Honeybound audits and fixes the product data, structured data, crawler policy, AI discovery files, and attribution gaps that stop AI assistants, search engines, and shopping agents from understanding your store.
Most stores do not need vague AI strategy. They need clean machine-readable catalog signals, intentional crawler policy, useful AI discovery files, and attribution that can tell whether new discovery surfaces are producing buyers.
Assistants can find the site, but not the canonical products, policy pages, or answers you want cited.
Variant state, availability, pricing, reviews, and policy links are scattered across page markup instead of cleanly expressed.
Traffic from assistants may arrive as direct, referral, or generic search traffic, making revenue impact hard to defend.
Contracts, billing cycles, dunning, state machines.
Read case study →Server-side tracking for Shopify with dedup, retry, DLQ, 8+ destinations.
Read case study →Meilisearch-backed collection filtering on entire catalogs.
Read case study →AI assistants, shopping agents, and new search surfaces reward stores that are crawlable, machine-readable, and answer-ready. The free AI Commerce Readiness Audit tells you exactly where yours stands — robots.txt, llms.txt, agents.md, product JSON-LD, sitemap, policies, and attribution — no install, no account.
