basemouse.com

Executive summary

Extracted positioning (as AI sees it)

Category scores

Evidence checklist

Fix these first

Sequenced by execution dependency — foundational crawlability first, then structured data, then content assets. Only signals weak or missing on this site are listed.

1. Publish sitemap.xml and reference from robots.txt — A sitemap referenced from robots.txt is foundational crawlability — pages that are not discoverable cannot be read at all.
2. Add llms.txt — A published /llms.txt gives AI crawlers a curated profile to read instead of inferring positioning from raw HTML.
3. Add Organization / SoftwareApplication JSON-LD — Structured data lets answer engines classify the product and company from explicit fields rather than guessing from prose.
4. Declare official profiles with sameAs — sameAs entity links let AI systems verify the company across independent profiles instead of treating it as an unverified entity.
5. Add FAQ content — FAQ content supplies the direct question-and-answer text that answer engines prefer to quote.

Prioritized recommendations

Beyond your website: getting cited, not just crawled

On-site fixes make your site legible to AI engines, but engines recommend companies they can find in search indexes and verify across independent sources. Complete the path:

1. Register with the indexes AI engines read. ChatGPT search reads Bing's index; Gemini and AI Overviews read Google's — an unindexed site cannot be cited.
   • Google Search Console (search.google.com/search-console): add a Domain property, verify with the DNS TXT record, submit your sitemap.xml, then URL Inspection → Request Indexing on your top pages.
   • Bing Webmaster Tools (bing.com/webmasters): use "Import from Google Search Console", or verify the same way.
   • First indexing of a new domain takes days to weeks — start before any content work.
2. Enable IndexNow — a free protocol that pushes changed URLs to Bing, Seznam, Naver, and Yandex in hours instead of weeks (Google doesn't participate; Search Console covers it).
   • Generate a key: any 8–128 character hex string (e.g. openssl rand -hex 16, or the generator at indexnow.org).
   • Publish a plain-text file at https://yourdomain.com/<key>.txt containing just the key — this proves domain ownership.
   • On every publish, POST your changed URLs with the key to api.indexnow.org/indexnow; a 200/202 means accepted. Add the call to your deploy pipeline so it never gets forgotten.
3. Build the entity triangle. Engines recommend companies they can verify as real, single entities by cross-checking independent profiles.
   • Create or update a LinkedIn company page, GitHub organization, and Crunchbase profile — exact same company name, one-line description, and website URL on all three.
   • Every profile links back to your site; your homepage declares the profiles via sameAs in Organization JSON-LD:

   { "@context": "https://schema.org", "@type": "Organization",
     "name": "Your Company", "url": "https://yourdomain.com",
     "sameAs": ["https://www.linkedin.com/company/yourcompany",
                "https://github.com/yourcompany",
                "https://www.crunchbase.com/organization/yourcompany"] }

   • List only profiles that exist and are yours, then validate at validator.schema.org. The "Entity links (sameAs)" check above looks for exactly this.
4. Get cited where AI already looks. AI "best tools" answers lean on a small set of roundup articles — getting into them beats any volume of social posts.
   • Ask ChatGPT, Perplexity, and Gemini "best [your category] tools" and note which articles they cite — those pages decide AI recommendations in your category.
   • Pitch those authors a 3-line email: what your product does, pricing, the one differentiator.
   • Publish definitional pages for buyer questions ("what is [category]", "[competitor] alternatives") — direct answer in the first paragraph, plain language, FAQ schema.
5. Baseline and track. Fix a set of 5–10 buyer questions, ask ChatGPT/Perplexity/Gemini monthly, record whether and how you appear, screenshot your first appearance, and re-run this audit after major site changes.

Generated llms.txt draft

# BaseMouse

> Context infrastructure for AI agents: import your docs, pull cited context packs over one endpoint, and maintain an append-only history of everything your agents were told.

## Primary URL
https://basemouse.com

## About
BaseMouse is context infrastructure designed for AI agents. It allows development teams to import a folder of Markdown documents, retrieve cited and checksummed context packs through a single API endpoint, and preserve an append-only audit history of every piece of context an agent received. The platform is available as a hosted service and as an open-source core that teams can run themselves.

## Products & offering
BaseMouse provides a Context Store (rev 0.3) that handles corpus ingestion, grounded context retrieval, and agent governance. Developers interact through a single API call that returns context packs with full provenance. The platform includes a governance demo, a design-partner intake flow, API access with a published rate card, open-source self-hosting, and documentation.

## Who it's for
BaseMouse is built for engineering teams developing AI agents who need reliable, auditable context grounding rather than ad-hoc prompt stuffing. It is particularly relevant to teams that require provenance tracking, checksummed retrieval, and a verifiable record of what their agents were told during operation.

## Key pages
- Home: https://basemouse.com
- Pricing / Rate Card: https://basemouse.com/pricing
- Documentation: https://basemouse.com/docs
- Open Source: https://basemouse.com/open-source
- API: https://basemouse.com/api
- Governance Demo: https://basemouse.com/demo
- Design Partner Intake: https://basemouse.com/design-partner

## Contact
https://basemouse.com