Confident AI offers an AI quality platform that automatically turns production traces into evaluation datasets, runs continuous model evaluations, and alerts teams to regressions and edge‑case failures before release. By integrating the DeepEval SDK with any LLM framework, it provides collaborative dashboards, custom metrics, and compliance‑ready monitoring for cross‑functional AI product teams.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI development teams often release models without reliable mechanisms to detect failures, regressions, or edge‑case behaviors, leading to broken user experiences and costly post‑release fixes. Existing tooling requires extensive engineering effort to collect traces, build evaluation datasets, and integrate quality checks into CI/CD pipelines.
Solution
Confident AI provides an AI quality platform that automates the creation of evaluation datasets from production traces, runs continuous evaluations, and surfaces regressions before they reach users. The service builds on the open‑source DeepEval framework to offer a library of metrics and testing logic, while adding collaboration, visualization, and workflow layers for cross‑functional teams. Users install the DeepEval SDK, instrument their LLM applications to capture trace data, define custom or built‑in metrics, and run automated evals in development, CI/CD, or production. Results are presented in dashboards with real‑time alerts, dataset management, and human‑in‑the‑loop feedback, enabling engineers, QA, and product managers to iterate safely and maintain compliance.
Target Audience
Primary customers are cross‑functional AI product teams—including engineers, QA specialists, product managers, and domain experts—at technology companies building LLM‑driven applications such as chatbots, RAG pipelines, and autonomous agents.
Features
- DeepEval SDK that integrates with any LLM framework to capture execution traces and apply metrics with a simple decorator
- Automated generation of evaluation datasets directly from production traces, with auto‑ingest and categorization
- Continuous evaluation pipelines for CI/CD, scheduled runs, and on‑demand online evals
- Real‑time monitoring and alerting of quality degradations in production environments
- Collaborative dashboards for dataset management, metric visualization, and regression tracking across teams
- Support for custom metrics, safety/red‑team testing, and human‑in‑the‑loop feedback loops
- Enterprise‑grade security features including SSO, HIPAA/SOC2 compliance, role‑based access, and optional self‑hosted deployment