Proctoring & Assessment Intelligence
ML-powered integrity intelligence that supports fair, reviewable, and defensible assessments — designed for scale, governance, and high-stakes decisions.
Run large-scale, high-stakes assessments with structured review workflows and reliable integrity evidence — without overwhelming reviewers.
Meet governance and compliance expectations with traceable session data, reviewable signals, and standardized enforcement policies.
Configure rules, thresholds, and review criteria aligned to institutional or enterprise policy.
Execute assessments or evaluations with ML-backed integrity monitoring in the background.
Collect structured telemetry, timestamps, and contextual evidence during the session.
Use guided review workflows to make consistent, defensible decisions.
Detect and surface meaningful behavioral and environmental signals — designed to reduce noise and avoid false positives.
Signals are presented as reviewable evidence, not automatic punishments — keeping humans in control of decisions.
Reconstruct sessions with time-based events, signal correlation, and contextual playback for confident reviews.
Built to support thousands of concurrent sessions reliably — without degrading signal quality or system performance.
Standardize enforcement across cohorts using configurable rules, thresholds, and escalation paths.
Traditional proctoring overwhelms teams with raw alerts. KeneLabs focuses on signal quality, reviewability, and fairness — so integrity decisions are clear, explainable, and defensible.
ML models prioritize meaningful integrity indicators instead of flooding reviewers with low-value alerts.
Every signal is contextualized for human judgment — reducing false accusations and operational noise.
Integrity workflows strengthen institutional credibility and decision confidence.
Instead of a long list of alerts, we group signals into categories that reviewers can understand quickly — with timestamps, context, and policy alignment.
Patterns that may indicate unusual behavior — surfaced as reviewable events, not auto-penalties.
Session context indicators that help reviewers understand what happened — without jumping to conclusions.
Stability and integrity context that supports defensible review — especially at scale.
Whether it’s university exams, certifications, or hiring assessments — the system is engineered to scale without losing consistency or control.
Run thousands of sessions in parallel without performance degradation.
Maintain integrity signal quality across devices, locations, and network conditions.
Fault-tolerant design ensures sessions complete cleanly even under load.
For regulated environments, integrity decisions must be explainable. KeneLabs provides traceability and structured evidence from session start to final decision.
Complete timelines with signal references, reviewer actions, and outcomes.
Decisions align to predefined rules instead of ad-hoc reviewer judgment.
Evidence and logs support internal audits and external reviews.
AI surfaces patterns and anomalies, while humans remain responsible for interpretation and final decisions.
Identify anomalies across sessions without manual scanning.
Signals are presented with context and timestamps — not black-box scores.
Tuned to reduce environmental and demographic bias where possible.
KeneLabs isn’t point-solution proctoring. Proctoring Intelligence connects to our Learning Platform and Secure Interview workflows — so evidence, policies, and outcomes stay consistent across the student-to-hiring journey.
Deliver course tests with reviewable integrity signals and consistent governance — ideal for internal college readiness programs.
Use integrity context and evidence trails to support high-trust interview sessions when needed — especially for high-stakes roles.
One system of record for integrity evidence and outcomes — easier audits, clearer stakeholder trust, better operational control.
High-integrity systems avoid extremes. We design for fairness, transparency, and governance — not fear-driven surveillance.
We don’t auto-fail candidates based on one signal. Decisions require review and policy context.
We don’t optimize for intimidation. We optimize for defensible outcomes and reviewer clarity.
We don’t flood teams with raw events. We surface prioritized, grouped signals with timelines and context.
A proctoring system that supports fairness, governance, and scale — without operational chaos or reviewer burnout.
Reduce reviewer load with grouped signals, guided reviews, and clear timelines for fast adjudication.
Protect academic credibility with standardized enforcement and audit-ready integrity trails.
Run high-stakes assessments and evaluations with confidence, fairness, and defensible evidence.
We’ll help you deploy ML-powered integrity workflows that are reviewable, defensible, and built for high-volume environments.