Platform Overview
Investor Dataroom
Investor Dataroom
LLMs can reason. Retailers have catalogs. Neither can do what we do. Delectable AI is the agentic commerce platform that transforms generic AI into a personalized shopping intelligence — with proprietary food science, behavioral graphs, and deterministic commerce pipelines that no LLM can replicate from pre-training alone.
14 orchestrated tools. 10-stage ranking pipeline. 3 knowledge graphs. One unified API that works across every channel.
Each capability compounds the others. Food intelligence makes ranking smarter. Behavioral graphs make recommendations personal. Commerce activation makes it all shoppable. No single capability is the moat — the integrated system is.
Not a chatbot. A multi-turn reasoning agent that selects from 14 specialized tools, composes them into playbooks, and adapts its strategy based on shopper context.
Every SKU enriched with nutritional science, dietary classification, and health scoring from USDA, Open Food Facts, and FooDB — creating a data asset no LLM has in its weights.
A 10-stage composable pipeline that transforms raw search results into personally-ranked, health-scored, dietarily-annotated product recommendations.
S = S_base + λ_sodium(N) + λ_sugar(N) − λ_protein(N)
Confidence-based annotations that never over-filter. A Glass Box architecture where every recommendation is explainable, auditable, and traceable.
A deterministic Recipe-to-Cart pipeline that converts meal inspiration into shoppable, priced, SKU-matched shopping carts — in under 50ms, with zero ML inference.
Monetization built into the intelligence layer — not bolted on. Sponsored placements, health-validated swaps, and closed-loop attribution that proves ROI to CPG brands.
Every product result passes through ten composable stages — each independently toggleable, auditable, and observable. This is the production pipeline, not a prototype.
Pure vector search can't distinguish household members, understand molecular flavor pairings, or predict what's in someone's pantry. Our graphs can.
Disentangles individual personas within a single loyalty card. Distinguishes the "Athlete" from the "Parent" — Gemini reasons about each independently.
openCypherMaps SKU → Ingredient → Recipe → Flavor Compound. Enables molecular pairing discovery and allergen graph traversal.
2.1M+ nodesPurchase-derived behavioral signals — organic preference, health consciousness, mission clusters — powering personalized ranking and annotation confidence.
12-month signalsThe agent doesn't just know what you like. It knows what you have. An exponential decay model predicts home inventory from purchase history.
stock = quantity × e(-days / shelf_life)
A vegan shopper (100% propensity) searches for cheese. The agent shows vegan options first, then annotates dairy options for household members — never filters.
The same intelligence layer — same loyalty ID, same personalization graph, same ranking pipeline — serves every channel. Each surface gets a lightweight client profile config, not a separate build.
LLMs are commoditizing. The value is in the context engineering, the proprietary data, and the commerce integration that makes AI actually transactable.
The platform is an instrumented measurement layer for agentic commerce. Every interaction generates attribution data that traditional retail media cannot capture.
Infrastructure-layer bet. Picks and shovels for agentic commerce. Every new LLM entrant needs this integration layer, expanding our TAM.
This overview covers the business narrative. For full technical architecture, code-level documentation, and implementation details, visit the documentation site.