Single source of truth for Delectable AI’s product demos, architecture deep‑dives, and technical thought‑leadership content. The platform composes the way a real one does — a foundation of data, knowledge, governance and identity; an intelligence layer of models and pipelines; composable modules; and the agentic experiences shoppers touch. Pick a layer to explore its assets.
Top-down: what the world touches sits on progressively deeper substrates. Each layer is independently useful and compounds the value of the layers below it. Click a layer to jump to its assets.
Building diligence? The Evidence tab collects the architecture deep-dives and the Challenge thought-leadership series that explain why the stack is defensible.
Everything above assumes this layer. The canonical Food & Shopper Knowledge Graph and AI-ready catalog data give the platform something true to reason over; governance, identity, integration and multi-tenant config make it safe and deployable per tenant.
The HyperGraph — products linked to ingredients, nutrition, flavor, cuisine, dietary tags and recipe primitives. The moat under everything.
delectable-knowledge-graph.htmlDrill from any node — shopper, product, recipe, ingredient, dietary, flavor — into its semantic neighborhood. Inspect edge metadata and see the equivalent Cypher firing against Memgraph.
graph-explorer.htmlHow AXP emits structured conversational attributes (MerchantListing JSON-LD, Q&A chunks, variant matrices) so ChatGPT, Perplexity, Gemini & Claude cite retailer SKUs. Up to +40% citation lift (Princeton/GT KDD 2024). Includes live SKU explorer.
ai-ready-catalog.htmlThe data journey: heterogeneous sources (transactions, catalog, recipes, social, nutrition) โ ingest โ food-science enrichment โ the Knowledge Graph, embedding space & feature store the agent reads at runtime. Bridges Foundation โ Intelligence.
data-foundation.htmlThe four-pillar substrate that earns trust by construction: hard rules (allergen, dietary, recall), regulatory compliance (FDA ยท FTC ยท state, pre-validated at emit), audit trail (every decision logged, traceable in <5s), and model provenance (constrained to the tenant's verified live catalog — never invented).
ai-governance.htmlDetailed visual evidence of CCPA/GDPR/CPRA, HIPAA (Zero-Knowledge, clinical shields), AI Governance (hallucination checks, Zero Data Retention proxies), and Infosec audits (SOC 2, CMEK, TLS 1.3, SLAs) with active DLP edge sandbox.
compliance-evidence.htmlHow retailer data, agent calls, and module surfaces tie together end-to-end.
delectable-integration-flows.htmlDelectable + Instacart integration topology — how a partner fulfillment + catalog network plugs into the agent.
instacart-architecture.htmlThe multi-tenant backbone behind every console: per-tenant branding, content, feature flags and entitlement. Resolves through infrastructure/customers/<slug>.json.
The trust spine: organizations entitle modules (via_tenant_features), roles grant read/write/admin per module, and every grant is capped by the tenant's entitlement. Interactive role ร module permission matrix.
The security traffic cop: unifies enterprise federated SSO (OIDC) and cryptographic Web3 wallets (SIWE) into a uniform, signed JWT header context. Zero database cold starts.
governance-gateway.htmlEvery URL, secret, model and feature flag resolves through global โ customer โ env โ module โ secret store โ process env. Pick a key and watch the precedence ladder resolve and the winning layer glow. Zero hardcoded values.
config-not-code.htmlfully-interactive Sandbox proving zero-cold-start storefront personalization, cardiologist prescription compliance, and active cryptographic shredding of envelope-encrypted CAS pointers for immediate GDPR Right-to-Erasure.
did-explorer.htmlThe data-science layer that turns the foundation into behavior: propensity and household models, the embedding space, the grounding/routing/evaluation pipeline, and the agent architecture that ties them together.
The full intelligence engine in the Platform-chart visual language: model portfolio (graph ML · embeddings · BQML · optimizers · grounded generation), governed runtime (control-plane PEP), data substrate & learning loop. Composed score functions — the LLM is the last-mile narrator. Expandable layers · Now/Next/Vision. Print/PDF companion in doc/.
Experience how 7+ core models (Bayesian pantry decay, propensity clickstream, GNN householding, IPL optimization, dynamic coupons, last-mile auction) assemble in real time to generate the perfect basket, side-by-side with a legacy search-based cart.
perfect-cart.html8 interactive widgets demonstrating the ML models under the hood: Real-time propensity, GNN householding, orchestrator routing, pantry forecasting, agentic testing, and more.
ai-capabilities-deep-dive.htmlInteractive marketing-safe answers to the 10 core Grocery Brain questions, with structured content fields ready to migrate into CMS entries.
grocery-brain-qa.htmlPropensity model training, feature store, household GNN, attribute sensitivity — from raw transactions to a per-shopper inference.
ml-personalization-pipeline.htmlHow the agent reasons, routes between models, and grounds its answers in the HyperGraph rather than free-associating.
eagle-ai-agent-architecture.html3D PCA walk through the product embedding space — 550 pre-projected points across 9 departments. See how semantically-near SKUs cluster.
embeddings-projector.htmlHallucination scorecards, accuracy benchmarks, latency budgets — how the intelligence layer is held accountable over time.
evaluation-tracker.htmlLive shopper-behavior analytics on the production data warehouse; exports a 9-tab deck.
data-insights-dashboard.htmlWant the defensibility argument behind these models? The Evidence tab carries the deeper reads — the AI pipeline, householding & GNN, and LLM-levers essays.
The platform stack, built on the Agentic Commerce Protocol (ACP). Each module is independently entitled and deployable; tenants opt in via the multi-tenant config in infrastructure/customers/<slug>.json.
The contract: A2A capabilities, MCP tools, A2UI events. The model- and channel-portable foundation for every other module.
acp-overview.htmlWalk through how Google Search calls a grocer endpoint hosted in the grocer's own GCP project. Watch which services light up; track consumption attributed to the customer's billing.
ucp-interactive.htmlThree pillars in one frame: Food Intelligence Graph + Shopper Memory + Cart Builder. Switch personas, run a query, and watch the unified context object mutate in real time.
unified-commerce.htmlAgentic retail media network. Brand-attribute targeting and live ad placement.
ads-interactive.htmlFull RMN platform walkthrough: campaign authoring, attribution, ROAS reporting.
ads-platform-demo.htmlShoppable discovery pitch: viral trend detection, creator-driven cart, real-time intent signals.
social-interactive.html“TikTok to cohort in seconds” — the pipeline that turns a viral food trend into a merchandisable cohort.
social-intelligence-pipeline.htmlOperator setup console for the social listening fleet. Configure active platform streams, run live Gemini video parser simulations, moderate nutrition claims, and review sponsor match biddings.
social-admin-ux.htmlThe levers a merchandiser or marketer uses to steer the agent. Three tabs — Levers, Test (A/B harness, holdouts, p-values), and Attribution (multi-touch paths, ROAS matrix, incremental lift). Every change auditable.
operator-studio.htmlLive retailer demos showing the Delectable AI agent embedded in real-world commerce surfaces — the layer a shopper actually sees.
The actual shopper-facing storefront with shoppable videos, creator spotlights, trending signals, AI shopping assistant, and a CMS that composes it all per tenant. Opens in a new tab.
delectable-prototypes.web.app/social-experience →Walk five shopper archetypes across the whole journey — home โ category โ product โ cart — on desktop and mobile, with the agent driving every surface.
persona-studio.htmlSide-by-side e-commerce face-off. Watch a traditional static store run alongside Delectable's agentic commerce protocol across 4 interactive shopper missions.
split-stage.htmlA side-by-side comparative playground: Standard Cart vs. Delectable AI Perfect Cart. Toggle between Business Outcome view and Technical Deep-Dive view to see how 7+ orchestrated models build the optimal checkout basket.
perfect-cart.htmlPersonalized agentic landing experience with embedded chat assistant.
integrated-home.htmlProduct detail view with agent-driven recommendations and substitutes.
integrated-product.htmlFull agent-driven shopping flow from intent to cart.
integrated-shop.htmlPersonalized category browse with propensity-driven ranking.
ge-category.htmlMobile app frame — the agentic surface in a phone shell, with A2UI cards and image fallback.
mobile-experience.htmlThe polished consumer + admin experiences that the platform ships with. Investors comparing the Persona Studio (curated mocks) against the production-grade admin console see both the storytelling and the actual product.
Deep-dives written for engineering and product diligence audiences — the “how it’s actually built” reading behind every layer of the stack.
Click through the full system topology: agents, graph, data layer, deploy targets.
architecture-interactive.htmlThe diligence-grade write-up: what's real, what's proven, and where the substrate lives.
architecture-evidence.htmlSearch-era grocery vs. agentic grocery — side-by-side outcomes.
before-after.htmlContinuous compliance evidence for HIPAA, CCPA, GDPR, and AI Governance with real-time edge DLP filters, zero-retention audits, and cryptographic proofs.
compliance-evidence.htmlTechnical thought leadership: how we build defensible AI for grocery without falling into LLM-wrapper traps. The analyst/exec leave-behind set.
The full thesis in one read.
challenge-executive-summary.htmlHow the agent is grounded, routed, and evaluated end-to-end.
challenge-ai-pipeline.htmlThe defensibility argument: HyperGraph is the moat.
challenge-vs-chatgpt.htmlWhere the prompt, the graph, and the model interact.
challenge-llm-levers.htmlGraph neural networks for household-level inference.
challenge-householding-gnn.htmlHow rankings stay relevant without becoming homogenous.
challenge-algorithmic-curation.htmlArchitecture for engineering audiences.
challenge-technical-deep-dive.htmlThe unit-economics story behind the platform.
challenge-delectable-value.html