Specific activities
- Annotate and classify data
- Compare responses and evaluate preferences
- Apply error categories and evaluation rules
- Review evaluated datasets for consistency
AI quality · AI training and data work
Process AI training data against clear guidelines.
AI training and data work cover labelling and categorising data and making reasoned comparisons of model responses. The agreed evaluation guidelines govern the work; model development and technical operation are not part of this service.
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Suitable starting points
When data work requires consistent decisions across many individual cases.
This service is suited to clearly defined training, evaluation and quality-assurance tasks involving AI data.
- Model responses require reasoned subject-matter feedback.
- Data needs consistent annotation or classification.
- Evaluation guidelines must be applied reliably to edge cases.
Service contribution
Reliable data work connects guidelines, individual cases and quality control.
svensystem Engineering processes and evaluates data against supplied or jointly clarified criteria. Edge cases, uncertainty and recurring errors are classified transparently.
Objectives, data access and binding evaluation guidelines remain with the client. The service does not include development or operation of a client model.
Activities and outcomes
Consistent evaluations with a documented quality status.
The activities and outcomes included in the assignment are agreed before work begins. The following selection describes possible components.
Possible outcomes
- Processed datasets with the agreed labels and categories
- Response-pair comparisons with reasoned preferences based on the evaluation guidelines
- List of ambiguous cases with explanations and questions requiring clarification
- Documentation of checked cases, identified deviations and corrections made
Working approach
From evaluation guidelines to a controlled dataset.
Clarify
Define the task, data scope, evaluation objective, guidelines and protection requirements.
Prepare
Structure data, categories, examples and edge cases for reviewable processing.
Process
Annotate, classify or evaluate data and response pairs.
Cross-check
Review consistency, guideline adherence and recurring errors.
Document
Record results, uncertainty and the achieved quality status traceably.
Responsibilities and boundaries
Data processing, model accountability and approval remain clearly separated.
svensystem Engineering
Performs the agreed data and evaluation work, checks consistency and documents edge cases and quality findings.
Client and subject-matter owners
Provide permitted data, objectives and binding guidelines and remain accountable for model use and approvals.
The service covers data and evaluation work, not the development, hosting or operation of a proprietary client model.
Specialist reviews remain with appropriately qualified subject-matter or testing professionals.
AI supports the work. Final review, evaluation and approval remain under human control.
Possible starting point