Agentic AIAI-Powered Commerce
Intelligent storefronts that price, merchandise, and personalize autonomously — at a scale no human team can match.
- Phase
- 4-step engagement
- Hypercare
- 30 days included
- Cadence
- Weekly demos
Trusted by teams shipping at scale
What we deliver
Every AI-Powered Commerce engagement bundles these capabilities by default. We tune the depth of each to fit your scope.
Dynamic pricing agents
Inventory optimization AI
CX personalization engines
Autonomous merchandising
Shopify Functions integration
Real-time recommendation systems
How we build AI-Powered Commerce
A repeatable four-phase engagement. Same rigor every time, scoped to the work in front of us.
Discover
We map the current state, surface constraints, and lock the scope before any code is written. You leave the phase with a written success definition.
- Audit document
- Success criteria
- Risk register
Architect
We pick the stack, design the data model, and prove the riskiest path first. Architecture decisions are reviewed with your team before build starts.
- Architecture doc
- Stack decision record
- Spike on riskiest path
Build
Iterative delivery in weekly increments. You see working software every Friday, can redirect priorities each Monday, and never wait six weeks for a demo.
- Weekly demo cadence
- Production-ready code
- CI/CD + tests
Operate
We ship with observability, hand off runbooks, and stay accountable post-launch. 30-day hypercare is included on every engagement.
- Monitoring dashboards
- Operational runbooks
- 30-day hypercare
The full breakdown
Architecture, decisions, and the operational details behind every AI-Powered Commerce engagement. Skim with the table of contents, or read straight through.
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
Four independent agents, one orchestration plane. Each layer makes decisions inside guardrails you define and logs every action for review.
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.
Outcome 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.
Outcome 20-30% lower carrying costs 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, decisions are enforced at the platform level with zero latency.
Outcome Editorial 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.
Outcome Validate impact via A/B testing before any full rollout.
Implementation on Shopify Plus
Shopify Plus is our primary platform for AI commerce implementations. The architecture is built as four cleanly separated layers — each layer is independently deployable, observable, and revertible.
The four-layer commerce stack
Cleanly separated concerns. Each layer is independently deployable and revertible.
Webhooks fire on every product view, cart event, and purchase, feeding your behavioral data pipeline in real time. No polling, no batching delay.
Python services consume events and maintain customer and product embeddings. This is where the learning happens, isolated from the storefront so model updates never block the customer experience.
Pricing and merchandising agents run on your configured schedule or in response to events. Every decision is logged with full context, so you can replay, audit, or roll back any action.
Shopify Functions apply agent decisions at checkout and browse time with zero latency. Decisions are pre-computed or served from cache, so the customer experience stays fast.
The result is a commerce system that continuously self-optimizes within the guardrails you define — and stays observable, debuggable, and reversible the entire way.
Included in every engagement
- 01
AI commerce architecture document
- 02
Deployed pricing and merchandising agents
- 03
Personalization layer with A/B testing framework
- 04
Real-time analytics dashboard
- 05
Model retraining pipeline and monitoring
Technology
The tools and platforms we deploy on every AI-Powered Commerce engagement.
Common questions
Everything you need to know before starting a project with us.
No. We layer AI capabilities onto your existing stack through Shopify APIs, Functions, and webhooks. No full rebuilds required.
Every pricing decision is bounded by floor/ceiling constraints you define. The agent optimizes within those bounds and logs every decision for review.
AI ecommerce personalization analyzes behavioral signals — browse history, purchase patterns, and session intent — to deliver individually relevant product experiences in real time. Stores that implement machine learning for ecommerce personalization typically see 15-30% lifts in conversion rate because customers encounter products that match their actual preferences, not generic bestseller lists.
AI-powered product recommendations perform best with a combination of behavioral data (views, clicks, add-to-carts), transactional data (purchases, returns), and product attribute data (categories, tags, descriptions). We can begin generating effective recommendations with as little as 30 days of historical data, and accuracy improves continuously as the machine learning models train on new interactions.
AI pricing optimization continuously evaluates competitor pricing, inventory levels, demand velocity, and margin constraints to recommend or execute optimal price points in real time. Unlike manual repricing, an AI pricing optimization engine can process thousands of SKUs simultaneously and adapt within minutes to market changes, maximizing both revenue and margin.
Yes. Our machine learning for ecommerce architecture runs inference on a separate service layer and pushes decisions to Shopify via Functions and APIs, so there is zero impact on storefront load times. AI-powered product recommendations and pricing decisions are pre-computed or served from cache, ensuring the customer experience remains fast while benefiting from intelligent personalization.
Related services
Agentic Workflow Orchestration
We design and deploy autonomous agent systems that replace manual workflows end-to-end. Our agents execute multi-step processes, make decisions based on real-time data, and self-correct without human intervention.
AI Product Description Automation
Automated AI product description generation and optimization. We build systems that write, update, and A/B test product copy across your entire catalog at scale.
AI Strategy & Audits
Before building, we map your highest-leverage AI opportunities. Our audits analyze your data, workflows, and competitive landscape to identify where autonomous systems will generate the most ROI.
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