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AML and fraud compliance platform pairing transaction monitoring, screening, and explainable AI agents for financial institutions.
Privacy-focused CAPTCHA and bot/fraud-detection service, a drop-in reCAPTCHA alternative for websites and apps.
Enterprise AI governance platform to inventory AI systems, assess risk and map controls to regulations like the EU AI Act and NIST.
Enterprise AI platform for redacting, anonymizing, and governing sensitive data across documents and AI workflows.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
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No public pricing
No public pricing
No public pricing
- ✦Real-time transaction monitoring and rule engine
- ✦Explainable AI forensics agents
- ✦Dynamic risk scoring
- ✦Watchlist/sanctions/PEP screening
- ✦AI-native case management
- ✦Automated SAR filing to FinCEN and 70+ GoAML countries
- ✦AI bot detection
- ✦Transaction fraud protection
- ✦Account-takeover (ATO) defense
- ✦Pull-based SMS MFA
- ✦Private Learning ML risk models
- ✦Two-line reCAPTCHA migration
- ✦Hundreds of integrations
- ✦AI/agent registry with shadow-AI discovery
- ✦Continuous, contextual risk intelligence
- ✦Agentic risk and control library
- ✦Policy-to-code with pre-built regulatory policy packs
- ✦Compliance mapping and audit evidence
- ✦GAIA governance assistant and 300+ integrations
- ✦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
- ✦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
- →AML compliance and monitoring
- →Reducing false-positive alerts
- →Streamlining fincrime investigations and SAR filing
- →Blocking bots and spam signups
- →Preventing account takeover
- →Reducing transaction and payment fraud
- →Stopping credential stuffing
- →Comply with the EU AI Act, ISO 42001 and NIST AI RMF
- →Track and govern AI adoption and shadow AI
- →Assess third-party/vendor AI risk
- →Govern AI agents from intake to runtime
- →Automating FOIA and public-records redaction
- →Protecting privileged data in eDiscovery
- →Preventing data leakage to AI systems
- →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