# Implementation Roadmap — Blivakker AI Shopping Readiness

Generated from the Phase 22 audit (board-pack report design). Score: **89.22 / B**.

## Roadmap by horizon

### 0-2w

- **Low content-to-boilerplate ratio**
  - Why it matters: If this stays unfixed, a crawler may spend its token budget on navigation, banners, scripts, and repeated layout text instead of the actual merchant/category facts. The practical result is weaker summaries, missed product-discovery context, and a higher chance that an AI assistant treats the page as thin or unhelpful compared with competitors that expose concise crawlable copy.
  - Owner: dev/seo
  - Fix: Move important answers into crawlable text and reduce boilerplate.
  - Verification: Re-run the audit for the same target and confirm this finding is absent or lower severity.

### 2-6w

- **No agent-native cart or checkout integration verified**
  - Why it matters: If this stays unfixed, an AI assistant can point a shopper at a product page but cannot safely create or verify the exact cart, variant, price, stock state, delivery terms, or checkout handoff. That leaves the highest-value step—moving from recommendation to purchase—manual, fragile, and hard to measure.
  - Owner: ecommerce/dev
  - Fix: After feed/schema readiness, evaluate cart deep links or Storefront/MCP/UCP/ACP integration for agent-assisted checkout.
  - Verification: Re-run the audit for the same target and confirm this finding is absent or lower severity.

### 6-12w

- **Publish an evidence-backed /llms.txt guidance file**
  - Why it matters: If this stays unfixed, agents have no compact, owner-authored map to canonical product, policy, and support URLs. They must infer priorities from generic HTML/navigation, which increases the chance of stale policy answers, missed catalog entry points, or use of non-canonical pages. It still is not a ranking guarantee, but it removes avoidable ambiguity.
  - Owner: content/seo/dev
  - Fix: Create /llms.txt with brand summary, category map, priority collections/products, shipping/returns facts, canonical links, explicit agent policy, token-budget guidance, and freshness/source-of-truth notes.
  - Verification: curl -fsSL https://example.com/llms.txt and confirm it returns HTTP 200 text with canonical links and policy facts.
- **Unclear H1 structure**
  - Why it matters: If this stays unfixed, agents and search systems can infer the wrong page purpose because the primary heading does not clearly state what the page is about. That makes category/brand/entity classification less reliable and can push the assistant to rely on titles, menus, or snippets instead of an explicit on-page semantic anchor.
  - Owner: dev/seo
  - Fix: Use one descriptive H1 and hierarchical H2/H3 sections.
  - Verification: Re-run the audit for the same target and confirm this finding is absent or lower severity.
- **OpenAI visibility check completed (live model run)**
  - Why it matters: If this stays unfixed, the report finding remains a practical blocker: A live OpenAI model run answered 2 of 5 visibility prompt(s) about this site, and 6 cited URL(s) were recorded from the transcripts. The remaining 3 prompt(s) timed out or errored and are recorded as such in the evidence. The likely client impact is continued informational-impact risk until the recommended fix is implemented and retested.
  - Owner: dev/seo
  - Fix: Nothing to fix here — this entry documents how the visibility evidence was collected. Read it as a one-time spot check of live OpenAI answers (responses vary between runs), not as a ChatGPT ranking or share-of-voice measurement.
  - Verification: Re-run the audit for the same target and confirm this finding is absent or lower severity.

See `remediation-pack/implementation-tickets.md` for ticket-level acceptance checks and `value-pack/` for governance/feed/policy artifacts.
