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Turn traces into datasets, datasets into evals, evals into experiments. No code required — ship better AI with every release.
Most teams ship AI without knowing when it will break. We show you failures, regressions, and edge cases before your users ever see them.
“Confident AI increased our speed to market by 200%. For us, compliance and trust aren’t optional—they’re required. Confident AI helps us deliver both.”
Confident AI gives organizations an easy way for teams of different backgrounds to monitor AI apps, build datasets, and run AI evals in one simple workflow.
Trace UUID 6d63ad3c-8083-fa75-93dd-82e36b52996a
How can I improve my credit score from 670 to 700?
Improving your credit score from 670 to 700 is definitely achievable with some focused efforts. Here are several strategies you can implement to help boost your score:
Inspect every trace in production, monitor quality and latency over time, and get notified immediately when regressions or incidents occur.
Turn observability traces into evaluation datasets automatically, then auto-categorize failures and edge cases so dataset operations scale with your product.
Let product owners and non-engineers call your AI app directly over HTTP and streaming endpoints, without waiting on engineering or relying on mock single-prompt tests.
Evaluating multi-turn chatbots bottlenecks on manually prompting realistic conversations. Simulate thousands of conversations in 10 minutes to test behavior before release.
In a regulated industry? Confident AI centralizes red teaming workflows so you catch risks before users do, with PDF ready assessment reports you can share with stakeholders.
Manage prompts with a git-based branching workflow synced to your codebase. Teams can work in parallel, enforce merge permissions, and gate merges with eval results.
Looking to enable Confident AI for your organization? Our APIs give you the ability to automate everything, from prompts to even building your own custom dashboards.
1from deepeval.prompt import Prompt2from deepeval.prompt.api import PromptMessage3 4prompt = Prompt(alias="support-agent-v2")5 6# Push to Confident AI, synced with your GitHub repo7prompt.push(8 messages=[9 PromptMessage(10 role="system",11 content="You are an AI support agent with access to tools. "12 "Use them to look up orders, process refunds, and resolve issues. "13 "Always verify the customer's identity before making changes.",14 ),15 ]16)17 18# Pull a specific version in production19prompt.pull(version="latest")SDKs in Python, Typescript; 20+ integrations, including OpenAI, LangGraph, Opentelemetry, and tons of more LLM gateways.
Join the largest and fastest growing community on AI evaluation.
FinomBefore Confident AI, a single improvement cycle took 10 days — I'd create a task, assign it to an engineer, wait for availability, and go back and forth. Now the same cycle takes three hours, and our product managers can run it themselves.
Confident AI saves us 480+ hours of manual AI evaluation every month — and gives us the data to defend every quality decision in front of engineering, product, and leadership.
Confident AI gave our team one place to turn production failures into datasets, align metrics, and keep regressions out of releases without waiting on custom engineering work.
HumachWe run a lot of large-scale, multi-turn simulations, and Confident AI made it far easier to design scenarios and execute those tests without piecing together external tools.
Thanks to Confident AI, we were able to move to a fine-tuned model and cut our LLM costs by 80%. This opens up whole new use cases now to generate better output with more targeted LLM calls.
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