Confident AI
Confident AI builds an AI quality platform for evaluating, monitoring, red-teaming, and governing LLM applications and agents. Its goal is to help cross-functional teams ship reliable AI by connecting production traces, human feedback, datasets, and repeatable evaluations.
At a Glance
- AI-native startups
- Enterprise engineering and ML teams
- Product management and QA teams building LLM applications
- Domain experts and human reviewers
- +2 more
AI Tools by Confident AI
(3)
DeepTeam
Open Source LLM Red Teaming
DeepEval
LLM Evaluation Framework
Confident AI
LLM Evaluation and Tracing Platform
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Latest News
Confident AI and TrueFoundry AI Gateway integration for evaluating every model request over OpenTelemetry
Introducing Confident Monitor for production trace classification, issue ranking, root-cause evidence, and tested fixes
Say Hello to Mr. Bits & Bytes, previewing agentic capabilities on Confident AI
Introducing Jev-as-a-Judge for faster, cheaper, more consistent online eval decisions
Products & Services
Hosted AI quality workspace combining evaluation, observability, annotation, datasets, red teaming, governance, collaboration, and production monitoring.
Open-source LLM evaluation framework, maintained by Confident AI, with 50+ research-backed metrics, Python and TypeScript support, datasets, experiments, regression testing, and CI/CD workflows.
Open-source framework for red-teaming LLM systems and AI agents; simulates adversarial attacks, tests 40+/120+ vulnerability categories depending on the product surface, and supports OWASP, NIST AI RMF, MITRE ATLAS, EU AI Act, and safety benchmark frameworks.
Production monitoring capability that evaluates incoming traces, detects and ranks issues, supplies evidence and root-cause context, and converts failures into durable evaluation coverage.
Market Position
Confident AI positions itself as an eval-first, cross-functional AI quality platform rather than a conventional application-performance monitor. It differentiates through DeepEval's open-source, research-backed metrics; a trace-to-dataset-to-regression loop; support for agents, RAG, voice, MCP, and chatbots; and integrated human review, red teaming, and governance. Comparable competitors include LangSmith, Langfuse, Arize AI/Phoenix, Braintrust, W&B Weave, Galileo, and Helicone.
Leadership
Founders
Jeffrey Ip
Co-founder and CEO; previously a software engineer at Google, where he worked on YouTube creator infrastructure, and at Microsoft, where he built document recommenders for Office 365. Creator of DeepEval.
Kritin Vongthongsri
Co-founder; AI researcher and CHI-published author who previously built NLP pipelines for fintech startups and researched self-driving cars and human-computer interaction at Princeton, where he studied operations research and financial engineering and computer science.
Executive Team
Jeffrey Ip
Co-founder and CEO
Former software engineer at Google and Microsoft; creator of DeepEval and author of Confident AI's evaluation and product material.
Kritin Vongthongsri
Co-founder
AI researcher and CHI-published author; previously worked on fintech NLP pipelines and Princeton research in self-driving cars and HCI.
Board of Directors
Founding Story
The founders first built DeepEval as a bootstrapped open-source LLM evaluation framework after seeing how difficult it was to evaluate real LLM applications reliably. DeepEval's adoption led to Confident AI and YC; the company then built a hosted cloud layer so teams could collaborate on evaluation, observability, safety testing, and governance rather than maintaining separate internal stacks.
Business Model
Revenue Model
Subscription SaaS for the hosted Confident AI platform, with self-serve tiers and custom enterprise contracts; tracing/storage also has usage-based billing per GB-month.
Pricing Tiers
Forever-free plan with usage limits and core capabilities.
Includes Free features plus full project API access; additional tracing/storage is billed at $1 per GB-month ingested or retained.
Includes Starter features with higher team and usage capabilities; additional tracing/storage is billed at $1 per GB-month ingested or retained.
Enterprise features, support, security, governance, and deployment options negotiated with sales.
Target Markets
- AI-native startups
- Enterprise engineering and ML teams
- Product management and QA teams building LLM applications
- Domain experts and human reviewers
- Financial services, healthcare, insurance, and other regulated industries
- Organizations needing AI governance, security reviews, and compliance evidence
- Evaluating and regression-testing chatbots, RAG systems, LLM applications, and multi-step AI agents
- Monitoring production traces for quality, safety, latency, cost, and drift issues
- Turning production failures and expert feedback into datasets and repeatable evals
- Red-teaming AI systems for jailbreaks, prompt injection, privacy leakage, bias, toxicity, and agentic risks
- Giving product managers, QA teams, and domain experts no-code or low-code AI quality workflows
- Enforcing AI quality and safety standards across development, staging, and production
- Panasonic
- Lego Group
- Samsung
- Phreesia