AI-Powered Commerce

AI Commerce: Beyond the Chatbot

The first wave of AI in e-commerce was about adding a chat interface to existing processes. Chatbots answering FAQ questions. Recommendation widgets using collaborative filtering. Useful, but incremental.

We build the second wave: autonomous commerce systems that make operational decisions across pricing, inventory, and merchandising — continuously, in real-time, without human intervention for routine decisions.

The Four Commerce Intelligence Layers

Dynamic Pricing Intelligence

Your pricing shouldn't be static. An AI pricing agent continuously evaluates:

• Competitor pricing (via structured scraping and API feeds)
• Current inventory levels and sell-through velocity
• Demand signals from your session and purchase data
• Margin constraints you define
• Promotional calendar and markdown windows

The agent proposes and executes price changes within your defined bounds, logs every decision with context, and learns from conversion outcomes. Result: prices that are always optimally positioned without a team of analysts.

Inventory Optimization

Inventory agents replace reactive reorder triggers with predictive procurement. The agent monitors:

• SKU-level sell-through velocity
• Supply chain lead times by vendor
• Seasonal demand curves
• Stockout risk scores

It generates procurement recommendations (or executes them directly if connected to your ERP) before you run out — not after. Well-designed inventory agents reduce carrying costs by 20-30% while simultaneously reducing stockouts.

Autonomous Merchandising

Your product catalog order shouldn't be manually curated. A merchandising agent continuously optimizes:

• Collection and category page product ordering
• Cross-sell and upsell recommendation sequences
• Search result ranking
• Promotional placement and sequencing

Implemented via Shopify Functions, the agent's decisions are enforced at the Shopify platform level with zero latency. Your editorial team defines the strategy; the agent executes it at scale.

CX Personalization

Personalization without enough data is noise. We build personalization engines that earn their complexity:

• Behavioral embedding models that learn individual customer preferences
• Segment-level merchandising for cohorts with shared patterns
• Session-aware recommendations that respond to current intent
• Post-purchase sequence optimization

All instrumented for A/B testing so you can validate impact before full rollout.

Implementation on Shopify Plus

Shopify Plus is our primary platform for AI commerce implementations. The architecture:

1. Event layer: Webhooks fire on every product view, cart event, and purchase — feeding your behavioral data pipeline
2. Intelligence layer: Python services consume events and maintain customer and product embeddings
3. Decision layer: Pricing and merchandising agents run on your configured schedule or in response to events
4. Enforcement layer: Shopify Functions apply agent decisions at checkout and browse time with zero latency

The result is a commerce system that continuously self-optimizes within the guardrails you define.