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Confident AI, from the creators of the open-source DeepEval project, sells a platform that evaluates, monitors, and red-teams large language model applications. Its pull is developer adoption. DeepEval has more than 16,000 GitHub stars, and independent researchers describe it as among the most widely adopted LLM evaluation tools. The companion DeepTeam library simulates adversarial attacks and screens model inputs and outputs for unsafe content. The platform publishes case studies from RLDatix, Amdocs, and Finom, while enterprises such as BCG and Mercedes-Benz run the free tool. The evaluation code and metrics are open for a rival to reproduce, so the durable advantage is the base of developers who already run DeepEval.
| Description | Confident AI is an LLM evaluation and observability platform from the creators of the open-source DeepEval framework, with the companion DeepTeam framework adding AI red teaming and input and output guardrails. | [f1] |
|---|---|---|
| Founded | 2024 | [f2] |
| HQ | San Francisco, California, United States | [f2] |
| Funding | $2.2M total | [f3] |
| Latest funding | Seed, $2.2M, 2025 | [f4] |
| Product | What it does |
|---|---|
| DeepEval | Open-source LLM evaluation framework with metrics such as G-Eval and DAG for testing LLM apps, RAG pipelines, and agents in code and CI/CD. |
| DeepTeam | Open-source LLM red-teaming and guardrails framework that simulates adversarial attacks across eight vulnerability categories and screens model inputs and outputs. |
| Confident AI Platform | Cloud and self-hosted platform adding dataset management, tracing, evaluation, and production monitoring on top of DeepEval. |
AI Defense Matrix
| Govern | Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|---|
| AI-Workload Platforms Inference servers, training platforms, vector DB platforms, and the model-loading supply chain. | ||||||
| AI Orchestration Tools Agentic orchestration tools, plus their plugins, skills, hooks, system prompts, scaffolding, harnesses, configuration settings, and MCP clients on user devices. | ||||||
| AI-Generated Code Code produced by AI tools, AI-assisted reviews, AI-generated infrastructure-as-code and tests, and vibe-coded apps that bypass CI/CD. | ||||||
| AI Gateways & Routers MCP proxies and gateways, LLM routers, outbound AI-service traffic, shadow AI egress, and model-registry traffic. | ||||||
| AI Model Model weights, fine-tuning checkpoints, model cards, registries, AIBOM, and the third-party LLMs your enterprise consumes. | ||||||
| Training Data Datasets used for training, fine-tuning, and continued learning. | ||||||
| Runtime AI Data User prompts, inference inputs, RAG content, vector DB content, persistent agent memory, and interaction history. | ||||||
| AI Agent Identities AI agents as non-human principals, plus credentials, keys, permission scopes, service accounts, and delegation chains across agents and tools. |
DeepTeam red-teams LLMs and AI agents against jailbreaks, prompt injection, and multi-turn attacks across more than 50 vulnerability types, and its guardrails evaluate LLM inputs and outputs, blocking malicious prompts and unsafe responses. These capabilities are mapped to the AI Defense Matrix. [f5]
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score |
|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs, demos, and third-party validation. | 4/5 |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 3/5 |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 3/5 |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 2/5 |
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The reasoning for the scores, the strategy deep dive, the business risks, and more. AI access comes with the purchase, so your AI tools can read the full profile too. You keep 12 months of access.
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A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
pivot urgently
| Dimension | Score |
|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 2/3 |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 |
Unlock the Full Analysis
The reasoning for the scores, the strategy deep dive, the business risks, and more. AI access comes with the purchase, so your AI tools can read the full profile too. You keep 12 months of access.
One-time purchase: $20 per profile.
UnlockReading several? Unlock the entire catalog.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | Confident AI homepage | official | 2026-07-06 |
| f2 | Confident AI on Y Combinator (Founded 2024, Winter 2025 batch); the DeepEval open-source project began 2023 | other | 2026-07-06 |
| f3 | Signalbase: Confident AI Secures $2.2M Seed Funding | press | 2026-07-06 |
| f4 | Confident AI seed round announcement | official | 2026-07-06 |
| f5 | AI Defense Matrix Catalog mapping (aligned to catalog) | other | 2026-07-06 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Confident AI homepage “Confident AI is SOC 2 Type II compliant and offers both cloud and on-prem deployment. All data is encrypted in transit and at rest, and we never use your data to train models.” | official | 2026-07-06 |
| s2 | Confident AI on Y Combinator (Winter 2025) “Confident AI is founded by Jeffrey Ip, a SWE formally at Google scaling YouTube's creators studio infrastructure, and Microsoft building document recommenders for Office 365, and Kritin Vongthongsri, an AI researcher and CHI-published author” | other | 2026-07-06 |
| s3 | DeepEval repository on GitHub “G-Eval, a research-backed LLM-as-a-judge metric for evaluating on any custom criteria with human-like accuracy” | official | 2026-07-06 |
| s4 | DeepEval repository metadata, more than 16,000 GitHub stars (GitHub API) “"stargazers_count": 16673” | official | 2026-07-06 |
| s5 | DeepTeam homepage “120+ vulnerabilities across 8 categories” | official | 2026-07-06 |
| s6 | DeepTeam red teaming introduction “deepteam offers 10+ attack methods such as prompt inject, jailbreaking, etc.” | official | 2026-07-06 |
| s7 | DeepTeam guardrails introduction “deepteam's comprehensive suite of guardrails acts as binary metrics to evaluate end-to-end LLM system inputs and output for malicious intent, unsafe behavior, and security vulnerabilities.” | official | 2026-07-06 |
| s8 | AI Defense Matrix Catalog: Confident AI “AI quality and LLM evaluation platform from the creators of DeepEval, with the DeepTeam framework adding red teaming and production input and output guardrails.” | other | 2026-07-06 |
| s9 | Confident AI seed round announcement “DeepEval is used at enterprises such as BCG, Astrazenca, Stellantis, Mercedes Benz” | official | 2026-07-06 |
| s10 | Signalbase: Confident AI Secures $2.2M Seed Funding “Confident AI is excited to announce a successful funding round in which the company raised $2,200,000” | press | 2026-07-06 |
| s11 | arXiv preprint: End-to-End Chatbot Evaluation with Adaptive Reasoning and Uncertainty Filtering “with DeepEval and RAGAS being among the most widely adopted” | research | 2026-07-06 |
| s12 | Confident AI pricing “From $9.99” | official | 2026-07-06 |
| s13 | Confident AI homepage customer case studies (RLDatix, Finom, Humach, Amdocs, Supernormal, a Fortune 500 medical device company) “Director of QA, Amdocs” | official | 2026-07-06 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Confident AI homepage “Confident AI is SOC 2 Type II compliant and offers both cloud and on-prem deployment. All data is encrypted in transit and at rest, and we never use your data to train models.” | official | 2026-07-06 |
| s2 | Confident AI on Y Combinator (Winter 2025) “Confident AI is founded by Jeffrey Ip, a SWE formally at Google scaling YouTube's creators studio infrastructure, and Microsoft building document recommenders for Office 365, and Kritin Vongthongsri, an AI researcher and CHI-published author” | other | 2026-07-06 |
| s3 | DeepEval repository on GitHub “G-Eval, a research-backed LLM-as-a-judge metric for evaluating on any custom criteria with human-like accuracy” | official | 2026-07-06 |
| s4 | DeepEval repository metadata, more than 16,000 GitHub stars (GitHub API) “"stargazers_count": 16673” | official | 2026-07-06 |
| s5 | DeepTeam homepage “120+ vulnerabilities across 8 categories” | official | 2026-07-06 |
| s6 | DeepTeam red teaming introduction “deepteam offers 10+ attack methods such as prompt inject, jailbreaking, etc.” | official | 2026-07-06 |
| s7 | DeepTeam guardrails introduction “deepteam's comprehensive suite of guardrails acts as binary metrics to evaluate end-to-end LLM system inputs and output for malicious intent, unsafe behavior, and security vulnerabilities.” | official | 2026-07-06 |
| s8 | AI Defense Matrix Catalog: Confident AI “AI quality and LLM evaluation platform from the creators of DeepEval, with the DeepTeam framework adding red teaming and production input and output guardrails.” | other | 2026-07-06 |
| s9 | Confident AI seed round announcement “DeepEval is used at enterprises such as BCG, Astrazenca, Stellantis, Mercedes Benz” | official | 2026-07-06 |
| s10 | Signalbase: Confident AI Secures $2.2M Seed Funding “Confident AI is excited to announce a successful funding round in which the company raised $2,200,000” | press | 2026-07-06 |
| s11 | arXiv preprint: End-to-End Chatbot Evaluation with Adaptive Reasoning and Uncertainty Filtering “with DeepEval and RAGAS being among the most widely adopted” | research | 2026-07-06 |
| s12 | Confident AI pricing “From $9.99 ; Confident AI offers the cheapest tracing on the market starting from $1/GB-month.” | official | 2026-07-15 |
| s13 | Confident AI homepage customer case studies (RLDatix, Finom, Humach, Amdocs, Supernormal, a Fortune 500 medical device company) “Director of QA, Amdocs” | official | 2026-07-06 |
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