Confident AI

What Confident AI does

Confident AI is an AI quality platform providing an all-in-one evaluation and observability suite for technical and non-technical teams. It helps organizations build reliable AI by standardizing how teams turn live traces into test cases, validate with evaluations, and catch vulnerabilities before deployment. The platform supports every step of the AI lifecycle, from development and experimentation to production monitoring and governance. It offers tools for LLM tracing, dataset auto-curation, AI application testing, chat simulations, AI risk assessments (red teaming), and Git-based prompt versioning. Confident AI also provides enterprise-grade security features like HIPAA and SOC2 compliance, multi-data residency, RBAC, and data masking. It is powered by DeepEval, an open-source LLM evaluation framework, and DeepTeam, an open-source red teaming framework.

Products

• DeepEvalAn open-source evaluation framework for LLMs that powers Confident AI's metrics and testing logic. Used by teams at OpenAI, Google, and Microsoft.
• DeepTeamAn open-source red teaming framework that powers Confident AI's platform for adversarial LLM testing.

Key capabilities

• AI Observability Workflows
• LLM Tracing
• Dataset Auto-curation
• Postman for AI apps (API testing)
• Chat Simulations
• AI Risk Assessments (Red Teaming)
• Git-based Prompt Versioning
• Alerting on monitored traces
• Evaluation datasets from observability traces
• Enterprise security posture (HIPAA, SOC2 compliant)
• Multi-data residency
• RBAC and data masking
• 99.9% Uptime SLA
• Full LLM unit and regression testing suite
• Evals in development and CI/CD
• Custom evaluation metrics
• Online evals on live traffic
• Annotation queues & workflows
• Downstream observability workflows
• Real-time alerting
• Full Project API Access
• No-code AI evaluation workflows
• Alert integrations (e.g. Slack and PagerDuty)
• Metric & dataset versioning
• Custom RBAC
• SSO
• Dedicated On-Prem Deployment
• Infosec review
• Custom data residency
• Dedicated 24x7 technical support
• AI red teaming module
• AI governance module
• Sharable testing reports
• AI arena (run evals on prompts and AI apps)
• Regression testing
• Simulate multi-turn conversations
• No-code eval workflows
• Evaluate live AI apps via APIs
• Advanced authorization for AI APIs
• Pre-evaluation data transformers
• Dataset annotation on the cloud
• Scheduled dataset runs
• Dataset backup and version history
• Custom synthetic data generation
• Single-turn metrics (30+ research-backed DeepEval metrics)
• Multi-turn metrics (15+ multi-turn DeepEval metrics)
• Custom G-Eval metrics
• Code-eval metrics
• Metric versioning
• Thumbs up or down annotations
• Custom criteria annotations
• Custom annotation forms
• Multi-turn conversation testing
• Side-by-side experiments
• Alignment metrics with humans
• Automated evals on every change
• MCP-native workflow
• Agent graph view
• Trace annotations
• Model endpoint, cost, & latency tracking
• User-level analytics
• OWASP Top 10 for Agentic AI testing
• CVSS scoring for findings
• Continuous red teaming
• Policy enforcement for AI governance
• Audit-ready evidence for compliance

Use cases

• Standardizing AI evaluation across teams
• Validating AI models with evaluations
• Catching AI vulnerabilities before shipping
• Monitoring AI quality and latency in production
• Debugging AI issues with traces
• Auto-curating datasets from production traces
• Testing AI applications directly via HTTP and streaming endpoints
• Simulating multi-turn chatbot conversations
• Conducting AI risk assessments and red teaming
• Managing prompts with Git-based versioning
• Unit-testing AI apps in CI/CD
• Building test datasets and running regression suites
• Tracking quality metrics over time and comparing experiments
• Labeling data and providing human feedback
• Experimenting with prompts, models, and parameters
• Catching regressions pre-deployment
• Monitoring AI quality in real-time
• Red teaming for security vulnerabilities
• Building RAG pipelines
• Agentic workflows
• Chatbots
• Fine-tuning models
• Summarization
• Text-SQL
• Customer support chatbots
• Internal RAG QAs
• Conversational agents
• Automating operational processes (internal agents)
• User-facing financial products (AI agents)
• Scaling testing for enterprise voice experiences
• Ensuring compliance and trust in AI deployments
• Evaluating generative AI-powered knowledge assistants

AI approach

Confident AI provides an AI quality platform for evaluation and observability of AI applications, particularly LLMs. It offers tools for testing, monitoring, and red teaming AI, using its open-source DeepEval framework for metrics and DeepTeam for red teaming. The platform helps teams standardize evaluations, catch regressions, monitor production quality, and ensure compliance and safety for AI applications.

Tech named: DeepEval, DeepTeam, LLM-as-a-judge evaluators, RAG pipelines, agentic workflows, chatbots, fine-tuning models, OWASP LLM Top 10, OWASP Agentic AI Top 10, NIST AI RMF, MITRE ATLAS

Industries served

• Healthcare
• Insurance
• Financial Services
• Software Development
• Telecommunications

What it says sets it apart

• All-in-one evaluation and observability suite
• Standardizes AI quality across diverse teams and initiatives
• Powered by widely adopted open-source frameworks (DeepEval, DeepTeam)
• Focus on industries where AI safety and compliance are critical
• Accelerates AI improvement cycles significantly (e.g., 10 days to 3 hours for Finom)
• Cost-effective tracing ($1/GB-month, 3x cheaper than alternatives)
• No-code evaluation workflows for non-engineers (product managers, QA)
• Comprehensive multi-turn conversation testing
• 50+ research-backed evaluation metrics
• Extensive integrations with model providers, frameworks, and CI/CD pipelines
• Automated red teaming against OWASP Top 10 and custom policies
• Centralized platform for AI governance and compliance
• Ability to turn production failures into datasets
• Scalable for large enterprises with thousands of employees and complex AI portfolios
• Non-intrusive tracing with zero latency impact and silent failure mode

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.