01 / INFRASTRUCTURE
The agentic storefront.
Shopify Agentic Storefronts point toward live native inventory feeds for ChatGPT and Meta Muse networks. Our roadmap explores how catalog data can meet these emerging channels.
INVENTORY → INTELLIGENCE
System Active / Processing Engine 3.5 Overhaul
The Optimization Layer
for the Agentic Commerce Era.
Your products. Understood by AI.
Turn unstructured catalogs into the language of intelligent commerce.
Explore the local demo. No signup required.
01 — INTELLIGENCE, STRUCTURED.RAW CATALOG → SEMANTIC CONTEXT → AI DISCOVERY
Raw Shopify Payload
INPUT / 01catalog.stream → unstructured
Claude 3.5 Engine
ENRICHING CONTEXT
Semantic Product Schema
OUTPUT / 03llm.context → discovery-ready
VISIBILITY INDEX
ENRICHMENT IN PROGRESS
ENRICHMENT IN PROGRESS
42%
SCHEMA-FIRST ARCHITECTURESEMANTIC ENRICHMENTBUILT FOR AI DISCOVERY
02 / The structural shift
Commerce is changing.
Your catalog should, too.
The next storefront is a conversation.
The missing link is structured product intelligence.
02 / THE BOTTLENECK
Invisible by default.
Unstructured descriptions and missing technical variables leave AI shopping nodes without the context they need. A great product is only as discoverable as its data.
FRAGMENTED DATA → LOST CONTEXT
03 / THE ENGINE
Built to be understood.
AgenticFlow AI is designed to restructure catalogs for LLM context windows, enriching semantic attributes so products can compete in natural language discovery.
STRUCTURED CONTEXT → AI DISCOVERY
03 / Incubated architecture
Engineered for
what comes next.
A deliberate path from raw product data to agent-ready intelligence. Explore the technical foundation and the three phases of our development roadmap.
01 / INFERENCE LAYERClaude 3.5 API
02 / CONTEXT MEMORYVector Data Storage
03 / APPLICATION RUNTIMERemix Framework
PROPOSED STACK / FRONTEND DEMO ACTIVE
Architecture indicators illustrate the roadmap, not connected service health.
PHASE 01 / CATALOG FOUNDATIONAudit & map.
PHASE 01 / CATALOG FOUNDATION
Audit & map.
Inspect catalog completeness, identify missing attributes, and map product data to a consistent semantic schema.
PHASE 02 / INTELLIGENCE LAYERThe enrichment engine.
PHASE 02 / INTELLIGENCE LAYER
The enrichment engine.
Normalize descriptions, extract product context, and prepare searchable semantic tokens for LLM consumption.
PHASE 03 / DISCOVERY VALIDATIONLive agent simulation sandbox.
PHASE 03 / DISCOVERY VALIDATION
Live agent simulation sandbox.
Explore structured product output and evaluate conversational discovery scenarios. The local preview demonstrates schema transformation without calling an AI service.
Your next customer might be an agent.
Make sure your products speak its language.