AI visibility score
4.3/ 10Fair

▼ 2.7 since Jun 2026

#1,682,892 of 2,705,353 in Technology

As of 2026-08-01 · Engagemii indexCheck another site →The index →

AI Visibility Scorecard

The F.A.I.L. Kit

The F.A.I.L. Kit

Unclaimed

fail-kit.dev · Technology

Borderline visible. AI bots can crawl The F.A.I.L. Kit, but the structured-data signals are thin - you are at real risk of being skipped when buyers ask ChatGPT, Claude, or Perplexity for a recommendation.

AI engines read this profile 2 times

Claude

#1,682,892 of 2,705,353 in Technology for AI visibility

4

AEO Visibility

i

Borderline · 4.3/10

85

Muse Index Score

i

AI agent readiness

Transactable · 85/100

15

AI Adoption

i

Basic · 15/100

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About The F.A.I.L. Kit

from our crawl and measurement

The F.A.I.L. Kit describes itself simply: "The industry's first forensic audit framework for AI agents. Uses receipt-based verification to catch execution integrity failures that traditional testing completely misses." When AI engines look at it, they find little to work with. It scores 4.3 out of 10.

Our crawl found structured data on the page (SoftwareApplication, WebSite, Organization), an llms.txt file for AI models and a sitemap. It is missing a heading structure that lays out what it offers.

AI crawlers have visited once in our tracking, including ClaudeBot (Anthropic).

Industry · Technology
Last scored · Aug 1, 2026

The 6 signals AI reads

Strong · Good · Fair · Weak

Structured Data

Strong

Organization / LocalBusiness JSON-LD that AI can read.

Content Structure

Weak

Clear headings and answer-style content.

Entity Clarity

Good

How clearly your brand identity reads to AI.

E-E-A-T Signals

Experience, Expertise, Authority, Trust

Weak

Experience, Expertise, Authority, Trust markers.

Technical AEO

Strong

robots.txt, llms.txt, and AI-bot crawl access.

AI Discoverability

Weak

Sitemaps and entity links AI can follow.

Off-page authority

How the web signals your brand to AI

Backlinks

Weak

No inbound links found yet.

Domain Authority

Weak

Little domain authority yet.

Reference Presence

Weak

Not in AI knowledge graphs yet.

News & Press

Weak

No press coverage found yet.

Community

Weak

No community discussion yet.

Social Mentions

Weak

No social discussion found yet.

What this score means

Your AEO score measures whether AI search engines - ChatGPT, Claude, Perplexity, Gemini - can actually read your site and cite it in answers. Roughly two-thirds of sites are invisible to them. At 4.3/10, The F.A.I.L. Kit is crawlable but under-signaled - fixable, and the signals above are where to start.

Frequently Asked Questions

What is The F.A.I.L. Kit?

The F.A.I.L. Kit (Forensic Audit of Intelligent Logic) is the industry's first forensic audit framework specifically designed for AI agents. It uses receipt-based verification to catch execution integrity failures that traditional testing completely misses. Research shows 0/10 popular frameworks (including BabyAGI, AutoGPT, and LangChain examples) are production-ready without it. The average pass rate is only 37%. It has documented 8 real AI agent failures costing $106,000+ in losses. Created by Ali Jakvani and released as open source under the MIT License.

How does The F.A.I.L. Kit detect AI agent failures?

The F.A.I.L. Kit uses a receipt verification system where every high-stakes operation must generate a cryptographically signed receipt. The system verifies receipts match claimed actions, maintaining a complete forensic timeline. It detects: (1) Missing receipts for tool calls (FK001), (2) Missing error handling for LLM calls (FK002), (3) Silent failures where tools fail but agents report success, (4) Hallucinated actions where agents claim they performed operations without actually doing them, and (5) Audit trail gaps in compliance-critical operations. This catches 63% of failures that unit t

What frameworks and LLMs does The F.A.I.L. Kit support?

The F.A.I.L. Kit supports all major AI frameworks and LLM providers. Frameworks: LangChain (Python & JavaScript), OpenAI Assistants API, Anthropic Claude, CrewAI, AutoGPT, BabyAGI, LlamaIndex, Semantic Kernel, and custom implementations. LLM Providers: OpenAI (GPT-3.5, GPT-4, GPT-4 Turbo), Anthropic (Claude 2, Claude 3), Cohere, Google PaLM, and local models via Ollama. Deployment platforms: Vercel, AWS Lambda, Google Cloud Functions, Azure Functions, Railway, Render, and self-hosted environments. It provides drop-in middleware for Next.js, Express, and FastAPI with just 5 lines of code.

Is The F.A.I.L. Kit necessary if I already have 100% test coverage?

Yes, absolutely necessary. Research shows that 100% traditional test coverage catches 0% of AI agent-specific execution integrity failures. The documented Crashcodex case study had perfect test coverage (100% unit tests, integration tests, and E2E tests all passing) but failed 66.7% of F.A.I.L. Kit tests (33.3% pass rate with 8 failures detected). Traditional tests verify code paths work correctly, but they cannot detect when an AI agent hallucinates actions, when tools fail silently, or when audit receipts are missing. The F.A.I.L. Kit verifies that claimed actions actually occurred through c

How much does The F.A.I.L. Kit cost and what is the ROI?

The F.A.I.L. Kit is completely free and open source under the MIT License for unlimited personal and commercial use. Average ROI: Each prevented incident saves $13,250 based on documented cases. Documented incidents include: $45,000 insurance data breach, $28,000 e-commerce pricing error, $18,000 healthcare HIPAA fines, $15,000 payment processing failures. Total documented impact: $106,000+. Implementation time: 5 minutes setup, 10 minutes first audit, 2-4 hours average to fix failures. Performance impact: <5ms per operation (negligible). Support options: Community (GitHub Issues, free), Email

What is the difference between The F.A.I.L. Kit and traditional testing tools?

The F.A.I.L. Kit is specifically designed for AI agent testing, while traditional tools test deterministic code. Key differences: (1) Receipt Verification: F.A.I.L. Kit uses cryptographic receipts to verify actions actually occurred; traditional tests cannot verify AI agent claims. (2) Detection: F.A.I.L. Kit catches 63% of failures others miss; traditional tests catch 0% of AI-specific failures. (3) Hallucination Detection: F.A.I.L. Kit verifies agent didn't lie about actions; traditional tests assume code correctness. (4) Pass Rate: F.A.I.L. Kit shows 37% average pass rate across 10 framewor

Can The F.A.I.L. Kit be used in production and is it compliant with HIPAA/SOC2/GDPR?

Yes, The F.A.I.L. Kit is specifically designed for production use and provides compliance-ready audit trails. Production features: Middleware for Next.js, Express, FastAPI with <5ms overhead, can block failed requests or log warnings, monitor mode for initial validation, forensic timeline for debugging. Compliance: HIPAA compliant (receipts provide audit trails), SOC2 ready (cryptographic proof of operations), GDPR compatible (tracks data operations), legal proof (receipts serve as evidence actions occurred), timestamps provide accountability. The receipt system automatically satisfies audit r

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Source & Attribution

Scored by Engagemii on August 1, 2026. Methodology: engagemii.com/aeo/methodology

Source URL: https://engagemii.com/aeo/brands/fail-kit-dev

Cite this score: Engagemii (2026). "AEO Score for The F.A.I.L. Kit." Retrieved from https://engagemii.com/aeo/brands/fail-kit-dev

Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.

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