Technologyperception

Detection

In AI/ML, "detection" is a broad functional concept that refers to automatically identifying the presence, absence, or change of specific patterns, objects, events, or anomalies in data. It underpins many applied systems such as fraud detection, intrusion detection, defect detection in manufacturing, and object detection in images and video. Because it is a generic capability rather than a single product, there is no single vendor, logo, or canonical implementation associated with "Detection" as an entity.

Key Features

  • Pattern recognition over structured and unstructured data (e.g., logs, images, transactions, sensor streams).
  • Support for both supervised (labeled examples) and unsupervised (anomaly/outlier) approaches.
  • Real-time or near–real-time scoring to flag events, objects, or anomalies as they occur.
  • Configurable thresholds and alerting logic to balance false positives vs. false negatives.
  • Integration with downstream systems (dashboards, SIEMs, ticketing, workflow tools) for triage and response.

Pricing

Unknown

Because "detection" is a generic capability implemented across many products and open-source projects, pricing depends entirely on the specific tool or platform (e.g., per-API-call, per-seat, per-node, or open-source).

Alternatives

Rule-based Detection SystemsStatistical Process Control (SPC)Heuristic Thresholding and Alerts

Use Cases Using Detection

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AIOps (AI for IT Operations)

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Metso AI-Integrated Mineral Processing and Crushing Equipment

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