Confident AI
A YC-backed startup commercializing DeepEval, a widely-used open-source framework for unit-testing, red-teaming and guardrailing LLM applications.
Visit Website ↗ + Add to Compare Claim This CompanyOverview
Confident AI is the company behind DeepEval, an open-source framework that lets developers unit-test LLM applications — chatbots, RAG pipelines and agents — the way they would unit-test conventional software. DeepEval provides metrics for hallucination, answer relevancy, faithfulness, bias and LLM-as-judge evaluation (G-Eval), runnable in CI/CD pipelines under an Apache 2.0 license. In 2025 the company extended the framework with DeepTeam, an open-source red-teaming tool for adversarially probing LLM applications for jailbreaks, prompt injection and data leakage.
Founded by Jeffrey Ip (CEO) and Kritin Vongthongsri, the San Francisco-based company went through Y Combinator’s Winter 2025 batch and closed an oversubscribed $2.2 million seed round from YC, Flex Capital, Liquid 2 Ventures and Rebel Fund. Confident AI’s commercial platform layers dataset management, regression dashboards, prompt versioning, LLM tracing, and production guardrails on top of the open-source core.
Its differentiator is bottom-up, developer-led adoption: DeepEval has accumulated tens of thousands of GitHub stars and millions of monthly package downloads (vendor-reported figures), giving Confident AI a genuine open-source usage signal that most closed-platform AI-security vendors lack — though that scale reflects testing/evaluation usage broadly, not security-specific efficacy.
Innovation Matrix Assessment
Shipped a second major open-source product (DeepTeam red-teaming) within about a year of DeepEval's traction, with an active public GitHub release cadence.
DeepEval/DeepTeam give engineering and AppSec teams a concrete, CI/CD-automatable way to test and red-team LLM applications before shipping, lowering the barrier to routine AI testing.
YC W25 participation and a $2.2M seed from named investors are independently reported; open-source download/star counts suggest real developer adoption, though they are self-reported and the company remains very early-stage (team of roughly seven).
LLM evaluation/red-teaming frameworks are a fast-commoditizing category with several open-source and commercial alternatives (e.g., Promptfoo, Giskard) already on this platform; Confident AI is a strong entrant but not category-defining.
High open-source usage is a genuine adoption signal, but it measures testing/evaluation activity, not independently verified security outcomes; no named incident prevention or third-party benchmark was found.
As LLM application testing becomes a standard part of the software development lifecycle, developer-adopted open-source tooling like DeepEval is well positioned to remain relevant, though consolidation risk is real given well-funded adjacent competitors.
Why CISOs Should Care
Lets security and engineering teams bake automated LLM testing and adversarial red-teaming directly into CI/CD, catching hallucination, bias and prompt-injection regressions before they reach production.
What Makes It Different
Built bottom-up from a widely-adopted open-source testing framework (DeepEval) rather than top-down as a closed enterprise platform, giving it a genuine developer-adoption signal most AI-security vendors lack.
The Matrix Verdict
58/100 — INCREMENTAL INNOVATOR
Incremental tier: real open-source traction and credible early-stage funding, but it is a very small, young company in an increasingly crowded LLM-testing space without independent efficacy evidence — solid, not yet transformational.
Editorial Note: Claims vs. Verified Findings
The YC W25 batch placement and $2.2M seed round (investors: YC, Flex Capital, Liquid 2 Ventures, Rebel Fund) are corroborated across multiple funding trackers. GitHub star counts (~16k+) and download figures (millions/month) are self-reported by the company and should be treated as directional, not audited.
Sources
Alternatives to Confident AI
Adaptive Security
AI-driven platform that simulates deepfake, voice, and multichannel social-engineering attacks to train and test organizations against next-generation phishing.
Quilr
Early-stage agentic AI security startup building a 'Service-as-Software' platform to guard against human-related breaches and secure AI agent…
Zenity
Governance and security platform for AI agents and low-code/no-code development, securing agent identity, permissions and behavior across the…
Gray Swan AI
Carnegie Mellon-linked startup building automated red-teaming and runtime guardrails that frontier AI labs use to stress-test their models…
Tenzai
An agentic AI penetration testing startup building autonomous 'AI hackers' to find and validate exploitable vulnerabilities at a…
Reco
Reco secures the "agentic ecosystem" — mapping what AI agents can access across SaaS and enterprise apps, detecting…