Alert on monitored traces
Inspect every trace in production, monitor quality and latency over time, and get notified immediately when regressions or incidents occur.
Backed byY Combinator
AI quality for teams that can't afford to get it wrong
Turn live traces into test cases, validate with evals, and catch vulnerabilities before they ship.
TRUSTED BY 500+ LEADING AI COMPANIES
Evals ran to date[ 0+ ]
THE ROI
One process. Every team. Every release.
Align every team to the same evals and quality bar — no matter who ships the release.
“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.”
Sean AustinChief AI Officer, Humach
WHO WE SERVE
For AI that has to be safe. Not just useful.
Purpose built for industries where a functional AI is not good enough.
ASI01:2026 Agent Goal Hijack
Attackers manipulate agent goals, plans, or decision paths through direct or indirect instruction injection, causing agents to pursue unintended or malicious objectives.
Vulnerability Types:15 / 124Attack Vectors:5 / 27
Vulnerabilities
Agentic (15)
Data Privacy (0)
Responsible AI (0)
Security (0)
PII LeakageNo priority
Deselect All (3)
Names & EmailsPhone Numbers
Exploit Tool AgentNo priority
Select All (3)
Privilege EscalationFinancial Manipulation
Attack Vectors
RoleplayWraps requests in fictional scenarios to bypass safety guardrails.
JailbreakingUses adversarial prompts to override the agent's safety policies.
Prompt InjectionEmbeds malicious instructions in inputs to hijack the agent's intent.
MultilingualTranslates harmful prompts into low-resource languages to evade filters.
Refusal SuppressionPressures the agent to never reply with disclaimers or refusals.
LeetspeakSubstitutes letters with numbers and symbols to bypass keyword filters.
“It’s all around trust in healthcare. It’s not about removing people from an organization or shipping code faster — it’s about whether this tool is high quality enough to put into the healthcare system and be trusted.”
Richard JarvisChief Technology Officer, RLDatix
HOW TEAMS WORK
Where product, QA, and engineering align.
LLM Tracing
Trace UUID 6d63ad3c-8083-fa75-93dd-82e36b52996a
TRACE TREE6d63ad3c-8083-fa75-93dd-82e36b52996a
ics_orchestratorAGENT23.52s
ops_analyst_agentAGENT10.41s
gen_dynamics_knowledgeFUNC2.10s
gen_response_w_tracingLLM8.31s
net_ops_lookupTOOL2.08s
net_ops_lookupTOOL1.87s
ops_report_formatterFUNC12.84s
gen_response_w_tracingLLM
INPUT
How can I improve my credit score from 670 to 700?
OUTPUT
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:
- Check Your Credit Report — Obtain a free copy from each of the three major credit bureaus at AnnualCreditReport.com.
- Pay Bills On Time — Payment history is the largest factor in your credit score.
TOTAL LATENCY
23.52s
LLM CALLS
1
TOOL CALLS
2
TOTAL TOKENS
2,050
COST
$0.038
THE PLATFORM
Built for every step of the AI lifecycle.
Dataset auto-curation
Turn observability traces into evaluation datasets automatically, then auto-categorize failures and edge cases so dataset operations scale with your product.
Postman for AI apps
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.
Chat simulations
Evaluating multi-turn chatbots bottlenecks on manually prompting realistic conversations. Simulate thousands of conversations in 10 minutes to test behavior before release.
AI risk assessments
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.
Git-based prompt versioning
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.
HIPAA, SOCII COMPLIANT
Our compliance standards meets the requirements of even the most regulated healthcare, insurance, and financial industries.
MULTI-DATA RESIDENCY
Store and process data in the United States of America (North Carolina) or the European Union (Frankfurt).
RBAC AND DATA MASKING
Our flexible infrastructure allows data separation between projects, custom permissions control, and masking for LLM traces.
99.9% UPTIME SLA
We offer enterprise-level guarantees for our services to ensure mission critical workflows are always accessible.
ON-PREM HOSTING
Optionally deploy Confident AI in your cloud premises, may it be AWS, Azure, or GCP, with tailored hands-on support.
AUTOMATIONS
APIs for the entire pipeline.
Every part of Confident AI is exposed as an API. Version prompts, build datasets, ingest traces, ship custom dashboards — wire it into whatever your team already runs on.
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")INTEGRATIONStay in your stack.
Stay in your stack.
We'll meet you there.
SDKs in Python, Typescript; 20+ integrations, including OpenAI, LangGraph, Opentelemetry, and tons of more LLM gateways.
pip install deepeval
COMMUNITY
The future of quality AI depends on you.
Join the largest and fastest growing community on AI evaluation.
TESTIMONIALS
Trusted by companies that take AI seriously.
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.
Igor Kolodkin,Head of AI Quality, Finom
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.
Anoop Mahajan,Director of QA, Amdocs
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.
SD
Senior Director of Engineering,Fortune 500 medical device company
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.
Sean Austin,Chief AI Officer, Humach
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.
John Lemmon,AI Lead, Supernormal
FAQ
Have a Question?
Checkout our FAQs below, or talk to a human. They won't hallucinate.
Confident AI is the AI quality platform built by the creators of DeepEval. It gives engineering, QA, and product teams a single place to evaluate, observe, and improve LLM applications — from prototyping through production.
DeepEval is our open-source evaluation framework for running LLM tests locally or in CI. Confident AI is the cloud platform that layers on top — adding collaboration, dataset management, tracing, real-time monitoring, and dashboards so the whole team can work together.
Yes. Every LLM call is captured as a trace with full context — inputs, outputs, tool calls, latency, token cost, and metadata. You can drill into any production request, set up alerts on quality degradation, and monitor trends over time without building custom logging.
Yes. Confident AI offers a fully self-hosted deployment option alongside the managed cloud. You can run the entire platform in your own VPC or on-prem infrastructure, keeping all data within your network. Self-hosting is available on our Enterprise plan — book a demo to get started.
Most teams are up and running in under 15 minutes. Install the SDK, add a few lines of code to log traces or run evals, and results show up in the platform immediately.
Yes. DeepEval integrates directly into your CI pipeline so you can run regression tests on every pull request. If quality drops below thresholds you define, the build fails — no bad prompts make it to production.
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.