Comprehensive workforce intelligence that goes beyond simple monitoring. Detect fraud, verify identities, and build trust—all in one platform.
Verify employee identities through face matching with government-issued IDs and GitHub activity analysis.
Track work patterns, application usage, and productivity metrics without being invasive.
Identify when employees are using AI tools to fake work or automate their responses.
Verify employee locations and detect VPN usage or location spoofing attempts.
Identify employees working multiple full-time jobs simultaneously.
Deep insights into work patterns, output quality, and team performance.
Going beyond basic monitoring to true workforce intelligence
Our AI analyzes subtle patterns in employee behavior to detect anomalies that might indicate fraud or outsourcing.
Typing Pattern Recognition
Detect when someone else is doing the work
Communication Style Analysis
Identify sudden changes in writing patterns
Work Rhythm Detection
Spot irregular patterns that suggest automation
Each employee gets a comprehensive risk score based on multiple factors, helping you prioritize investigations.
Fraud Probability
ML-powered fraud likelihood assessment
Verification Confidence
How certain we are about identity
Anomaly Detection
Real-time alerts for suspicious activities
Our assessment engine is patented — U.S. Patent No. 12,694,359. Here is how it works.
The system ingests a candidate's own commits and analyzes diff patterns to locate the moment each technical concept was introduced and every later point it was refined. Cross-file dependencies are extracted alongside it.
Concepts as nodes
Each technical concept found in the history becomes a node
Time as edges
Edges are time-relationships that show skill progression
Commit-linked output
Every question carries metadata naming the commits it came from
Five machine-learning agents run at the same time and communicate over an asynchronous shared-memory message queue.
Structural layer
Schema validation on the shape of every generated question
Semantic layer
A fine-tuned LLM handles duplicate detection via embeddings, relevance scoring, and skill-level alignment
Bidirectional feedback
Failures at either layer feed back into generation in both directions
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