Confident AI: Enterprise AI Evaluation & Observability Platform
Where AI Quality is Standardized. Not Improvised.
Standardize how different teams 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[-20,853,773,733+]
One eval standard. Enforced across every team.
Align every team to the same evals and quality bar — no matter who ships the release.
TIME TO PRODUCTION
WITH CONFIDENT AI 3 WEEKS
WITHOUT CONFIDENT AI 3 MONTHS
“We hit a point where every AI team was building their own eval stack. That’s fine for one product. With five, ten, fifteen AI initiatives across the portfolio, it’s never going to live up to our high standards of AI governance.”
— Richard Jarvis, Chief Technology Officer, RLDatix
Where product, QA, and engineering align.
One platform that gives engineers, product owners, and QA teams a shared source of truth.
LLM Tracing
Trace UUID 6d63ad3c-8083-fa75-93dd-82e36b52996a
TRACE TREE 6d63ad3c-8083-fa75-93dd-82e36b52996a
| Component | Type | Latency |
|---|---|---|
| ics_orchestrator | AGENT | 23.52s |
| ops_analyst_agent | AGENT | 10.41s |
| gen_dynamics_knowledge | FUNC | 2.10s |
| gen_response_w_tracing | LLM | 8.31s |
| net_ops_lookup | TOOL | 2.08s |
| net_ops_lookup | TOOL | 1.87s |
| ops_report_formatter | FUNC | 12.84s |
| gen_response_w_tracing | LLM |
MODEL gpt-4.1
TOKENS 847 in / 1,203 out
LATENCY 8.31s
INPUT How can I improve my credit score from 670 to 700?
OUTPUT Improving your score from 670 to 700 is achievable. A few strategies to start with:
- Check Your Credit Report — Pull a free copy from each of the three major bureaus.
- Pay Bills On Time — Payment history is the largest factor in your score.
TOTAL LATENCY 23.52s
LLM CALLS 1
TOOL CALLS 2
TOTAL TOKENS 2,050
COST $0.038
“Before 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
For AI that has to be safe. Not just useful.
Purpose built for industries where a perfectly functional AI is not good enough.
OWASP Top 10 for Agentic Applications 2026
A comprehensive list of the most critical security risks associated with agentic AI applications.
Risk Categories
| Risk Category | Vulnerability Types | Attack Vectors | Total Test Cases |
|---|---|---|---|
| ASI01:2026 Agent Goal Hijack | 15 | 5 | 75 |
| ASI02:2026 Tool Misuse & Exploitation | 10 | 2 | 20 |
| ASI03:2026 Agent Identity & Privilege Abuse | 13 | 2 | 26 |
| ASI04:2026 Agentic Supply Chain Compromise | 8 | 1 | 8 |
“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 Austin, Chief AI Officer, Humach
Built for every step of the AI lifecycle.
QUALITY ALERT Faithfulness below threshold rag_pipeline - 2m ago
| Trace ID | Endpoint | Latency | Quality | Status |
|---|---|---|---|---|
| tr_8f3a2c1d/v1/chat/rag | 2.4s | 0.58 | FAIL | |
| tr_e7b14d9f/v1/chat/rag | 3.1s | 0.62 | FAIL | |
| tr_2c9d4e7a/v1/summarize | 1.1s | 0.91 | PASS | |
| tr_5a8b3f2e/v1/summarize | 0.9s | 0.94 | PASS |
P50 latency 1.2s
Avg quality 0.76
Alerts today 3
Alert on monitored traces
Inspect every trace in production, monitor quality and latency over time, and get notified immediately when regressions or incidents occur.
Dataset auto-curation
Turn observability traces into evaluation datasets automatically, then auto-categorize failures and edge cases so dataset operations scale with your product.
APIs for the entire pipeline.
Every part of Confident AI is exposed as an API. Version prompts, build datasets, ingest traces, provision projects, and enroll them into governance policies — wire it into whatever your team already runs on.
from confidentai import ConfidentAI
confident_ai = ConfidentAI()
# Create a dedicated project for a new agent or customer
project = confident_ai.projects.create(name="support-bot")
# Route that agent's traces with its own Project API Key
print(project.project.id)
print(project.api_key.value)
Testimonials
Trusted by companies that take AI seriously.
“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
“We 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
FAQ
What is Confident AI?
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.
Does Confident AI offer LLM observability?
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.
Can I self-host Confident AI?
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.