01 / Interaction
Requester
Cancel my plan and refund last month. I did not use the service.
AI response
Your plan is cancelled and the $84 refund has been processed.
Data work for production AI
Data annotation and evaluation for AI teams.
Marka helps you label training data, review model outputs, and test AI systems with trained human reviewers.
Small pilots welcomeProject-specific instructionsBuilt by Capvise Labs
02 / System trace
subscription.cancel: success payment.refund: failed — gateway timeout
Evidence captured
03 / Marka review
Needs correctionThe reply says the refund succeeded, but the payment tool failed. The customer was given a false confirmation.
- Task completion
- Failed
- Policy compliance
- Passed
- Communication
- Misleading
- Reviewer confidence
- High
The work
Send us the data work slowing your AI team down. Begin with one scoped project, inspect the result, then expand only when it works.
01
What Marka does
The language is simple because the work should be easy to understand. We label data, review AI output, evaluate agent behavior, and operate recurring review programs.
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
Model 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
02
A polished answer can still be wrong.
The response looked correct. The transaction never happened.
Marka checks the underlying action, not only the final sentence.
The model followed the obvious rule. It missed the exception.
Review criteria can include your policies, edge cases, and escalation rules.
The answer sounded confident. The evidence said something else.
Reviewers can inspect retrieved sources and explain why a score was assigned.
03
Begin with one clear project.
A pilot reduces risk for both sides. It lets your team inspect Marka’s work before committing to a larger delivery or ongoing program.
Show us the task
Tell us what you are building, what data you have, and what a correct result should look like.
Agree on instructions
We turn the project requirements into clear reviewer guidance, examples, edge cases, and acceptance criteria.
Run a pilot batch
A small batch is completed, checked, and discussed before the project expands.
Receive usable output
You receive the completed data, quality findings, review notes, and agreed documentation.
Continue when it works
Scale the project or establish recurring review only after the agreed standard has been met.
04
Data your AI team can use.
Example delivery
Failure map
See recurring failure patterns, their frequency, severity, and the evidence behind them. Use the findings to improve prompts, tools, policies, datasets, and release tests.
Explore agent evaluationRELEASE 1.6 / 1,000 REVIEWED CASES
Incorrect success confirmation18.4%
Unsupported policy claim9.2%
Premature escalation7.8%
Constraint ignored5.6%
Authentication bypass2.1%
05
Review the work before you trust the claims.
Marka is a new venture. The website should prove the method with real artifacts rather than borrow trust through vague language or unrelated logos.
No fake scale. No mystery process.
Capabilities, customer results, and security claims should appear only after they are operationally true.
- A sample annotated dataset or evaluated agent trace
- Project instructions and quality-control documentation
- Named leadership and a direct company contact
- Clear data-handling and access practices
- Case studies with exact scope and measured results once earned
Tell us what your AI team needs reviewed.
Share the dataset, model output or workflow. We will confirm whether Marka is a suitable fit and propose a clear pilot.
Start a pilot