Quality designed upstream
Acceptance rules and failure modes are defined before production begins.
MANAGED QUALITY ENGINEERING
Build quality into every decision and every delivery.
Infinity designs and operates the complete quality layer for AI data and human-in-the-loop programs—from acceptance criteria and calibration to independent audits, expert adjudication and continuous improvement.
Defined standardsQuality before production
Layered controlsRisk-based review depth
Visible performanceMetrics and defect insight
Closed-loop actionErrors drive improvement
Quality cannot be inspected into a dataset at the end.
Final sampling alone discovers problems after time and budget have already been spent. Infinity treats quality as an operating system: requirements are translated into measurable controls, teams are calibrated before scale, risk is monitored during production and every defect produces a corrective action.
THE QUALITY ADVANTAGE
Prevent defects. Prove performance. Improve continuously.
Your organization defines the required outcome. Infinity owns the control structure needed to reach and maintain it across the complete delivery lifecycle.
Fewer rework cycles
Early calibration and targeted controls prevent large-scale defects and schedule disruption.
Consistent delivery
One quality framework follows every team, language, domain and delivery batch.
Transparent performance
Dashboards reveal accuracy, agreement, defect severity, rework and release status.
Faster issue resolution
Clear ownership and escalation paths turn quality exceptions into controlled actions.
Continuous improvement
Every audit and adjudication feeds better guidelines, training and process design.
Every control required for dependable AI operations.
Quality planning
Convert business requirements into measurable acceptance criteria, sampling plans and escalation rules.
Guideline validation
Test instructions, examples and edge cases before production to remove ambiguity at the source.
Gold-standard control
Create and maintain trusted reference tasks for training, certification and ongoing performance checks.
Multi-layer review
Combine self-checks, peer review, independent QA, expert adjudication and final release control.
Agreement analysis
Measure inter-annotator agreement and investigate where reviewers interpret tasks differently.
Defect management
Classify errors by severity, route rework, track closure and prevent repeated delivery defects.
Drift monitoring
Detect changes in data, reviewer behavior, task difficulty and quality performance over time.
Root-cause improvement
Connect recurring errors to guidelines, training, tooling, source data or workflow design.
From requirement definition to corrective improvement.
Requirement review
Risk & defect mapping
Quality plan design
Gold-set creation
Team calibration
In-process controls
Independent QA audit
Expert adjudication
Release approval
Corrective improvement
MEASUREMENT & CONTROL
Quality performance you can see and defend.
Metrics are selected according to task risk and business impact. Results are segmented by workflow, team, reviewer, language, class and batch to expose meaningful patterns—not only averages.
AccuracyPrecision & recallInter-rater agreementGold-set performanceDefect severityAudit pass rateRework rateFirst-pass yieldDrift alertsRelease status
GOVERNANCE & TRACEABILITY
Every quality decision leaves a clear record.
Quality plans, audit samples, reviewer actions, adjudications and release approvals can be traced to the relevant dataset version and delivery batch.
- Versioned quality plans and acceptance criteria
- Controlled gold-standard ownership
- Reviewer certification and calibration records
- Defect, rework and adjudication histories
- Corrective and preventive action tracking
- Release approvals and delivery evidence
Assess. Design. Control. Improve.
Assess
Review objectives, risk, current controls, defects and delivery expectations.
Design
Define acceptance criteria, sampling, gold sets, metrics and escalation paths.
Control
Run calibration, layered reviews, audits, adjudication and release approval.
Improve
Use root-cause insight to strengthen guidelines, training, tooling and workflow.
Build a quality system that scales with your AI program.
Share your workflow, modality, risk level, current quality challenges and acceptance targets. We will structure the complete assurance model.
Discuss your quality program ↗