Use Case
Analytics and Intelligence with Endee
Detect anomalies, find behavioral patterns, and surface insights from any high-dimensional data. Vector similarity finds what rule-based systems miss.
Real-time
anomaly detection
Any data
logs, events, telemetry
1B+
events indexed per node
Built for embedding-native analytics
Anomaly Detection
Encode each event, log line, or telemetry reading as a dense vector. Store the baseline corpus in Endee. At ingestion time, query for the k nearest neighbors of each new event. Events with large average distances from their neighbors are statistical outliers and surface as anomalies in real time.
Behavioral Pattern Matching
Embed user session sequences or journey paths as vectors. Find the k users most similar to a given user to power cohort analysis, look-alike targeting, and fraud ring detection. Similarity is computed on the full behavioral sequence, not on individual attributes, so you catch patterns that rule-based systems miss.
Log and Incident Intelligence
Embed error messages and stack traces using a code-aware model. Index production logs in Endee. When a new incident fires, retrieve the most similar past incidents with their resolution metadata. On-call engineers get relevant context and past remediation steps surfaced automatically in seconds.
Embedding-space Analytics
Use ANN queries to implement nearest-neighbor classification, regression on embedding space, and cluster assignment without re-training a model. Store centroid vectors for known categories and classify new items by finding the nearest centroid. Apply metadata filters to scope analytics to specific time windows, services, or user segments.
How it works
Embed your telemetry or event data
Choose an encoder appropriate for your data type: a code-aware model for logs and stack traces, a time-series encoder for sensor readings, or a sentence transformer for unstructured text events. Embed at ingest time and write vectors to Endee asynchronously without blocking the hot path.
Index with context metadata
Store vectors alongside structured metadata: service name, host, timestamp, severity, and any business context. Endee indexes both the dense vector for similarity queries and the metadata for filtering. Use time-range filters to compare current patterns against a rolling baseline window.
Detect anomalies and surface insights
Run real-time anomaly detection by querying the distance of each new event to its nearest neighbors. Set a distance threshold based on your baseline. Use scheduled batch queries to find emerging clusters, user segments, or behavioral shifts. Export results to your observability stack or alerting system.
What teams build
Fraud Detection
Embed transaction sequences and surface transactions that are far from all known legitimate behavioral patterns.
Security Threat Detection
Encode network events and find sequences that resemble known attack patterns stored as reference vectors.
User Cohort Discovery
Cluster users by their behavioral embeddings to find natural cohorts for targeting, pricing, and product decisions.
Code Similarity Search
Find duplicate or near-duplicate code across a codebase by comparing function embeddings from a code model.