AI that sells, forecasts,
and supports — autonomously.
Personalized recommendations, demand forecasting, automated customer service, and dynamic pricing — AI agents engineered to increase revenue per visitor while reducing operational cost.
Where AI agents drive commerce performance.
Four high-impact agent deployments where we consistently see measurable improvements in revenue, efficiency, and customer experience.
Personalized Product Recommendations
Before
Static 'bestsellers' and 'you might also like' widgets showing the same products to every visitor. Recommendation click-through rate at 1.8%. Product discovery limited to search and manual browsing. Average order value flat for 6 months.
After
AI-powered recommendations personalized to each visitor's browsing behavior, purchase history, and real-time session context — on product pages, cart, search results, and email. The model learns which product attributes drive purchase decisions for each customer segment and surfaces items they wouldn't have found on their own.
26% higher AOV
Inventory Demand Forecasting
Before
Inventory planning based on last year's sales plus a gut-feel multiplier. Frequent stockouts on trending items (lost sales). Overstock on slow movers tying up capital. No visibility into demand signals until it's too late.
After
ML-based demand forecasting that incorporates historical sales, seasonality, marketing calendar, weather data, social media trends, and competitor pricing. Daily SKU-level forecasts with confidence intervals. Automated reorder triggers when forecast exceeds available inventory within lead time.
34% fewer stockouts
Customer Service Automation
Before
Support team drowning in repetitive inquiries — order status (35%), return policy (20%), product questions (25%). Average first response time of 4 hours. CSAT declining as wait times increase during peak periods. Hiring more agents isn't sustainable.
After
AI agent that handles 70%+ of routine inquiries instantly — order tracking, return initiation, product availability, shipping estimates — with natural language understanding, full order context, and seamless handoff to human agents for complex issues. 24/7 coverage with consistent quality.
72% ticket deflection
Dynamic Pricing Optimization
Before
Manual pricing reviews once per quarter. Competitors change prices daily and you don't know until a customer complains. Promotions are gut-feel discounts applied to entire categories. No way to price individual SKUs based on demand elasticity.
After
AI pricing engine that monitors competitor prices, demand elasticity, inventory levels, and margin targets to recommend optimal prices for every SKU. Automated price adjustments within guardrails you define. Promotion optimization that calculates the minimum discount needed to hit volume targets without over-discounting.
12% margin improvement
Built for the teams ready to automate growth.
VP of E-Commerce
You know personalization drives revenue but your current recommendation engine is basic and your pricing is reactive. You need AI that operates at the speed and scale of modern e-commerce — adjusting in real-time based on data, not quarterly reviews.
CMO scaling a DTC brand
Customer acquisition costs keep climbing. The brands winning market share are the ones that personalize every touchpoint. You need AI-powered product discovery and customer communication that makes every visitor feel like the store was built for them.
Head of Customer Experience
Your support team is overwhelmed with repetitive inquiries while complex issues wait in the queue. You need an AI agent that handles routine requests instantly and frees your team to focus on the interactions that actually require human judgment.
CTO building for scale
Your catalog is growing, your customer base is expanding, and manual processes that worked at 1,000 orders per day break at 10,000. You need intelligent automation that scales with the business without linearly scaling headcount.
From data audit to production AI.
Data & Integration Audit
We inventory your data sources — product catalog, order history, browsing behavior, customer profiles, inventory levels — and assess data quality, volume, and integration feasibility. This determines which agents are viable and which need data infrastructure first.
Agent Architecture Design
We design the agent architecture — model selection, training data pipeline, inference infrastructure, API contracts, and integration points with your storefront and backend systems. Each agent has defined success metrics and measurement methodology.
A/B Testing Pilot
We deploy each agent to a controlled traffic segment (typically 10-20%) and measure performance against the existing system. No agent goes to 100% traffic until it proves statistically significant improvement over the control.
Scale & Monitor
Proven agents scale to full traffic with real-time monitoring, drift detection, and automated retraining triggers. We provide performance dashboards showing attributed revenue, accuracy metrics, and cost savings.
Questions about AI agents for e-commerce.
How accurate are AI product recommendation engines compared to rule-based systems?
Rule-based systems (bestsellers, 'customers also bought') typically generate 2-5% of total revenue. AI recommendation engines that incorporate browsing behavior, purchase history, product attributes, and real-time session context typically generate 10-31% of total revenue — a 3-6x improvement. The accuracy comes from the model's ability to learn patterns that humans can't manually program — like the fact that customers who buy a specific running shoe in size 10 are 4x more likely to buy a particular brand of compression socks within 30 days. We benchmark recommendation accuracy using click-through rate, add-to-cart rate, and attributed revenue, and we run continuous A/B tests against your existing system to prove incremental value before scaling.
How do AI agents integrate with Shopify and custom commerce platforms?
For Shopify, we integrate via the Storefront API for product data, the Admin API for orders and inventory, and Shopify Functions for checkout customization. Real-time behavioral data is captured via a lightweight JavaScript snippet on the storefront. For custom platforms, we integrate through your existing APIs — product catalog, order management, inventory, and customer data endpoints. The AI agents run as independent microservices that receive events (page view, add to cart, purchase) via webhooks or a message queue, process them against trained models, and return recommendations, forecasts, or decisions via low-latency REST APIs. Average response time for recommendation requests is under 50ms.
How do you handle customer data privacy with AI personalization?
All personalization models are trained on first-party data that your customers have consented to share. We implement privacy by design — behavioral data is pseudonymized (linked to session IDs, not PII) during model training, models are served from your infrastructure or a dedicated tenant (not shared with other customers), and we provide data deletion pipelines that remove individual customer data from both raw datasets and trained models when requested. For GDPR and CCPA compliance, we implement consent-gated personalization — customers who opt out receive non-personalized recommendations (bestsellers, trending items) rather than no recommendations at all.
How do you measure ROI on e-commerce AI agents?
We measure each agent against the specific metric it was designed to move. Recommendation engines are measured by attributed revenue (purchases that included a recommended item), lift over the control group, and average order value increase. Demand forecasting is measured by inventory accuracy improvement (reduction in stockouts and overstock), carrying cost reduction, and lost sales prevention. Customer service agents are measured by ticket deflection rate, customer satisfaction score, resolution time, and cost per resolution compared to human agents. Dynamic pricing is measured by margin improvement, revenue per unit, and competitive win rate. Every agent ships with a monitoring dashboard and a holdout test so you can measure incremental impact.
How do AI agents handle returns, edge cases, and unusual customer behavior?
This is where production AI systems diverge from prototypes. Our agents include explicit fallback logic — when the model's confidence is below a configurable threshold, it defers to rule-based defaults or escalates to a human. For customer service agents, we define clear escalation paths: the agent handles routine inquiries (order status, return policy, product questions) autonomously and transfers complex or emotionally charged interactions to human agents with full context. For recommendation engines, we implement cold-start handling for new products (attribute-based recommendations until behavioral data accumulates) and anomaly detection for unusual behavior (gift purchases, bulk orders) that would distort personalization. We also build monitoring that alerts on drift — when model performance degrades against baseline metrics, triggering retraining or investigation.
Production AI, not proof-of-concept demos
Most AI projects stall in the POC stage because the team that built the demo can't operationalize it. We build for production from day one — real-time inference, monitoring, drift detection, fallback logic, and the integration work that connects a model to your actual commerce stack. As a US LLC with LATAM AI engineering talent, we deliver production-grade AI systems at 40-60% less than US-only agencies.
Production-grade from day one
Monitoring, fallbacks, and drift detection built in
40-60% cost savings
US LLC + LATAM AI engineers — senior talent, competitive rates
Ready to personalize every customer interaction?
Tell us about your e-commerce AI goals — product recommendations, demand forecasting, customer service automation, or dynamic pricing. We'll assess your data readiness and show you which agents can deliver ROI fastest.