LEAFBYTE
Discuss a project
QUALITY & SECURITY
AI data projects depend on consistent human judgment, controlled access, clear accountability, and measurable quality. LeafByte designs delivery workflows around project-specific quality requirements and responsible data handling from qualification through final review.
QUALITY FRAMEWORK
QUALITY IS A PROCESS, NOT A FINAL CHECK.
LeafByte’s delivery model introduces quality controls throughout the project lifecycle rather than relying only on final inspection.
DEFINE → QUALIFY → CALIBRATE → PRODUCE → REVIEW → MEASURE → IMPROVE
DEFINE — Establish task guidelines, evaluation criteria, escalation rules, and quality expectations.
QUALIFY — Assess evaluators against project-specific requirements before production access.
CALIBRATE — Align selected evaluators using examples, rubrics, edge cases, and feedback.
PRODUCE — Execute work according to documented project instructions.
REVIEW — Apply sampling, secondary review, or other project-specific QA procedures.
MEASURE — Track quality signals, recurring errors, evaluator consistency, and workflow performance.
IMPROVE — Refine instructions, provide feedback, retrain where appropriate, and improve delivery.
EVALUATOR QUALITY
CONSISTENCY STARTS WITH THE PEOPLE DOING THE WORK.
SCREENING
Candidates can be evaluated against language, domain knowledge, reasoning, instruction-following, and other project requirements.
QUALIFICATION
Project-specific assessments can be used before evaluators enter production.
CALIBRATION
Evaluators can be aligned using shared examples, scoring rubrics, edge cases, and feedback.
ONGOING REVIEW
Performance can be monitored throughout delivery, with additional review, feedback, retraining, or removal when required.
QUALITY CONTROL
MAKE QUALITY VISIBLE.
Different projects require different quality-control strategies. LeafByte can configure review procedures around the risk, complexity, throughput, and accuracy requirements of an engagement.
Secondary review · Quality sampling · Gold-standard tasks · Inter-annotator agreement · Consensus review · Escalation workflows · Error categorization · Guideline clarification · Evaluator feedback · Rework procedures · Performance monitoring
Specific quality metrics, acceptance thresholds, and review rates should be defined with the client for each engagement.
DATA HANDLING
ACCESS SHOULD FOLLOW THE REQUIREMENTS OF THE PROJECT.
Data sensitivity varies significantly across AI workflows. LeafByte’s delivery model is designed to establish project-specific data-handling procedures based on client requirements, the nature of the dataset, and the responsibilities assigned to each team member.
MINIMUM NECESSARY ACCESS — Project access can be limited according to role and workflow requirements.
PROJECT-SPECIFIC ACCESS — Team members should receive access only to systems and information required for assigned work.
CONFIDENTIALITY — Confidentiality obligations and project-specific handling requirements can be incorporated into onboarding.
ACCESS CHANGES — Access should be adjusted or removed when responsibilities change or participation ends.
WORKFORCE GOVERNANCE
CLEAR RESPONSIBILITY AT EVERY LEVEL.
Managed AI data projects require accountability beyond individual workers. Team composition varies according to the project.
CLIENT / PROJECT REQUIREMENTS
↓
PROJECT COORDINATION
↓
TEAM LEADERSHIP
↓
EVALUATORS / ANNOTATORS
↓
QUALITY REVIEW
Guideline communication · Evaluator onboarding · Qualification tracking · Production oversight · Quality review · Escalation management · Feedback coordination · Access removal
CONFIDENTIALITY
CLIENT DATA IS NOT TRAINING MATERIAL FOR OUR WORKFORCE.
Project information should be used only for the work it was provided to support. LeafByte can establish confidentiality expectations, project-specific handling instructions, and workforce agreements appropriate to the engagement.
Client data should not be independently reused, published, sold, or repurposed by project personnel.
CLIENT-DEFINED CONTROLS
SECURITY REQUIREMENTS SHOULD MATCH THE ENGAGEMENT.
Enterprise clients may require additional controls based on data sensitivity, internal policies, regulatory obligations, or project risk. LeafByte can evaluate project requirements during scoping and determine what operational or technical controls are necessary before work begins.
REQUIREMENTS → REVIEW → DELIVERY DESIGN → APPROVAL → PROJECT LAUNCH
Where a project requires controls or certifications LeafByte does not currently maintain, those requirements should be identified before engagement and addressed before work begins.
CONTINUOUS IMPROVEMENT
EVERY REVIEW SHOULD MAKE THE NEXT BATCH BETTER.
Quality findings should feed back into the delivery system. Recurring errors, ambiguous guidelines, evaluator disagreements, and edge cases can be analyzed to improve instructions, calibration, training, and review procedures.
WORK → REVIEW → FINDINGS → FEEDBACK → CALIBRATION → IMPROVED WORK
Tell us about your workflow, data sensitivity, quality requirements, and delivery expectations. LeafByte can help design a project approach around the controls your engagement requires.