— Surface 04 · The moat Gemini cannot build alone

Two graphs.
One unfair advantage.

Generic LLMs hallucinate the grocery aisle. Delectable's two graphs are the deterministic layer Gemini calls before it generates — making every Gemini response grounded, fast, and 14× more cost-efficient per call. That efficiency is what gets the feature shipped — and what unlocks the 5–10× call-volume Vertex AI wouldn't have otherwise. Five years of food science, food intelligence, and household behavior modeling, poured into two structures Gemini Pro consumes as context.

— Engine 01 · Computational gastronomy

Food HyperGraphTM

142k
Ingredients mapped

Every SKU on a grocer's shelf, mapped across ingredient × flavor × nutrition × dietary × cultural context. Gemini queries this graph BEFORE generating an answer — eliminating SKU hallucinations entirely.

ingredient_lineage flavor_compounds glycemic_index ferment_markers processing_tier allergen_top9 dietary_9vec cultural_dna
"Substitute the gochujang for ssamjang if budget < $4 — keep the umami profile." — Gemini cannot reason this without grounding.
— Engine 02 · Household-level memory

Shopper HyperGraphTM

847
Signals per household

Dynamic household-level memory fed by the grocer's loyalty data, mParticle, and proprietary signals. Joins to BigQuery as a federated source — Gemini sees the household, not the segment. 94% prediction accuracy on recurring-basket items.

mission_propensity budget_band brand_loyalty dietary_household cultural_dma life_stage occasion_propensity
"This household buys gluten-free on Wednesdays for the daughter, but the adults eat regular." Not segment-of-one theatre — actual household.
— Why Gemini wins grocery when Delectable answers first

The 80/20 split that protects Gemini's margin.

Without the HyperGraphs

Gemini Pro · Generic LLM

  • 1. Shopper: "Friday family dinner, gluten-free, $80 budget."
  • 2. Gemini tokenizes the full grocer catalog (~70k SKUs).
  • 3. Reasoning on ~4M tokens. Cost / call: $0.033.
  • 4. Response in 4.0s. Some SKUs hallucinated. Gluten-free flagged wrong on 2.
  • 5. Grocer abandons rollout. Vertex consumption flat.
With the HyperGraphs

Delectable + Gemini · Grounded

  • 1. Same prompt: "Friday family dinner, gluten-free, $80 budget."
  • 2. Food + Shopper HyperGraphs pre-resolve dietary, household, budget filters.
  • 3. Gemini reasons only the qualified subset. Cost / call: $0.0024.
  • 4. Response in 490ms. 16 items. Pre-substituted. Under budget by $4.20.
  • 5. +18% basket lift. 3× conversion. CUDs activated.
Speed
8×
490ms vs. 4.0s. Delectable resolves the deterministic 80% before Gemini sees the prompt.
Per-call efficiency
14×
$0.0024 vs. $0.033 raw. Same Gemini Pro model · same margin per token. The efficiency that flips this from shelved to shipped — multiplying volume, not discounting Google.
Grounding
5.25×
2.94M data points vs. 560k baseline. Zero SKU hallucinations.
— Why this matters for Google

Gemini doesn't lose volume. It gains win rate.

The 80/20 split looks like Gemini loses 80% of calls. The opposite is true: Gemini gains the 80% that only ship because they're now fast and accurate enough to ship. Without grounding, the agentic basket feature gets cut in Q3 product review. With grounding, the feature ships and Gemini gets called 1.2M times a month per grocer.

01
— Volume

1.2M Gemini calls/mo

Per Tier-2 grocer. Volume Gemini does not get if the agentic basket feature gets killed in Q3 review. Volume goes up linearly with grocers landed.

02
— Net consumption

+$1.6M / yr per Tier-1 grocer

The Tier-1 grocer's net Vertex AI + BigQuery + Cloud Run ARR — that wouldn't exist if the feature got killed at $0.033/call & 4s latency. Same Gemini Pro model, same margin per token, dramatically more calls landed. Net aggregate consumption goes up, not down.

03
— Win rate

vs. Claude, vs. GPT-4o

Grocer evaluates Gemini against Claude / GPT. Without grounding, Gemini ties or loses on accuracy. With Delectable grounding, Gemini wins — because the food + household context is uniquely Vertex-resident.

The graphs are 5+ years in the making.

Food HyperGraph: built from ingredient databases (USDA, FoodData Central), proprietary food-science partnerships, and 142k canonical ingredient mappings refined by working food scientists. Patent-pending. Shopper HyperGraph: built on loyalty integration with Eagle Eye, Inmar, Stuzo, and mParticle, plus proprietary household-modeling work calibrated against 250k+ households of behavior.

The five-year head start is the moat. Gemini is the activation layer. Delectable + Google = grocer's CMO buys both because she's buying one thing.