Encord vs Fortinet

Merlin is Encord's agentic intelligence layer, letting teams build, observe and optimise their data infrastructure through conversation. It creates complete labeling setups from prompts or documents, reports data metrics and coverage gaps on demand, and surfaces issues affecting model performance. It is reachable inside Encord or from external tools over Model Context Protocol, including Claude and Slack.

Data Agents automate Encord data pipelines by integrating humans, state-of-the-art models and a customer's own models into data workflows. They handle tasks such as pre-labeling, object segmentation and tracking, video captioning, audio transcription and sentiment analysis, combined with human-in-the-loop quality assurance.

Encord Active is Encord's model and data evaluation product. It evaluates and validates models against their data to surface, curate and prioritise the most valuable data for training and fine-tuning, covering model robustness checks, drift and failure-mode detection, automated label-error detection, and active learning workflows across images, video, 3D/LiDAR, audio and documents.

Encord Annotate is Encord's multimodal data labeling product, used to annotate and review images, video, audio, text, documents, DICOM, LiDAR and geospatial data. It provides AI-assisted labeling, customizable ontologies, human-in-the-loop review workflows and quality-control tooling for managing large annotation teams.

Managed annotation service: Encord supplies vetted domain experts for a customer's task and runs the projects, with an evaluation workflow built around the customer's own spec rather than volume alone.

Managed collection of real-world training data for physical AI: in-field operators, teleoperation facilities and configurable lab environments gathering the embodied, egocentric and sensor data a robotics model needs.

Encord Index is Encord's multimodal data curation and management product. It lets teams search datasets with natural language and similarity search across video, image, audio, LiDAR, text, document, geospatial and HTML files, filter by 40+ data metrics and custom metadata, visualise outliers on embeddings plots, and remove duplicates and poor-quality data via Collections. Data stays in the customer's own cloud with 'zero data migration required'.

End-to-end data service for robotics, autonomous systems and embodied AI: data collection (in-field operators, teleoperation), curation, LiDAR/point-cloud/multi-camera annotation, VLA/VLM action-captioning, and post-deployment feedback loops, with on-prem/VPC deployment.

AI orchestration platform automating security and network operations: alert triage/investigation, network configuration validation, and threat hunting, integrated across Fortinet's management products.

Discovers, controls and governs AI use across enterprise environments: shadow-AI detection (6,500+ AI application URLs), AI-application visibility (FortiView for AI), and AI-workload data-loss prevention.

Cloud-native data-loss-prevention agent combining machine-learning content/context analysis with a GenAI-powered assistant that summarizes and contextualizes incidents for insider-risk and data-egress protection.

Email security gateway combining machine learning, LLM-based content analysis and image recognition to detect phishing, BEC and other threats across the email lifecycle.

Uses AI-based behavioural analysis of network traffic to detect lateral movement, data exfiltration and privilege escalation without endpoint agents, sold as on-premises appliances and VMs or as FortiNDR Cloud.

Sandbox appliance/service for zero-day and advanced-threat detection, using AI-powered analysis (a purpose-built ML engine plus artificial neural networks) alongside static and dynamic analysis to accelerate verdicts.

A runtime security proxy deployed between applications and LLM endpoints that blocks prompt injection, jailbreaking, model poisoning and excessive-consumption attacks and enforces context-aware data-loss prevention on AI traffic.