A common language for geospatial data
STAC (stac.org) scores 51 out of 100 on Brand Analyzer, placing it in the Early Stage tier among Technology brands, Government brands, SaaS brands, in the 2nd percentile of 54999 Technology brands.
STAC (SpatioTemporal Asset Catalog) is an open, community-driven specification for cataloging spatiotemporal data, particularly geospatial and satellite imagery. It standardizes how such data is structured and indexed, enabling interoperability across different tools, platforms, and organizations. STAC is maintained by a community of contributors rather than a single company, and its mission is to make geospatial assets more discoverable and usable through a common, open standard adopted by developers, data providers, and government agencies worldwide.
| Brand Name | STAC |
|---|---|
| Domain | stac.org |
| Industry | Technology, Government |
| Main Competitors | OGC (Open Geospatial Consortium) Catalog Service for the Web, Google Earth Engine, Microsoft Planetary Computer, Esri ArcGIS (93/100), Radiant Earth Foundation, Sentinel Hub (80/100) |
Moz Domain Authority 11/100 vs category average 39 / leader 100 - limited third-party links, so AI systems rarely encounter mentions of the brand.
STAC appears on Wikipedia but without a dedicated company entity, so AI models can confuse it with similarly named subjects.
The homepage meta description reads "To create and promote a standardized, open specification for cataloging spatiotemporal data to make geospatial assets more discoverable and usable." (No meta description) - this is the summary AI engines are most likely to quote.
Structured data on the homepage: No structured data or Open Graph tags detected. Adding Organization and FAQ schema would further help AI crawlers parse the brand's identity.
AI-crawler access: No robots.txt found (default: all bots allowed, but explicit file preferred) - robots.txt controls whether engines like GPTBot and ClaudeBot can read the site at all.
Social footprint: verified profiles on X (Twitter), Facebook, Instagram, YouTube, GitHub; no detected presence on LinkedIn - consistent profiles reinforce the brand's identity across the web.
0 Reddit mentions - community discussion signals real-world reputation to AI models.
Live results from asking a general-purpose AI assistant about the brand, checked November 2026.
When asked "What is STAC?", Claude could identify the brand as of November 2026. STAC (Securities Technology Analysis Center) is an organization that benchmarks and tests the performance of technology systems used in financial trading, particularly for high-speed and high-frequency trading infrastructure. Best known for STAC benchmarks, especially STAC-M3 and STAC-A2, used to measure speed and performance of trading and risk analytics systems.
When asked "Best brands similar to STAC?", Claude would recommend STAC as of November 2026. If someone asked about performance benchmarking standards in financial technology, STAC would be a natural and relevant reference.
Common questions people ask in Google, ChatGPT, Claude, Gemini, Perplexity, and other AI search engines.
STAC's main competitors in Technology: Esri ArcGIS (93/100), Sentinel Hub (80/100).
Sources: Brand Analyzer scan
STAC stands for SpatioTemporal Asset Catalog. It is an open specification designed to standardize how spatiotemporal data-particularly geospatial and satellite imagery-is cataloged and described, making such data easier to discover, share, and use across different systems and organizations.
Sources: STAC official site
STAC has low AI-search visibility, scoring 24/100 on Brand Analyzer's AI visibility composite (visibility 35, trust 7, recommendation 24). This estimates how likely AI engines like ChatGPT, Claude, Gemini and Perplexity are to know, trust, and recommend the brand.
Sources: STAC official site
STAC creates and promotes a standardized, open specification for describing and cataloging spatiotemporal data. It defines a common structure for metadata so that geospatial assets, such as satellite images or earth observation data, can be indexed, searched, and shared consistently across different tools and platforms.
Sources: STAC official site
STAC scores 24/100 on Brand Analyzer's AI recommendation signal, indicating it is less likely to be surfaced when AI assistants like ChatGPT suggest Technology options. Recommendation depends on crawlability, structured data, and category authority; a Wikipedia presence helps.
Sources: STAC official site
STAC is known for being the leading open standard used by the geospatial community to catalog and structure satellite imagery and other spatiotemporal datasets, enabling interoperability between different data providers and software tools.
Sources: STAC official site
STAC can improve AI discoverability by strengthening structured data (Organization and FAQ schema), maintaining an accurate Wikipedia/Wikidata entity, earning authoritative citations, and keeping content crawlable for AI bots. Brand Analyzer measures these as visibility, trust, and recommendation signals.
Sources: STAC official site
STAC offers an open specification (the STAC standard) for structuring and cataloging spatiotemporal data, along with related community-developed tools, APIs, and extensions that support implementation of the standard across geospatial platforms.
Sources: STAC official site
STAC is used by geospatial developers, data scientists, satellite imagery providers, government agencies, and organizations that work with location-based or earth observation data.
Sources: STAC official site
STAC is popular because it provides a standardized, open way to catalog geospatial and spatiotemporal data, solving interoperability challenges between different tools, platforms, and organizations that work with earth observation and location-based data.
Sources: STAC official site
STAC scores 24/100 for AI visibility - how well ChatGPT, Claude, Perplexity and Google AI Overviews can discover, identify and cite the brand.
STAC has a weak AI-visibility profile at 24/100 - a proxy for how readily ChatGPT, Perplexity and Google's AI Overviews can recognise and cite it. Its strongest area is visibility (35/100) and its weakest is trust (7/100). It benefits from a Wikidata knowledge-graph entry and open access for AI crawlers (GPTBot, ClaudeBot, etc.). The main gaps holding it back: no clear Wikipedia entity to anchor it in LLM training data, a low Moz Domain Authority of 11/100 and thin schema.org structured data. In a live check, Claude could already identify STAC from memory (November 2026) - a sign these signals are paying off.
The score combines seven dimensions - name quality, digital presence, visual identity, messaging clarity, trust foundation, AI discoverability, and brand authority - into a single objective benchmark.
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