Google rolled out six conversational attributes for AI-ready product feeds in 2026 — they are the minimum a grocer needs to be discoverable on AI Mode in Search. Delectable's PIM + IA modules deliver all six, plus thirty-six more, in the schema Vertex Retail Search and Gemini agents need. Every Tier-2 grocer with a Merchant Center feed needs this work done. Today, it doesn't get done.
The same enriched catalog feeds three Google products. Each one wins when the data is richer. Delectable's PIM + IA pipeline is the one place a grocer pays for the work — and three Google teams collect the dividend.
The six new attributes (question_and_answer, related_product, item_group_title, variant_option, document_link, popularity_rank) are how Google's AI Mode parses product nuance. Delectable populates all six from grocer-specific food intelligence, not generic LLM guesses.
Vertex Retail Search is already live in grocer projects (we run it ourselves — asset inventory shows retail.googleapis.com/Catalog: default_catalog). The deeper the attribute graph, the higher the relevance — Delectable's 42-dim enrichment is the substrate Vertex's ranker needs to outperform Algolia / Constructor / Bloomreach.
CPG retail-media dollars flow to whoever has the richest attribute graph — that's why Amazon Ads dominates today. Delectable enrichment makes a grocer's catalog targetable on dietary, lifestyle, occasion, cultural relevance. Google Ad Manager wins back the attribution loop. $69B TAM.
The Giant Eagle case study, walked end-to-end. Here we go past the headline numbers into the shape of the enrichment — the dimensional structure that makes a Gemini agent actually able to answer "is this gluten-free, kosher, and under $4?" without hallucinating.
Below is the official Google spec (six rows) mapped to Delectable's enrichment outputs. Every row is automated from the PIM ingestion pipeline + IA food/health/cultural ontologies — no manual taxonomy work on the grocer's side.
| Google attribute | What Google asks for | How Delectable derives it | Auto |
|---|---|---|---|
| question_and_answer FAQ pairs for AI Mode |
Product-specific Q&A pairs that conversational agents can quote verbatim. | IA module generates 8–15 Q&A per SKU from the Food HyperGraph (dietary, allergen, prep, storage, substitution). LLM rewrite at end-of-pipeline for tone. | ✓ |
| related_product Alternatives, accessories, required parts |
Substitution and complement graph keyed by GTIN / SKU / MPN. | PIM relationship engine: dietary substitutions, recipe complements, basket co-occurrence from BigQuery purchase data — all materialized to Google's relationship_type:id schema. | ✓ |
| item_group_title Human-readable variant family |
Parent name for a group of variants (e.g. flavor / size). | PIM canonicalization: variant rollup keyed on shared ingredient + brand. Auto-generated title from product-line vocabulary. | ✓ |
| variant_option name/value variant axes |
Structured variant axes — flavor, size, pack count, organic certification. | IA dimensional axes inferred from product titles + descriptions. 11 standard variant axes for grocery (flavor, size, count, organic, dietary, regional, …). | ✓ |
| document_link Manuals, prep guides, ingredient PDFs |
URLs to supplementary PDFs — manuals, allergen statements, cooking guides. | For grocery: nutrition labels (FDA-mandated), allergen sheets, prep videos. Delectable hosts in GCS, generates Merchant-compatible URLs automatically. | ✓ |
| popularity_rank % ranking within inventory |
Percentage-based performance metric relative to inventory. | Computed in BigQuery from loyalty purchase data (Eagle Eye / Inmar / Stuzo) joined to Shopper HyperGraph. Refreshed nightly. Decay-weighted, store-localized. | ✓ |
Google's six attributes are the bare minimum for AI Mode discovery. To run an agentic basket — "Friday family dinner, gluten-free, $80 budget" — Gemini needs the deeper dimensional structure of the IA module. Forty-two attributes per SKU is the working number for grounded reasoning. Below: where the other 36 come from.
Ingredient lineage, flavor compounds, glycemic index, ferment markers, processing tier (UPF / minimally-processed / whole-food). From the Food HyperGraph.
Gluten-free, vegan, ketogenic, low-FODMAP, kosher (parve / dairy / meat), halal, allergen avoidance graph (top-9 + sesame), pregnancy-safe, child-suitable.
Regional cuisine relevance, holiday baskets (Diwali, Lunar New Year, Eid, Easter, Passover), meal occasion (breakfast / weeknight / weekend / picnic), gifting suitability.
Weekly-stockup / quick-trip / fill-in / specialty / gift mission propensity. Computed from BigQuery transaction patterns joined to Shopper HyperGraph.
Fair-trade certifications, regenerative-ag tier, packaging-recyclability score. Increasingly demanded by Gen-Z grocers and a Google ESG narrative lever.
The grocer's loyalty program tier, private-label hierarchy, store-specific availability, cultural-DMA targeting (H Mart, 99 Ranch, Sigona's, Lulu, etc.). Configured, not coded.
Same architecture Giant Eagle uses today. Listed on Cloud Marketplace, IAM-integrated, consumed on the grocer's existing Google contract.
enrichment_warehouse dataset. 70k SKUs in ~6 hours.
retail_analytics + Vertex Retail Search catalog +
Merchant Center conversational-attributes feed (GCS bucket the bundle provisions).
Diff-mode: only changed rows write.
Highlighted stages are Delectable IP — the moat Gemini cannot build alone. The rest is Google-native plumbing the grocer's existing team can read.
↪ Full integration map: The complete topology — every grocer-system connector, every protocol, every API surface — is walked through in the Delectable integration flows interactive. Includes Azure / AWS / Databricks bridges for grocers whose existing data stack isn't on GCP.
The point where Delectable's enrichment lands inside Google's existing systems. Same pipeline, three Google consumers downstream.
Salsify / Akeneo / SAP. 70k SKUs, 8 attrs each. Bare-minimum feed today.
4-week sprint. 8 → 42 attrs. Food + Shopper HyperGraphs applied. Runs on Vertex + BigQuery.
Merchant Center (AI Mode) · Vertex Retail Search (grocer.com) · Google Ad Manager (retail media).
The fastest path to Vertex AI consumption inside a grocer's GCP project isn't a custom build. It's a packaged enrichment workstream the FSR can quote next quarter.
Grocer hires a data-engineering team. Builds a food ontology from scratch (failing once first). Negotiates with Salsify / Akeneo. Trains an LLM. Validates with food scientists. Q3 2027 if nothing slips. Most Tier-2 grocers never finish.
Phase 0 SOW signed Monday. Catalog ingestion live by Friday. 42-attr enrichment running on the grocer's GCP project by Week 4. Merchant Center feed flipping over to conversational attributes by Week 6.
The grocer pays Delectable for the enrichment workstream. Vertex + BigQuery + Gemini consumption lights up on the grocer's existing Google Cloud contract. FSR books the consumption number; Delectable books the workstream margin.
A discovery workshop with Delectable's PIM + IA team, a 4-week Phase 0 enrichment sprint, and a Vertex Retail Search + Merchant Center go-live by end of quarter. Joint case study at Google Cloud Next 2027. Same playbook scales to ten grocers next year.