Training data annotation
Prepare text, image, audio, video and structured records using instructions written for your model and use case.
Classification · extraction · segmentation · transcription · entity labeling
Services
The human data work behind better AI.
Marka supports scoped annotation, evaluation, and recurring review programs. The exact workflow depends on your data, risk, subject, and quality requirements.
Discuss a projectModel output review
Have trained reviewers assess responses for correctness, relevance, safety, tone and criteria defined by your team.
Rubric scoring · ranking · correction · preference data · red teaming
AI agent evaluation
Review the full interaction, including responses, tool calls, retrieved evidence, policy rules and completed business actions.
Task success · tool use · constraint following · escalation · release testing
See agent evaluation →Ongoing data operations
Run recurring annotation and evaluation without recruiting, training and managing an internal reviewer operation.
Sampling · calibration · quality review · reporting · dataset maintenance
Text and documents
Classification, extraction, entity labeling, response review, summarization evaluation, citation checking, preference ranking, and policy-based assessment.
Conversations and agent traces
Review full interactions, retrieved evidence, tool calls, business outcomes, escalation decisions, and compliance with explicit constraints.
Audio and speech
Transcription, speaker turns, intent, language, pronunciation, acoustic events, response quality, and voice-agent evaluation.
Images and video
Classification, bounding regions, segmentation, content review, object tracking, and project-specific quality checks.
Structured records
Field verification, reconciliation, normalization, exception labeling, and comparison between AI output and expected business records.
Scope
What a pilot defines
A pilot is not a generic package. It is a written agreement about one piece of work and the standard used to accept it.
| Decision | What gets agreed |
|---|---|
| Input | Data type, format, volume, sensitivity, language, and access method. |
| Instructions | Labels, scoring dimensions, examples, exceptions, and evidence requirements. |
| Reviewers | Required language, subject knowledge, experience, and access restrictions. |
| Quality | Calibration, second-pass review, disagreement handling, and acceptance threshold. |
| Delivery | Output format, timeline, reporting, correction cycle, and ownership. |
Have a dataset or AI workflow ready?
Send the real task. We will tell you whether Marka can support it and what a useful pilot would contain.
Start a pilot