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AI Visibility Scorecard
ximilar.com · Technology
Somewhat visible. AI bots can read Ximilar: Visual AI for Business, but it is missing the structured signals that push citation rate above competitors.
AI engines read this profile 2 times
Claude · Meta AI
#71,126 of 2,655,244 in Technology for AI visibility
6.6
/10
AEO Score
from our crawl and measurement
Ximilar: Visual AI for Business is a business whose own site puts it this way: "Level up your image processing with recognition and search tailored for you. Automate tagging, description, sorting, and searching of images." To AI engines like ChatGPT and Perplexity it is partially visible, scoring 6.6 out of 10, readable in places and missing in others.
Our crawl found structured data on the page (WebPage, ImageObject, BreadcrumbList) and a readable heading structure. It is missing an llms.txt file and a sitemap.
AI crawlers have visited once in our tracking, including ClaudeBot (Anthropic).
Higher is better · 0-10
Structured Data
8
Organization / LocalBusiness JSON-LD that AI can read.
Content Structure
8
Clear headings and answer-style content.
Entity Clarity
10
How clearly your brand identity reads to AI.
E-E-A-T Signals
Experience, Expertise, Authority, Trust
5
Experience, Expertise, Authority, Trust markers.
Technical AEO
6
robots.txt, llms.txt, and AI-bot crawl access.
AI Discoverability
9
Sitemaps and entity links AI can follow.
How the web signals your brand to AI
Backlinks
10
Inbound links from other sites.
Domain Authority
3
Established authority for your domain.
Reference Presence
0
Not in AI knowledge graphs yet.
News & Press
0
No press coverage found yet.
Community
3
Forum and community discussion.
Social Mentions
0
No social discussion found yet.
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 6.6/10, Ximilar: Visual AI for Business has a working base to build on - fixable, and the signals above are where to start.
How does image recognition work?
Image recognition uses convolutional neural networks to extract features from images and map them to categories, attributes, or numerical values. These mappings are learned from labelled training examples. Related tasks such as OCR use similar mechanisms to extract text, while localisation models identify and classify multiple regions within a single image in a single inference pass. We provide a number of off-the-shelf solutions for classifying specific image data, such as stock photos, home decor and furniture images, fashion photos
What is Visual Search?
Visual & similarity search technology can analyze the overall visual aesthetic of an image or detected object in an image, independent of the origin of images or metadata (such as keywords). It understands the concept of similarity according to your subjective perception. That is why it can provide the most relevant results to image queries, whether you look for the exact match or similar items.
Which collectibles can AI Recognition of Collectibles recognize?
As for now, the service is able to detect (and mark by bounding boxes) the collectibles such as stamps, coins, banknotes, comic books and trading cards, as well as antique items. For collectible cards, the service can identify whether it is a Trading Card Game (Pokémon, Magic The Gathering – MTG, Yu-Gi-Oh!, Lorcana, Flesh and Blood and so on) or a Sports Card (Baseball, Basketball, Hockey, Football, Soccer, or MMA), with several additional features (e.g., signature). It can be easily customized to evaluate images
How Ximilar streamlines image processing tasks and reduces costs?
Ximilar’s systems significantly reduce image processing costs by automating repetitive tasks such as analyzing, tagging and sorting of images. This automation results in significant long-term savings, allowing for continuous 24/7 addition of new visual content without additional metadata. We’re continually enhancing our platform, which enables us to both build services efficiently and quickly and also to modify existing solutions to suit your needs. We reduce costs and labour by utilizing a combination of pre-trained and new mo
What are the typical Visual Search applications?
Visual search typically involves searching images or products using an image query, including photos from social media and user-generated content, like smartphone photos. Our Search by Photo combines this technology with detecting individual products in images. One such solution is Search Fashion by Photo. The technology is frequently employed for similarity searches, particularly for product recommendations in e-commerce. It assesses image or detected object features, like colour, edges, or patterns, to suggest the most similar alter
Is Ximilar's visual AI able to handle large datasets and complex images?
Ximilar’s AI solutions were built to handle large datasets containing millions of images. Our cloud doesn’t physically store your image collections for visual search. We process each image once during synchronization, extract descriptive data, and then immediately discard it, allowing for swift image processing. For instance, our largest collections, holding over 100 million images, require only a few hundred milliseconds to search for visually similar items. Complex images containing more objects, patterns, topics, people or items, can be easily
How does Ximilar approach privacy and data protection issues?
Ximilar prioritizes data security and confidentiality. Our customers particularly value the following rules: We don’t store user images, except for mutually agreed training datasets, secured on Amazon S3 with time-limited links. The intellectual rights to generated image models are shared between you and Ximilar, and we are not authorized to use the models for other customers or internal purposes. We adhere to European regulations, including GDPR, and our data center in Prague is multi ISO-certified. We can sign NDAs and customize image access rest
Which types of images and formats does Ximilar support?
You can send images in these supported formats to our API (in fields of _base64 or _url): jpg, jpeg, png, webp, heic, bmp, tiff, and jfif. If you upload a gif, only the first frame will be processed. Contact us for customization.
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Scored by Engagemii on August 1, 2026. Methodology: engagemii.com/aeo/methodology
Source URL: https://engagemii.com/aeo/brands/ximilar
Cite this score: Engagemii (2026). "AEO Score for Ximilar: Visual AI for Business." Retrieved from https://engagemii.com/aeo/brands/ximilar
Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.
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