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Enterprise AI platform for redacting, anonymizing, and governing sensitive data across documents and AI workflows.
Generative-AI security platform for watermarking and detecting voice, image, and video deepfakes, built on its own voice-cloning models.
Cloud DLP and CASB that classifies and protects sensitive data across SaaS apps using deep learning (now Palo Alto Networks).
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
No public pricing
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No public pricing
No public pricing
No public pricing
- ✦AI-powered document redaction
- ✦Real-time data anonymization
- ✦AI guardrails for generative-AI apps
- ✦Automated compliance enforcement
- ✦Industry-specific solutions for government, legal, and healthcare
- ✦Multimodal deepfake detection for audio, image, and video
- ✦Invisible, persistent watermarking for media provenance
- ✦Real-time deepfake monitoring bot for live meetings
- ✦Voice cloning and text-to-speech generation
- ✦Pay-as-you-go credit-based Flex pricing plan
- ✦Enterprise on-premise deployment and SSO options
- ✦Deep-learning data classification (99.5% claimed accuracy)
- ✦Cloud DLP across SaaS applications
- ✦One-click deployment across apps, devices and users
- ✦End-user self-remediation of violations
- ✦Broad SaaS integrations
- ✦Insider-threat and breach monitoring
- ✦ChatLLM access to multiple top AI models
- ✦AI agents and automation
- ✦No-code full-stack app creation
- ✦Enterprise generative AI platform
- ✦Structured ML model building
- ✦Optimization and forecasting
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Automating FOIA and public-records redaction
- →Protecting privileged data in eDiscovery
- →Preventing data leakage to AI systems
- →Enterprises verifying caller identity to prevent voice fraud
- →Media companies watermarking content for provenance tracking
- →Security teams monitoring live calls for deepfake impersonation
- →Developers building AI voice agents with cloned voices
- →Prevent data leaks across SaaS apps
- →Classify and monitor sensitive data
- →Reduce breaches from human error
- →Give security teams cloud data visibility
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects