AI Solutions for E-commerce & Retail
E-commerce is a margin game. You’re competing on price, speed, experience, and personalization simultaneously. The companies winning right now aren’t just selling products; they’re running AI systems that decide what to show each customer, when to adjust prices, how much inventory to hold, and how to handle the 30% of orders that generate support tickets.
We’ve been building e-commerce platforms since 2014. Our work with Wooter, a sports e-commerce platform, taught us what actually matters in retail AI: the recommendation that converts, the price adjustment that protects margin without losing the sale, the voice agent that resolves “where’s my order?” in 40 seconds instead of 8 minutes.
Our team of 66 professionals, including 20+ AI/ML engineers, builds AI agents and voice agents for e-commerce companies that are past the startup phase and ready to automate. Here’s what we build and what it does.
What Does AI Do for E-commerce That Actually Moves Revenue?
Most AI in e-commerce falls into four buckets: recommendation, pricing, operations, and customer support. The first two drive revenue up. The second two drive costs down. The best implementations do all four.
AI Recommendation Engines
Product recommendations drive 35% of Amazon’s revenue. That’s not a secret. The technology behind it isn’t either. But implementing it well, for your specific catalog, customer base, and purchase patterns, requires custom model training.
Our recommendation AI agents use collaborative filtering (what similar customers bought), content-based filtering (product attributes and relationships), and contextual signals (time of day, device, browsing history, season) to serve personalized product suggestions across your site.
We build these as real-time agents, not batch-processed suggestion lists. When a customer adds running shoes to their cart, the recommendation agent immediately evaluates: What socks pair best? What’s their likely price sensitivity based on their browsing pattern? Have they bought from this brand before? The suggestions update in under 200 milliseconds.
For a typical mid-market e-commerce store ($5M-$50M annual revenue), a properly tuned recommendation engine adds 8-15% to average order value. We’ve seen as high as 22% for stores with large catalogs (10,000+ SKUs) where discovery is a real problem.
Visual Search
Customers photograph a product they want and search your store with that image. Our visual search agents use computer vision models trained on your product catalog to identify matching or similar items. This is particularly valuable for fashion, home decor, and furniture, categories where customers know what something looks like but don’t know what it’s called.
Visual search typically converts 30-40% higher than text search because the customer is already high-intent by the time they’re photographing something.
How Does AI-Powered Dynamic Pricing Work?
Dynamic pricing isn’t about changing prices randomly. It’s about making data-driven price adjustments based on demand, competition, inventory levels, and customer segments.
Our pricing AI agents monitor:
- Competitor prices: Real-time scraping and API-based monitoring of competitor pricing across marketplaces
- Demand signals: Search volume, page views, add-to-cart rates, and conversion rates per product
- Inventory position: Current stock levels, incoming shipments, and sell-through rates
- Customer segments: Price sensitivity varies by customer. A returning loyal customer and a first-time visitor from a deal site have different price elasticities
- Margin constraints: Minimum margin floors, MAP (Minimum Advertised Price) policies, and promotional calendars
The agent adjusts prices within your defined rules, automatically. You set the boundaries: minimum margin, maximum discount percentage, competitive positioning targets. The AI optimizes within those boundaries.
For competitive categories where price matters, dynamic pricing typically improves gross margin by 3-8% while maintaining or improving conversion rates. That’s pure profit on existing traffic.
Inventory Forecasting
Stockouts cost you sales. Overstock costs you margin and warehouse space. Our inventory forecasting agents use historical sales data, seasonality patterns, marketing calendar inputs, and external signals (weather, trends, economic indicators) to predict demand at the SKU level.
The forecast updates daily and integrates with your inventory management system to generate purchase recommendations: what to order, how much, and when. For seasonal businesses, the agent starts adjusting forecasts 8-12 weeks ahead of peak periods based on early demand signals.
We’ve built these systems for catalogs ranging from 500 to 50,000+ SKUs. The forecasting accuracy typically runs 85-92% at the SKU level for a 30-day window, compared to 60-70% for spreadsheet-based forecasting.
Let’s talk about AI for your e-commerce operation
What Can Voice Agents Do for E-commerce Customer Support?
Post-purchase support is the most expensive part of e-commerce customer service. “Where’s my order?” alone represents 30-40% of all support contacts for most online retailers. Returns processing, exchange requests, and order modifications make up another 25-30%.
Our voice agents handle these conversations end-to-end.
Order Tracking
The customer calls. The voice agent identifies them by order number or email address, pulls the tracking information from your OMS (order management system) and the carrier API, and gives a natural-language update: “Your order shipped yesterday via FedEx and is expected to arrive Thursday. It’s currently in Memphis. Would you like me to text you the tracking link?”
Total call time: 45-60 seconds. Cost per interaction: under $0.50, compared to $5-8 for a human agent handling the same call.
Returns and Exchanges
Returns are more complex than order tracking, but they follow predictable patterns. The voice agent verifies the order, confirms which item the customer wants to return, checks it against your return policy (window, condition, exclusions), generates a return label, and emails it. For exchanges, it checks size or color availability before processing.
For straightforward returns (which are 70-80% of return requests), the entire process is automated. Complex cases, like damaged items requiring photos or warranty claims, get escalated to a human agent with full context.
Product Recommendations Over the Phone
This is where voice agents get interesting for revenue. When a customer calls about an out-of-stock item, the voice agent doesn’t just say “sorry.” It checks alternatives: “That jacket isn’t available in medium right now, but we have it in a similar style from the same brand. It’s $10 less and available for two-day shipping. Want me to add it to your cart?”
Our voice agents at voice.agent.contrivesol.com are built for exactly these kinds of conversational commerce interactions, where the goal isn’t just to resolve a ticket but to keep the sale alive.
Explore our voice agents for customer support and voice agents for sales.
How Did You Build Wooter’s E-commerce Platform?
Wooter is a sports e-commerce platform that sells custom team uniforms and sports apparel. We built their platform handling product customization (team names, numbers, logos on jerseys), bulk ordering workflows, and a catalog that scales across dozens of sport categories.
The technical challenges were specific to e-commerce:
- Product configuration: Each item has dozens of customization options that affect pricing and fulfillment
- Visual previews: Customers need to see their customizations rendered on the product before ordering
- Bulk pricing logic: Team orders require quantity-based pricing tiers with different rules per product category
- Inventory management: Custom products have different inventory dynamics than off-the-shelf items
This project taught us that e-commerce AI isn’t just about the recommendation algorithm. It’s about understanding the business logic that makes each store unique and building AI that works within those constraints.
We also have deep Shopify and WooCommerce expertise. Our team has built custom Shopify themes, WooCommerce extensions, and headless commerce architectures using MERN and MEVN stacks. Whatever your platform, we can integrate AI into it.
What Does the AI Agent Architecture Look Like for E-commerce?
We build e-commerce AI using a multi-agent system where specialized agents handle different domains:
- Recommendation agent: Processes browsing behavior, purchase history, and catalog data to serve personalized suggestions
- Pricing agent: Monitors competitive data and demand signals to optimize prices within your rules
- Inventory agent: Forecasts demand, generates purchase orders, and alerts on stockout risks
- Customer service agent: Handles voice and chat interactions for support, upselling, and retention
- Catalog agent: Manages product descriptions, attribute extraction, SEO optimization, and cross-sell mapping
These agents share context through a central orchestration layer built on LangChain and LangGraph. When a customer calls about a delayed order, the customer service agent can check the inventory agent for restock dates and the recommendation agent for alternatives, all within the same conversation.
Integration points: We connect to Shopify, WooCommerce, Magento, BigCommerce, custom platforms, and headless commerce stacks. On the fulfillment side, we integrate with ShipStation, ShipBob, FedEx, UPS, USPS, and DHL APIs. For payment, Stripe, PayPal, Square, and Braintree.
What Results Should You Expect?
Here are specific, measurable outcomes from e-commerce AI deployments:
| Metric | Typical Result |
|---|---|
| Average order value increase (recommendations) | 8-15% |
| Gross margin improvement (dynamic pricing) | 3-8% |
| Inventory carrying cost reduction | 15-25% |
| Stockout frequency reduction | 40-60% |
| Support ticket resolution (automated) | 55-70% |
| Cost per support interaction reduction | 70-80% |
| Return processing time | 2 minutes vs 8-12 minutes |
| Cart abandonment recovery (AI follow-up) | 10-18% recovery rate |
These numbers come from e-commerce operations doing $2M-$100M in annual revenue. Results scale with catalog size, traffic volume, and the complexity of your current manual processes.
Why Contrive for E-commerce AI?
We’ve been building e-commerce platforms for 12+ years. We’ve delivered 250+ projects. We understand the difference between AI that looks good in a demo and AI that actually moves your P&L.
Our pricing reflects our Lahore, Pakistan headquarters: you get the same quality of AI engineering at 60-70% lower cost than US agencies. Our US office in Danville, California means we’re available in your timezone for planning and communication.
By the numbers: 95% client retention, 98% on-time delivery, 4.9/5 Indeed rating, 4.7/5 Clutch rating, 4.5/5 Glassdoor rating. Our 20+ AI/ML engineers work with LangChain, CrewAI, AutoGen, LangGraph, Python, and the full MERN/MEVN stack daily.
How We Start
We don’t pitch a 6-month, six-figure project on the first call. We start with a 2-week discovery phase where we audit your current tech stack, identify the highest-impact AI opportunity (usually recommendations or support automation), and build a scoped proposal with projected ROI.
Then we deliver a working pilot in 4-6 weeks. You measure the results against baseline. If it works, and it will, we expand to the next workflow.
Start the conversation about AI for your store
Frequently Asked Questions
Can you integrate AI recommendations into our existing Shopify or WooCommerce store?
Yes. We’ve built recommendation engines for both platforms. For Shopify, we deploy as a custom app that integrates with the Storefront API and uses theme app extensions for the frontend display. For WooCommerce, we build as a custom plugin that hooks into WooCommerce’s product and order data. Both approaches work without replacing your existing theme or disrupting your current setup. Integration typically takes 2-3 weeks.
How much historical data do we need for the AI to work effectively?
For recommendation engines, we need at least 3 months of order history and product catalog data. More data means better models, but 3 months is the minimum for useful personalization. For dynamic pricing, 6 months of pricing and sales data is ideal. For inventory forecasting, 12 months minimum to capture seasonal patterns. If you have less data, we can still deploy rule-based systems that transition to ML-based systems as your data accumulates.
Will dynamic pricing hurt our brand if customers see different prices?
We implement dynamic pricing with brand protection in mind. Price adjustments are typically applied across the board for a given time window, not per-customer, unless you specifically want personalized pricing (which some B2B operations do). We also set maximum change frequencies (prices don’t jump around hourly unless you’re in a marketplace environment), minimum price floors, and competitive parity rules. The goal is optimized pricing, not pricing that feels unpredictable to your customers.
How do your voice agents handle complex product questions, not just order status?
Our voice agents can be trained on your product catalog, including specifications, compatibility information, sizing guides, and common use cases. When a customer asks “will this laptop bag fit a 15-inch MacBook Pro?”, the agent checks the product dimensions against known laptop sizes and gives a specific answer. For highly technical products (electronics, industrial equipment), we set confidence thresholds: if the agent isn’t sufficiently confident in its answer, it transfers to a product specialist rather than guessing.
What’s the cost structure for your e-commerce AI services?
We price based on project scope, not hourly rates. A single-workflow implementation (like a recommendation engine or voice agent for order tracking) typically runs $15K-$40K depending on complexity and integration requirements. Multi-agent systems covering recommendations, pricing, and support automation range from $60K-$150K. These are one-time development costs. Ongoing infrastructure costs depend on your traffic and transaction volume but typically run $500-$3,000/month for cloud compute and API usage. We provide detailed cost breakdowns during the scoping phase.
Ready to put AI to work on your e-commerce margins? Contact us or call +1 (775) 459-7713 (US) or 042 35199410 / +92 327 4945650 (Pakistan). We’ll start with the number that matters most to your business and build from there.
