AI: Verified Review & AI Trust Profile
Hakuna helps forward-thinking teams harness the power of AI and product innovation to design smarter and more human digital experiences. From early exploration to scalable solutions, we turn complexity into clarity.
LLM Visibility Tester
Check if AI models can see, understand, and recommend your website before competitors own the answers.
Trust Score — Breakdown
AI Conversations, Questions and Answers
3 questions and answers about AI
QWhat is AI feature strategy and design?
What is AI feature strategy and design?
AI feature strategy and design is a structured framework for ideating, validating, and scaling impactful AI-driven features in digital products. This approach ensures that new features align with business goals and user needs through a process that includes thorough user research to identify opportunities, rapid prototyping to test concepts, and iterative design to refine the experience. It focuses on feasibility assessment, ethical AI considerations, and seamless integration with existing systems. By combining strategic planning with user-centric design, it helps companies innovate responsibly, reduce development risks, and deliver features that provide tangible value and enhance overall product effectiveness.
QHow does agentic experience design enhance user interactions?
How does agentic experience design enhance user interactions?
Agentic experience design enhances user interactions by creating intelligent, autonomous systems that anticipate needs and perform tasks proactively, moving beyond traditional click-based interfaces. This design paradigm enables more natural, conversational engagements where AI agents handle complex workflows, reducing user cognitive load and increasing efficiency. Key benefits include personalized experiences that adapt to user behavior, improved accessibility through voice or text-based commands, and the ability to automate routine tasks. Effective agentic design prioritizes user control, transparency in AI decision-making, and ethical considerations to build trust. By focusing on seamless integration and human-centric principles, it transforms interactions into intuitive, supportive partnerships that drive higher satisfaction and productivity.
QWhat should you look for in a design partner for AI projects?
What should you look for in a design partner for AI projects?
When selecting a design partner for AI projects, seek a team with proven expertise in both AI technology and human-centered design principles. Key criteria include a track record of delivering AI-driven experiences, cross-disciplinary skills that blend design, strategy, and development, and an adaptive approach that keeps pace with evolving technologies. Look for experience in AI feature validation, a focus on ethical design practices, and the ability to simplify complex AI concepts into intuitive interfaces. Additionally, assess their collaboration style for alignment with your vision, scalability to support project growth, and commitment to measurable business outcomes. A reliable partner should offer streamlined workflows, foster innovation, and ensure designs are both practical and user-friendly.
Reviews & Testimonials
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Services
AI Design Services
AI Product Design Agency
View details →AI Trust Verification Report
Public validation record for AI — Evidence of machine-readability across 66 technical checks and 4 LLM visibility validations.
Evidence & Links
- Crawlability & Accessibility
- Structured Data & Entities
- Content Quality Signals
- Security & Trust Indicators
Verifiable Identity Links
Third-party Identity
- YouTube
Do These LLMs Know This Website?
LLM "knowledge" is not binary. Some answers come from training data, others from retrieval/browsing, and results vary by prompt, language, and time. Our checks measure whether the model can correctly identify and describe the site for relevant prompts.
| LLM Platform | Recognition Status | Visibility Check |
|---|---|---|
 | Detected | Detected |
 | Detected | Detected |
 | Detected | Detected |
 | Detected | Detected |
Detected
Detected
Detected
Detected
Note: Model outputs can change over time as retrieval systems and model snapshots change. This report captures visibility signals at scan time.
What We Tested (66 Checks)
We evaluate categories that affect whether AI systems can safely fetch, interpret, and reuse information:
Crawlability & Accessibility
12Fetchable pages, indexable content, robots.txt compliance, crawler access for GPTBot, OAI-SearchBot, Google-Extended
Structured Data & Entity Clarity
11Schema.org markup, JSON-LD validity, Organization/Product entity resolution, knowledge panel alignment
Content Quality & Structure
10Answerable content structure, factual consistency, semantic HTML, E-E-A-T signals, citation-worthy data presence
Security & Trust Signals
8HTTPS enforcement, secure headers, privacy policy presence, author verification, transparency disclosures
Performance & UX
9Core Web Vitals, mobile rendering, JavaScript dependency minimal, reliable uptime signals
Readability Analysis
7Clear nomenclature matching user intent, disambiguation from similar brands, consistent naming across pages
15 AI Visibility Opportunities Detected
These technical gaps effectively "hide" AI from modern search engines and AI agents.
Top 3 Blockers
- !JSON-LD Schema: Organization, Product, FAQ, WebsiteAdd schema.org JSON-LD to describe your key entities (Organization, Product/Service, FAQPage, WebSite, Article when relevant). Structured data makes your meaning explicit and improves the chance of rich results and accurate AI citations. Validate markup with schema testing tools and keep the data consistent with the visible page content.
- !Dedicated Pricing/Product schemaUse Product and Offer schema (or a pricing page with structured data) to describe plans, prices, currency, availability, and key features. This reduces ambiguity for both search engines and AI assistants and can unlock richer search snippets. Keep pricing up to date and match schema values to the visible pricing table.
- !Breadcrumbs with structured data (BreadcrumbList)Add visible breadcrumbs for users and BreadcrumbList structured data for crawlers. Breadcrumbs clarify site hierarchy (category > subcategory > page) and help systems understand topical relationships. This can improve search snippets and makes it easier for AI to choose the right page as a source.
Top 3 Quick Wins
- !List in public LLM indexes (e.g., Huggingface database, Poe Profiles)List your tools, datasets, docs, or brand pages on major AI/LLM discovery hubs where relevant (for example model/dataset repositories or app directories). These platforms add credibility signals (likes, forks, usage) and create additional crawlable references to your brand. Keep names, descriptions, and links consistent with your official website.
- !LLM-crawlable llms.txtCreate an llms.txt file to guide AI crawlers to your most important, high-quality pages (docs, pricing, about, key guides). Keep it short, well-structured, and focused on authoritative URLs you want cited. Treat it as a curated “AI sitemap” that improves discovery and reduces the risk of crawlers prioritizing low-value pages.
- !Structured data schema presentImplement structured data wherever it matches the content (FAQPage, HowTo, Product, Organization, Article, BreadcrumbList). Schema gives machines a reliable map of your page and helps them extract facts correctly. Prioritize schema for your most valuable pages first, then expand site-wide after validation.
Claim this profile to instantly generate the code that makes your business machine-readable.
Embed Badge
VerifiedDisplay this AI Trust indicator on your website. Links back to this public verification URL.
<a href="https://bilarna.com/provider/joinhakuna" target="_blank" rel="nofollow noopener noreferrer" class="bilarna-trust-badge">
<img src="https://bilarna.com/badges/ai-trust-joinhakuna.svg"
alt="AI Trust Verified by Bilarna (51/66 checks)"
width="200" height="60" loading="lazy">
</a>Cite This Report
APA / MLAPaste-ready citation for articles, security pages, or compliance documentation.
Bilarna. "AI AI Trust & LLM Visibility Report." Bilarna AI Trust Index, Apr 20, 2026. https://bilarna.com/provider/joinhakunaWhat Verified Means
Verified means Bilarna's automated checks found enough consistent trust and machine-readability signals to treat the website as a dependable source for extraction and referencing. It is not a legal certification or an endorsement; it is a measurable snapshot of public signals at the time of scan.
Frequently Asked Questions
What does the AI Trust score for AI measure?
What does the AI Trust score for AI measure?
It summarizes crawlability, clarity, structured signals, and trust indicators that influence whether AI systems can reliably interpret and reference AI. The score aggregates 66 technical checks across six categories that affect how LLMs and search systems extract and validate information.
Does ChatGPT/Gemini/Perplexity know AI?
Does ChatGPT/Gemini/Perplexity know AI?
Sometimes, but not consistently: models may rely on training data, web retrieval, or both, and results vary by query and time. This report measures observable visibility and correctness signals rather than assuming permanent "knowledge." Our 4 LLM visibility checks confirm whether major platforms can correctly recognize and describe AI for relevant queries.
How often is this report updated?
How often is this report updated?
We rescan periodically and show the last updated date (currently Apr 20, 2026) so teams can validate freshness. Automated scans run bi-weekly, with manual validation of LLM visibility conducted monthly. Significant changes trigger intermediate updates.
Can I embed the AI Trust indicator on my site?
Can I embed the AI Trust indicator on my site?
Yes—use the badge embed code provided in the "Embed Badge" section above; it links back to this public verification URL so others can validate the indicator. The badge displays current verification status and updates automatically when the verification is refreshed.
Is this a certification or endorsement?
Is this a certification or endorsement?
No. It's an evidence-based, repeatable scan of public signals that affect AI and search interpretability. "Verified" status indicates sufficient technical signals for machine readability, not business quality, legal compliance, or product efficacy. It represents a snapshot of technical accessibility at scan time.
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