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Answer-first summary52/100 GEO

Confident AI

LLM evaluation and testing.

AI OperationsFair citabilityVisit site →

Your Citability Score

52/100
Identity75/100
Evidence45/100
Trust15/100
Freshness50/100
Classification80/100
What to improve to rank higher
  • Identity: Add: Founder / Team, Social links.
  • Evidence: Add: At least one evidence link, Multiple evidence sources, Demo URL.
  • Trust: Add: Contact information, Support URL, Privacy policy, Terms of service.
  • Freshness: Add: Has version number, Launch date provided, Reviewed within 90 days.
  • Classification: Add: At least 2 tags.

Promotion (Boost) does not change this score — it only changes ordering. This number reflects real, verifiable citability.

Frequently asked questions

What is Confident AI?
LLM evaluation and testing.
What does Confident AI do?
Open-source LLM evaluation framework for unit testing AI outputs with metrics for hallucination, relevancy, and toxicity.
Who is Confident AI for?
ML engineers, AI product teams, and developers building LLM applications at startups and enterprise software companies
Is Confident AI verified?
Confident AI is listed on CitableHub with a citability score of 52/100, computed from verifiable profile evidence.
Confident AI logo

Confident AI

Invited

LLM evaluation and testing.

AI OperationsCH-VER-967317Listed September 11, 2026
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AI-Extractable Summary
What:LLM evaluation and testing.
For whom:ML engineers, AI product teams, and developers building LLM applications at startups and enterprise software companies
Key outcome:Reduce LLM regression failures by validating prompts, models, and outputs before release
Category:AI Operations

Structured for AI systems to extract and cite.

Citability Score

52/100
75
Identity
45
Evidence
15
Trust
50
Freshness
80
Classification
677
Impressions
0
Clicks
0
Likes
0
GQI Earned

Citable Outcome

Reduce LLM regression failures by validating prompts, models, and outputs before release.

About

Open-source LLM evaluation framework for unit testing AI outputs with metrics for hallucination, relevancy, and toxicity.

Target Audience: ML engineers, AI product teams, and developers building LLM applications at startups and enterprise software companies
Not ideal for: Teams that are not building LLM-powered products or that need a consumer chat app rather than evaluation tooling.

What makes it different

  • Purpose-built for LLM evaluation and testing rather than generic software QA
  • Supports automated regression tests for prompts, model versions, and multi-step AI workflows
  • Combines quantitative scoring with human review for more reliable quality assessment
  • Helps teams compare versions and track output quality over time

Tags & Classification

llm regression testingprompt evaluationmodel comparisonoutput quality scoringai app validation
machine learning engineersllm developersai product teamsresearch engineers
softwarefinancial serviceshealthcareretail
Platform: PlatformModel: Developer Tool

Links & Transparency

Website

Cite this Project

BibTeX
@misc{citablehub_confident-ai,
  title = {Confident AI},
  url = {https://citablehub.com/p/confident-ai},
  note = {Listed September 11, 2026. CitableHub ID: CH-VER-967317},
  year = {2026}
}
APA
Confident AI. (2026). CitableHub Software Index. https://citablehub.com/p/confident-ai.
MLA
"Confident AI." CitableHub, 2026, https://citablehub.com/p/confident-ai.