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RETTA: Verified Review & AI Trust Profile

RETTA: 20 anos de experiência em desenvolvimento de software, outsourcing de TI e inteligência artificial. Soluções personalizadas para empresas em Lajeado/RS e todo Brasil.

LLM Visibility Tester

Check if AI models can see, understand, and recommend your website before competitors own the answers.

Check Your Website's AI Visibility
52%
Trust Score
C
43
Checks Passed
3/4
LLM Visible

Trust Score — Breakdown

65%
LLM Visibility
5/7 passed
100%
Content
2/2 passed
54%
Crawlability and Accessibility
6/10 passed
40%
Content Quality and Structure
11/16 passed
100%
Security and Trust Signals
2/2 passed
0%
Structured Data Recommendations
0/1 passed
100%
Performance and User Experience
2/2 passed
100%
Technical
1/1 passed
27%
GEO
6/8 passed
47%
Readability Analysis
8/17 passed
Verified
43/66
3/4
View verification details

RETTA Conversations, Questions and Answers

2 questions and answers about RETTA

Q

What are the different models of IT outsourcing for development teams?

The primary models for IT outsourcing of development teams are dedicated team and staff augmentation. In the dedicated team model, a company hires an external team that works exclusively on their project, often functioning as a remote extension of their in-house department with full project management responsibility. This model is ideal for long-term, complex projects requiring deep domain knowledge. The staff augmentation model involves hiring individual external developers, analysts, or testers to fill specific skill gaps or add temporary capacity to an existing in-house team. These professionals can work full-time or part-time and are managed internally. This model offers high flexibility and is cost-effective for short-term needs or when specialized, niche expertise is required without the commitment of a full team. The choice depends on project scope, duration, budget, and the level of control a company wishes to retain.

Q

How to maintain and modernize legacy software systems?

Maintaining and modernizing legacy software systems involves a structured approach to ensure continuous operation while planning for technological updates. The first step is regular maintenance, which includes fixing bugs, applying security patches, and ensuring compatibility with evolving operating systems and hardware. To modernize, a common strategy is refactoring, which improves the internal code structure without changing its external behavior, making it more maintainable. Another approach is re-engineering, where core parts of the system are redesigned and rebuilt using modern technologies and architectures, such as migrating to cloud-based services or microservices. A complete rewrite or replacement is considered when the existing system is too obsolete, costly to maintain, or no longer meets business needs. Throughout this process, it's critical to ensure data integrity, minimize business disruption, and align the modernization efforts with long-term strategic goals.

Trusted By

AfubraAfubraKey client
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OliversOlivers
PompermaierPompermaier
SindiTabacoSindiTabaco

Services

Custom Software Solutions

Custom Software Development

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AI Trust Verification

AI Trust Verification Report

Public validation record for RETTA — Evidence of machine-readability across 66 technical checks and 4 LLM visibility validations.

Evidence & Links

Scan Facts
Last Scan:Apr 19, 2026
Methodology:v2.2
Categories:66 checks
What We Tested
  • Crawlability & Accessibility
  • Structured Data & Entities
  • Content Quality Signals
  • Security & Trust Indicators

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.

Perplexity
Perplexity
Detected

Detected

ChatGPT
ChatGPT
Detected

Detected

Gemini
Gemini
Detected

Detected

Grok
Grok
Partial

Improve Grok visibility by maintaining consistent brand facts and strong entity signals (About page, Organization schema, sameAs links). Keep key pages fast, crawlable, and direct in their answers. Regularly update important pages so AI systems have fresh, reliable information to cite.

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

12

Fetchable pages, indexable content, robots.txt compliance, crawler access for GPTBot, OAI-SearchBot, Google-Extended

Structured Data & Entity Clarity

11

Schema.org markup, JSON-LD validity, Organization/Product entity resolution, knowledge panel alignment

Content Quality & Structure

10

Answerable content structure, factual consistency, semantic HTML, E-E-A-T signals, citation-worthy data presence

Security & Trust Signals

8

HTTPS enforcement, secure headers, privacy policy presence, author verification, transparency disclosures

Performance & UX

9

Core Web Vitals, mobile rendering, JavaScript dependency minimal, reliable uptime signals

Readability Analysis

7

Clear nomenclature matching user intent, disambiguation from similar brands, consistent naming across pages

23 AI Visibility Opportunities Detected

These technical gaps effectively "hide" RETTA from modern search engines and AI agents.

Top 3 Blockers

  • !
    Is sitemap.xml exists?
    Maintain a sitemap.xml that includes your important canonical URLs and keeps last-modified dates accurate when content changes. Submit it in Search Console and ensure it is accessible to crawlers. A sitemap improves discovery of deeper pages and helps systems prioritize fresh, updated content.
  • !
    Structured data schema present
    Implement 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.
  • !
    Sufficient body content present
    Avoid thin pages by providing enough useful main content to answer the topic properly. Add details such as steps, examples, FAQs, screenshots, definitions, and supporting links. Depth improves ranking stability and increases the chance that AI assistants can cite your page confidently.

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.
  • !
    List in Grok
    Improve Grok visibility by maintaining consistent brand facts and strong entity signals (About page, Organization schema, sameAs links). Keep key pages fast, crawlable, and direct in their answers. Regularly update important pages so AI systems have fresh, reliable information to cite.
  • !
    LLM-crawlable llms.txt
    Create 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.
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Embed Badge

Verified

Display this AI Trust indicator on your website. Links back to this public verification URL.

<a href="https://bilarna.com/provider/retta" target="_blank" rel="nofollow noopener noreferrer" class="bilarna-trust-badge"> <img src="https://bilarna.com/badges/ai-trust-retta.svg" alt="AI Trust Verified by Bilarna (43/66 checks)" width="200" height="60" loading="lazy"> </a>

Cite This Report

APA / MLA

Paste-ready citation for articles, security pages, or compliance documentation.

Bilarna. "RETTA AI Trust & LLM Visibility Report." Bilarna AI Trust Index, Apr 19, 2026. https://bilarna.com/provider/retta

What 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 RETTA measure?

It summarizes crawlability, clarity, structured signals, and trust indicators that influence whether AI systems can reliably interpret and reference RETTA. 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 RETTA?

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 RETTA for relevant queries.

How often is this report updated?

We rescan periodically and show the last updated date (currently Apr 19, 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?

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?

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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