How We Work

We begin with a short discussion to understand:

• Your AI system and use case
• The type of evaluation or annotation required
• Languages, domains, and risk level
• Timeline and expected outcomes

This ensures the scope is clearly defined before work begins.

Most engagements start with a short pilot or evaluation sprint.

During this stage, we:

• Define evaluation or annotation guidelines
• Select reviewers based on language and domain needs
• Establish quality criteria and reporting format

This phase allows both teams to validate fit, quality, and workflow.

Each project is delivered by:

• One technical lead responsible for oversight and communication
• A selected group of trained reviewers or annotators

All team members are briefed on:

• Project-specific guidelines
• Quality standards
• Confidentiality and data handling requirements

Work is carried out using structured processes that may include:

• Multi-reviewer evaluation
• Consistency checks
• Spot audits and quality sampling

The technical lead reviews outputs to ensure accuracy, alignment, and adherence to agreed standards.

We provide clear, actionable outputs such as:

• Evaluation summaries
• Quality findings and patterns
• Annotated datasets with documentation

Our goal is to deliver insights your team can directly apply to improve models.

At the end of each engagement, we review results together and discuss:

• What worked well
• Areas for improvement
• Whether to extend, scale, or conclude the collaboration

Clients may continue with:

• Additional sprints
• Expanded scope
• Ongoing monthly support