Confident AI LLM Evaluation and Red Teaming Platform

Confident AI Inc. is a San Francisco startup founded in 2024 by Jeffrey Ip and Kritin Vongthongsri and backed by Y Combinator (Winter 2025). It builds DeepEval, an open-source framework for unit testing LLM applications, and DeepTeam, an open-source LLM red teaming framework, and sells an enterprise AI quality platform on top of them.

The platform combines LLM evaluation with 50+ research-backed metrics, multi-turn and voice simulation and CI/CD regression testing; observability that runs evals on every trace, span and thread with drift detection and alerting; AI governance with audit trails, RBAC and release gates; and AI red teaming that probes prompt injection, jailbreaks, bias, PII leakage and misinformation with 50+ attack patterns, aligned to the OWASP Top 10 for LLMs, NIST AI RMF and EU AI Act with CVSS severity scoring. Case studies include Finom, RLDatix, Amdocs, Humach and Supernormal.

• LLM evaluation: 50+ research-backed metrics, multi-turn simulation and human metric alignment.
• Regression testing: evals in development and CI/CD pipelines to block failing releases.
• Observability: tracing with evals on every span and thread, drift detection and real-time alerting.
• AI red teaming: 50+ attack patterns for prompt injection, jailbreaks, bias and PII leakage with CVSS scoring.
• AI governance: audit trails, RBAC, compliance evidence and org-wide release gates.
• Open-source core: DeepEval and DeepTeam frameworks under the Apache-2.0 license.

Vietnamese software outsourcing firms can add DeepEval-based test suites to LLM projects delivered for foreign clients. Banks and insurers can red team customer-service chatbots and keep compliance evidence for audits. E-commerce platforms can monitor production assistants for hallucination and drift after model upgrades.

Engineering teams catch regressions in prompts, models and agents in CI/CD before release instead of relying on manual spot checks. Security and compliance staff can run no-code red teaming assessments and get reports mapped to OWASP, NIST and the EU AI Act. A free tier and open-source frameworks lower the cost of starting, while enterprise plans add on-prem deployment, data residency and HIPAA.

As companies move LLM features and agents into production, evaluation and red teaming are becoming standard release steps rather than research tasks. Regulators and auditors increasingly ask for documented testing against frameworks such as OWASP and NIST, and development teams in Southeast Asia often adopt open-source tools first before buying an enterprise platform.

Founded in 2024 in San Francisco and part of Y Combinator's Winter 2025 batch, with a team of 7 listed on its YC profile. The pricing page shows SOC 2 Type II, HIPAA and GDPR badges and a trust center. Plans range from a free tier to Starter at 200 USD and Team at 2,000 USD per month, with custom Enterprise pricing.