
Application intelligenceBacklog, decisions, language mix, turnaround and reviewer throughput analytics.
Trust & Safety reviewSession, device and network context available to authorized security review.
Transcription workflowLong-audio intake, Smart Split, human correction and L1/L2 review.
Client review pathsReview and decision workflows can retain the operational context behind delivery.
Trust by design
Controls that support serious data operations.
Adatacore separates public onboarding, contributor work and administrative access. Protected workflows use authenticated access, private storage where appropriate, role-scoped permissions and auditable operational actions.
Protected project mediaAssessment and project files can remain behind authenticated access.
Role-scoped administrationAdministrative capabilities can be separated by responsibility.
Security contextSession and network signals can support authorized account review.
Public transparencyPrivacy, subprocessors, platform status and Trust Center information are published.
Two clear paths
Built for teams and contributors.
Clients need reliable execution. Contributors need clear qualification, expectations, feedback and project access.
For AI & data teams
Bring us the workflow, not just a file.
Share the data type, language, quality bar and delivery context. We will scope the human operating flow around the problem.
For contributors
Apply through a real qualification flow.
Understand the application process, assessment expectations, project access and what work availability does—and does not—guarantee.
Adatacore does not charge contributors a fee to apply or join a project.
FAQ
Before you get started.
Common questions from teams and contributors.
Current platform depth is strongest in voice/speech, transcription/language and reviewer operations. Other human-data workflows are scoped against concrete project requirements.
New contributors create an account, complete profile information, choose a language and track, and complete the relevant assessment before application review.
Quality controls depend on the project and can include staged review, reviewer notes, changes requested, L1/L2 workflows, task limits and project-specific guidelines.
Use the Trust Center for security practices, the Privacy Policy for data handling, the Subprocessors page for key providers and Status for platform availability.
Build a human data workflow your AI team can actually operate.
Share the problem, data type, language and quality bar. We will start from the workflow you need—not a generic capability list.