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Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
Privacy-focused CAPTCHA and bot/fraud-detection service, a drop-in reCAPTCHA alternative for websites and apps.
AI governance and observability platform with 100+ automated tests and real-time guardrails to evaluate and monitor ML/LLM systems.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
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Free trial available
Free trial available
- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦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
- ✦100+ automated AI tests
- ✦Offline evaluation and CI/CD for AI
- ✦Real-time observability and tracing
- ✦Guardrails against PII leaks, injection, hallucination
- ✦Data-quality and drift monitoring
- ✦Compliance/governance alignment
- ✦Git, SDK, CLI and REST API integration
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- ✦OpenTelemetry-native distributed tracing across 100+ LLMs and frameworks
- ✦Online evaluation via LLM-as-a-judge or code
- ✦Offline experiments and regression detection
- ✦Annotation queues for expert review
- ✦Alerts and drift detection
- ✦Prompt management, CLI and docs MCP server
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Blocking bots and spam signups
- →Preventing account takeover
- →Reducing transaction and payment fraud
- →Stopping credential stuffing
- →Evaluate models before production
- →Monitor live AI systems for issues
- →Prevent unsafe or non-compliant outputs
- →Catch data drift and quality problems
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →Debugging multi-agent systems
- →Monitoring live agent quality at scale
- →Catching regressions before release
- →Human review of edge cases
- →Aligning automated evaluators with domain experts