Confident AI

by Confident AI  · Based in United States → — Where AI Quality is Standardized. Not Improvised.
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Freemium
Regulatory Status Disclosed

Overview

Confident AI is an enterprise AI evaluation and observability platform designed to standardize how teams measure and monitor their AI systems. It provides a comprehensive suite of tools for LLM evaluation, observability, AI red teaming, and AI governance. The platform helps engineering, product, and QA teams align on AI quality, turning live traces into test cases, validating with evaluations, and catching vulnerabilities before deployment. Confident AI is built on DeepEval, an open-source LLM evaluation framework, and offers features like real-time tracing of LLM calls, analysis of tool calls, tracking of agent performance, and alerting on degradation. It is purpose-built for industries where AI must be safe, not just useful, including healthcare, insurance, and finance.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • LLM Evaluation with research-backed metrics
  • LLM Observability for tracing, monitoring, and alerting on production systems
  • AI Red Teaming for stress-testing LLM apps against adversarial attacks
  • AI Governance to enforce AI standards and controls
  • Automated dataset curation from observability traces
  • Git-based prompt management with branching and approval workflows
  • Quality-aware alerting for performance degradation
  • Multi-turn simulation for chatbot and safety testing
  • Self-hosted deployment options
  • API for entire pipeline

Use Cases

  • Standardizing AI quality across teams
  • Accelerating AI development and deployment
  • Ensuring compliance and trust in AI systems
  • Testing and validating LLM applications in CI/CD pipelines
  • Monitoring and improving AI agent behavior
  • Detecting and preventing AI security and safety vulnerabilities

What Physicians Need to Know

HIPAA-Compliant Infrastructure
Confident AI is HIPAA, SOC II compliant, meeting the security posture requirements for regulated industries like healthcare. They offer multi-data residency (US and EU), RBAC and data masking, 99.9% uptime SLA, and on-premise hosting options.
Clinical NLP Capabilities
While Confident AI focuses on LLM evaluation and observability, the broader field of AI and NLP is crucial for healthcare. NLP in medicine helps analyze unstructured data like physician notes and lab reports to extract insights for clinical decision-making. Confident AI's platform can be used to evaluate AI applications, which could include those with clinical NLP capabilities.
De-Identification Tools
Confident AI's platform can be used to evaluate AI applications that may incorporate de-identification tools. The importance of HIPAA-compliant de-identification for healthcare data is highlighted, with AI tools playing a key role in anonymizing patient information for research and AI development.
Medical Terminology Support
Confident AI's core offering is LLM evaluation and observability. While not explicitly stated as a direct feature of Confident AI, effective clinical AI applications require understanding and responding to clinician intent, including healthcare abbreviations and natural language, which implies strong medical terminology support.
Sandbox/Testing Environment
Confident AI emphasizes testing and evaluation of AI applications. They provide a platform for running evaluations on prompts and AI apps, regression testing, and chat simulations for multi-turn conversations. The concept of a sandbox environment is crucial for safe experimentation and AI testing before deployment.
SDK Languages
Confident AI offers native SDKs in Python and TypeScript.
Rate Limits & Pricing
Confident AI uses transparent, per-seat pricing with tracing priced at $1/GB-month. They offer a free tier with 2 seats, 1 project, 1 GB-month, and 5 test runs/week. Paid plans (Starter, Team, Enterprise) offer increasing features and capacity, with unlimited user seats, projects, and trace spans available at the Enterprise level.
Physician Tip

When utilizing AI applications built with tools like Confident AI, physicians should remember that a 'confident answer isn't the same as a correct one'. It's crucial to maintain a consistent, lightweight review workflow for any AI-generated clinical notes or recommendations before they enter the medical record system, re-establishing the clinician as the author. Transparency in how AI systems arrive at decisions is essential for clinicians to verify recommendations and build confidence. Be aware that AI tools learn from vast amounts of information and may present frequently appearing information, even if incorrect or outdated. Always consider the clinical context, as AI may lack the nuanced understanding a human clinician possesses. Teams should openly discuss AI-assisted documentation, sharing examples of both successes and areas requiring significant editing to normalize the adjustment period.

Confident AI offers SDKs in Python and TypeScript and provides over 20 integrations, including OpenAI, LangGraph, OpenTelemetry, and various LLM gateways. It is designed to work with various frameworks such as LangChain, Pydantic AI, CrewAI, Vercel AI SDK, and LlamaIndex, ensuring consistent evaluation depth regardless of the stack. The platform also integrates with the AI SDK for tracing, online evaluations, and session analytics.

Details

Category Developer Tools & APIs
Pricing Freemium
  • Free tier: 2 seats, 1 project, 5 test runs/week, 1 week retention, 1 GB-month tracing; Starter: $19.99/seat/month, tracing $1/GB-month; Team: $2,000/month; Enterprise: Custom pricing
DeploymentCloud, Self-hosted (VPC or on-premise)
Compliance
BAA Available Yes AI-estimated
HIPAA Compliant Yes AI-estimated
FDA Status Not applicable AI-estimated — unknown
Integrations
EHR Not specified
Specialties All specialties

Ratings & Reviews

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Press & Coverage

Y Combinator
Confident AI: The LLM Eval and Observability Platform for AI Quality | Y Combinator
Confident AI offers an all-in-one platform for enterprises to evaluate, observe, red team, and govern AI applications, leveraging their open-source DeepEval framework. The platform aims to help engineering teams iterate on LLM apps faster and achieve significant ROI, such as decreasing LLM costs by over 70%.
2026-07
Startup Intros
Confident AI: Funding, Team & Investors | Startup Intros
Confident AI, founded in 2024, has secured $2.2 million in seed funding, with Y Combinator as the lead investor. The company provides an open-source platform for evaluating, monitoring, and optimizing large language model applications, serving major corporate clients like Microsoft and AstraZeneca.
2026-07
CIO
Confident AI begins with confident data - CIO
This article emphasizes the critical need for trusted data as a foundation for effective AI implementation, particularly in IT operations. It highlights how underlying data quality issues can be amplified by AI, leading to paused automation efforts and increased operational and security risks.
2026-06
Fierce Healthcare
From Vendor Overload to Confident AI Adoption: A Healthcare Contact Center's Journey
This news article discusses a healthcare contact center's experience transitioning from an overwhelming number of vendors to a more confident adoption of AI solutions. The piece likely explores the challenges and successes encountered in integrating AI within a healthcare setting.
2026-06
Silent Eight
Regulatory Sandboxes and AI: The EU's Plans for 2026 - Silent Eight
The EU AI Act mandates the establishment of AI regulatory sandboxes by August 2026 to enable structured testing and validation of AI systems under regulatory oversight. This is particularly significant for financial institutions, as AI systems in AML, sanctions screening, and fraud detection are likely to be classified as high-risk.
2026-04
Signalbase
Confident AI Secures $2.2M Seed Funding to Revolutionize LLM Evaluations | Signalbase
Confident AI announced a successful $2.2 million seed funding round to advance its leading LLM evaluation platform, built on the DeepEval framework. This funding will enhance their technology and expand their team to provide accurate metrics and guardrails for LLM applications.
2025-03
medRxiv
Real-World Evaluation of Large Language Models in Healthcare (RWE-LLM): A New Realm of AI Safety & Validation | medRxiv
This preprint reports on the Real-World Evaluation of Large Language Models in Healthcare (RWE-LLM) framework, a model for ensuring AI safety in healthcare settings. It highlights that even well-trained AI can produce unsafe responses in real-world patient interactions, emphasizing the need for robust evaluation during deployment.
2025-03
Confident AI
The Comprehensive LLM Safety Guide: Navigate AI regulations and Best Practices for LLM ... - Confident AI
This guide from Confident AI delves into LLM safety, covering government AI regulations like the EU AI Act, key LLM vulnerabilities, and risk mitigation strategies. It emphasizes the importance of aligning AI behavior with ethical standards to prevent unintended consequences and minimize harm.
2025-08

Videos

Product demos, reviews, and walkthroughs for Confident AI.

View all on YouTube

Frequently Asked Questions

Confident AI offers APIs and SDKs designed for seamless integration with various healthcare platforms. Our documentation provides detailed guides and examples for connecting with common EHR systems, and our support team can assist with specific integration challenges. We prioritize interoperability to minimize disruption to your current workflows.
Confident AI is built with a strong focus on data privacy and security. Our tools are designed to be HIPAA and GDPR compliant, incorporating features like data encryption, access controls, and audit trails. We regularly undergo third-party audits and maintain certifications to ensure we meet the highest regulatory standards for protected health information.
While Confident AI offers a comprehensive suite, physicians may explore open-source libraries like TensorFlow or PyTorch for custom model development, often requiring significant in-house expertise. Additionally, some cloud providers offer healthcare-specific AI services. However, Confident AI differentiates itself with pre-built, validated models and a focus on clinical workflows and compliance, potentially reducing development time and risk.
Confident AI typically offers a tiered pricing model, often based on API calls, data processed, or number of active users. We provide various plans, from introductory tiers for smaller practices or pilot projects to enterprise solutions with custom features and dedicated support. Detailed pricing information and a breakdown of features per tier are available on our website or upon request.
While powerful, Confident AI's tools have limitations. They are not a substitute for clinical judgment and require high-quality, unbiased data for optimal performance. Complex or rare medical cases might require human oversight, and the tools' effectiveness can vary based on the specific use case and data availability. We emphasize responsible AI deployment and provide guidelines for appropriate use.
Confident AI is committed to ethical AI. Our developer tools include features for bias detection and mitigation during model training. We provide guidelines and best practices for data curation and model evaluation to help developers identify and address potential biases. Our aim is to promote fair and equitable AI solutions in healthcare.
Confident AI provides extensive support for developers. This includes comprehensive API documentation, SDK guides, tutorials, and code samples. We also offer a dedicated developer forum, technical support channels, and premium support options for enterprise clients, ensuring you have the resources needed for successful integration and ongoing maintenance.

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Suggest an Edit → | Last Verified: 2026-07-26 | First Added: 2026-07-26

Investors who backed Confident AI

Funded through the company that built this tool.

January Capital
VC T2
Singapore, Singapore
$1M-$5M
January Capital is a Singapore-based data-driven venture firm with a primary thesis around B2B SaaS and commerce platforms across Asia-Pacific, with healthcare…
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