Human judgment at scale
Convert subjective user expectations into consistent, machine-readable preference and quality signals.
HUMAN-IN-THE-LOOP AI OPERATIONS
Turn human judgment into better model behavior.
Infinity designs and operates structured feedback programs that help AI systems become more useful, accurate, safe and aligned—from preference data and response scoring to expert review and production feedback.
Clear rubricsBehavior translated into criteria
Certified reviewersCalibrated human judgment
Expert escalationComplex cases routed correctly
Actionable insightFeedback connected to improvement
Models need more than labels.
They need informed judgment.
Many model failures are not simple right-or-wrong errors. Helpfulness, tone, reasoning quality, cultural fit and safety require contextual human judgment. Infinity turns these nuanced decisions into a controlled operating system with clear rubrics, trained reviewers, quality controls and traceable outcomes.
THE FEEDBACK ADVANTAGE
Make subjective quality measurable.
Your team defines the intended behavior. Infinity builds the people, process and quality layer required to produce dependable feedback signals at scale.
Better alignment
Teach models which responses are genuinely useful, safe, accurate and appropriate for the intended audience.
Reliable calibration
Reviewer certification and recurring calibration reduce disagreement before it becomes dataset noise.
Faster improvement loops
Structured error categories connect feedback directly to training, evaluation and product priorities.
Domain-aware decisions
Specialist reviewers assess claims and reasoning that generalist teams cannot reliably validate.
Measurable confidence
Agreement, audit outcomes and adjudication records reveal how trustworthy each feedback signal is.
Every feedback format inside one managed program.
Preference ranking
Compare candidate responses and identify the output that best satisfies the user intent and defined policy.
Rubric-based scoring
Score accuracy, relevance, completeness, clarity, style and safety against program-specific criteria.
Response rewriting
Transform weak model outputs into high-quality reference responses with documented correction reasons.
Conversation review
Evaluate multi-turn coherence, instruction following, memory, tone and recovery from user corrections.
Safety feedback
Identify harmful, deceptive, biased or policy-sensitive behavior and capture structured severity signals.
Expert feedback
Route medical, scientific, legal, financial, technical and multilingual tasks to qualified reviewers.
Adversarial testing
Probe difficult prompts, ambiguous instructions and edge cases to expose hidden model weaknesses.
Production feedback
Review real-world model interactions, cluster recurring failures and prioritize improvement opportunities.
From behavior definition to continuous model insight.
Objective & rubric design
Task and interface configuration
Reviewer sourcing
Training & certification
Pilot calibration
Scaled feedback collection
Blind quality review
Expert adjudication
Insight reporting
Continuous optimization
FEEDBACK QUALITY SYSTEM
Consistency is engineered, monitored and improved.
We combine qualification tests, gold-standard tasks, blind review and structured adjudication to ensure feedback reflects the rubric—not reviewer preference or fatigue.
Reviewer certificationGold-standard tasksInter-rater agreementBlind audit samplingConsensus reviewExpert adjudicationBias monitoringDrift detectionReason-code analysisRework control
RESPONSIBLE OPERATIONS
People, data and model risk remain protected.
Feedback environments can be segmented by data sensitivity, domain, language and reviewer qualification. Access and escalation policies are designed around each program’s risk profile.
- Role-based access and project-isolated teams
- Reviewer wellbeing for sensitive content
- PII handling and data-retention controls
- Bias, fairness and cultural-context review
- Issue escalation and complete audit trails
- Secure transfer and delivery procedures
Define behavior. Prove consistency. Scale feedback.
Discover
Clarify model use, target behavior, risks, languages and feedback objectives.
Calibrate
Build rubrics, examples, certification tasks and a measurable pilot.
Operate
Run certified teams, layered QA, expert escalation and delivery reporting.
Improve
Analyze disagreement and failures to refine rubrics, models and product priorities.
Build the human feedback loop your AI needs.
Share your model type, feedback objective, domain, languages, risk profile and target volume. We will structure the complete program.
Discuss your feedback program ↗