Store profile, audits, signals, and learning runs

Simple terms

Store profile is Apex's memory for the brand, technical setup, evidence, and learnings for a shop.

The persistent memory of a shop: brand facts, technical findings, product-data depth, storefront evidence, customer signals, audit findings, and operator learnings.

Routes

• /store-profile
• /signals

Implementation behavior

• Audit runs collect evidence and findings about storefront quality, tracking setup, product data, and technical readiness.
• Brand and technical profiles store normalized facts that Operator can reuse across ideas and builds.
• Signals capture behavior, reviewed findings, semantic sequences, and store facts that help audiences and personalization.
• Learning runs summarize completed work into reusable context for future experiment planning.

How to use it

• Open Store Profile before asking Operator to build major variants.
• Review and approve findings so only trusted evidence influences later work.
• Use Signals for reusable behavioral or store facts.
• Refresh audits after major store, theme, product catalog, or tracking changes.

When not to use it

• Do not use stale profile facts after a major theme, app, product, or tracking change.
• Do not let unreviewed audit findings drive important decisions.
• Do not use signal-based personalization until the underlying signals are trustworthy.

Implementation source

• Pages live under src/app/(dashboard)/store-profile and src/app/(dashboard)/signals.
• Handlers live under /api/store-profile, /api/shop-audits, /api/signals, and semantic analytics endpoints.
• Profile, evidence, finding, fact, and learning-run models are defined in prisma/schema.prisma.

Data and API

• Models include ShopAuditRun, ShopAuditEvidence, ShopAuditFinding, ShopBrandProfile, ShopTechnicalProfile, ShopLearningRun, ShopStoreFact, and signal records.
• APIs under /api/store-profile/*, /api/signals, /api/analytics/semantic/*, and audit review endpoints.
• Evidence can include screenshots, detected pages, product data, and runtime checks.

Failure modes

• Operator lacks context: profile is empty or findings are unreviewed.
• Old technical advice: rerun audit after a theme, app, or tracking change.
• Signal-based targeting is weak until enough events and facts exist.