Next-gen ecommerce intelligence
I build AI systems that help online stores sell more with less manual effort — recommendation engines that lift conversion, personalization that adapts to each shopper, demand forecasting that prevents stockouts, and automation that removes repetitive catalog and order work.
Available now · scoped projects start within 48 hours · NDA from day one
cold-start · ranking from content similarity only
Why this matters
I am Shreyans Padmani, a freelance AI developer with 5+ years building production AI systems for ecommerce and D2C businesses. Most online stores still run personalization off basic rule-based logic — bestsellers, "customers also bought" — or no personalization at all.
I build the systems that close that gap: recommendation engines trained on your actual catalog and customer behaviour, personalization layers that adapt display and messaging per shopper, forecasting models that keep inventory balanced, and automation that removes manual catalog, order, and support work. Everything integrates with your existing stack without disrupting current operations.
Run the numbers first
Move the sliders to match your store. The model applies the conservative middle of published benchmarks — an 18% conversion lift and an 8% AOV increase — to your current numbers.
Estimate only, based on published industry benchmarks and delivered projects. Your actual target metric is agreed in a written technical spec before any work begins — and measured against a live A/B test, not a slider.
Plain answer
An ecommerce AI developer builds machine learning systems that automate and personalize online retail operations: product recommendation engines, customer personalization layers, demand forecasting models, dynamic pricing systems, inventory optimization, and AI-powered customer support. The role combines data engineering (structuring product and behavioural data), machine learning (training recommendation and forecasting models), and integration engineering (connecting AI systems to platforms like Shopify, WooCommerce, and Magento via APIs). A freelance ecommerce AI developer typically delivers a working system — model plus API plus platform integration — rather than only a proof-of-concept notebook.
Searches for "AI ecommerce expert for hire" usually want a scoped, fast-starting engagement — exactly what a freelance AI developer delivers versus a multi-week agency onboarding process.
| Factor | Freelance (Shreyans) | Agency | In-house hire |
|---|---|---|---|
| Cost | $ to $$ — project or monthly | $$$ to $$$$ — team + markup | $$$$ — salary + benefits |
| Start time | 48 to 72 hours | 2 to 4 weeks | 3 to 6 months |
| Platform expertise | Shopify, WooCommerce, Magento, custom | Varies by allocated team | Depends on the hire |
| Direct access to the builder | Always | Rarely — account manager layer | Yes, after ramp-up |
| Best for | Recommendation engine, personalization, or forecasting project | Full platform rebuild, enterprise compliance | Ongoing, large-scale AI roadmap |
What I build
Practical AI systems designed to lift conversion, cut manual work, and scale with your store.
Personalized "you may also like," cross-sell, and upsell recommendations trained on your catalog and customer behaviour — not generic bestseller lists.
Adapts homepage layout, product ordering, search results, and email content to each shopper in real time based on browsing and purchase signals.
Automates listing, categorization, attribute tagging, and description generation to cut manual catalog work.
Analyzes browsing, cart, and purchase behaviour to build segments, predict intent, and improve email and ad targeting.
Predicts SKU-level demand to prevent stockouts and overstock, using historical sales, seasonality, and promotional data.
Automates order handling, fraud flagging, tracking updates, and delivery workflow coordination.
Chatbots and support automation grounded in your product catalog and policies — handling FAQs, order status, and returns.
Demand-responsive and competitor-aware pricing that adjusts within rules you define, not a black box.
Automated sales, customer, and inventory reporting with anomaly detection to surface issues before they become losses.
Highest-leverage build
An AI recommendation engine is a machine learning system that predicts which products a specific customer is most likely to purchase, based on their behaviour, purchase history, and similarity to other customers or products. Modern engines combine collaborative filtering (what similar customers bought), content-based filtering (what similar products look like), and increasingly two-tower deep learning models that encode both user and product data into a shared embedding space for real-time, high-relevance ranking.
This is the single highest-leverage AI investment most ecommerce stores can make. I build recommendation engines that go well beyond generic "customers also bought" logic:
Deployment is via a low-latency API (sub-100ms) that plugs into your storefront — homepage, product pages, cart, and email — on Shopify, WooCommerce, Magento, or a custom platform.
Built on the same foundation as my machine learning development and AI model training work.
Beyond recommendations
Recommendations are one output of personalization. A full personalization engine adapts the entire shopping experience per visitor — homepage banners, product sort order, search ranking, and triggered email or SMS content — based on real-time and historical behavioural signals.
Browsing pattern, cart value, and purchase frequency resolved into a live segment on every session.
Banners and product ordering adapt to visitor segment and in-session behaviour.
Results reordered against individual purchase and click history, not a single global relevance score.
Abandoned cart, browse abandonment, and post-purchase flows with per-shopper product selection.
Works within first-party data and cookie consent constraints — no reliance on third-party tracking.
Starts with the highest-ROI touchpoint — usually recommendations or abandoned cart — then expands. No slow all-at-once rebuild.
Architecture
Every system I build is structured across six layers, from customer data understanding through to secure infrastructure. Each layer depends on the one before it — which is why they are numbered.
Expectations, in writing
Concrete expectations based on published industry benchmarks and delivered projects. Target metrics are agreed in a written technical spec before work begins.
| Project type | Typical result | Timeline to production |
|---|---|---|
| AI recommendation engine | 10 to 30% conversion lift vs no personalization; 5 to 15% AOV increase from cross-sell and upsell | 4 to 8 weeks from catalog and behavioural data |
| Personalization engine (homepage, search) | 15 to 25% increase in click-through on personalized surfaces vs static | 5 to 10 weeks including A/B test setup |
| Demand forecasting | Forecast error (MAPE) 8 to 15% on a 30-day horizon; stockout reduction 20 to 40% | 3 to 6 weeks from historical sales data |
| Product catalog automation | 65 to 80% faster listing updates; 30 to 40% reduction in manual data entry | 3 to 5 weeks from catalog access |
| AI customer support automation | 40 to 60% of inbound queries automated; response time cut by 50%+ | 4 to 8 weeks including integration |
| Dynamic pricing model | 3 to 8% margin improvement within defined pricing rules | 4 to 7 weeks from historical pricing and demand data |
Delivered work
Automated product data entry and catalog management for an online retail business, reducing manual effort and improving listing accuracy across multiple sales channels. Includes automated product data extraction, catalog creation, image and description processing, and real-time inventory synchronization.
Streamlined order processing and customer handling for an ecommerce store by automating routine tasks. Includes automated order processing, real-time order tracking, instant status notifications, and automated delivery workflow coordination.
Built an AI-powered system to assist a customer support team by handling common queries and improving response time. Includes automated query handling, instant responses, order and refund support automation, and full interaction tracking.
Our commitment
Ecommerce businesses handle large volumes of product data, customer orders, and daily transactions. I focus on building practical AI systems — recommendation engines, personalization layers, and automation — that help online stores convert more visitors and reduce manual work. My approach combines structured data engineering, measurable experimentation, and reliable production deployment, so you can trust the numbers behind every recommendation.
Key differentiators
"The future of ecommerce is not just automated. It is personalized, intelligent, and built for scale."
Engineered with a working understanding of online retail workflows, customer behaviour, and digital marketplaces — so every system aligns with how ecommerce actually converts.
From small D2C stores to large multi-vendor platforms: recommendations, personalization, order processing, and customer support.
Transparent recommendation and pricing logic, so your team understands why a product was suggested or a price moved. Not a black box.
Models learn from customer interactions, browsing patterns, and sales data — improving without constant manual retuning.
Secure payment handling, customer data protection, and encrypted transactions built in from day one, not bolted on later.
Prior work with retail and customer behaviour data means faster deployment for personalized shopping and demand forecasting.
Supports daily store operations, from catalog to checkout to post-purchase.
Handles increasing listings, orders, traffic, and customer data without a rebuild.
Accurate product, order, and customer records with explainable AI decisions.
Ready-to-deploy modules
These modules help ecommerce teams manage products, orders, recommendations, and customer interactions more efficiently.
Manages product uploads and organizes catalog information automatically.
Continuously scores and ranks product recommendations per customer in real time.
Collects product details, images, and descriptions from multiple sources.
Reads key product details from supplier feeds or PDFs — built on computer vision.
Helps organize order details and processing workflows.
Tracks stock levels and predicts demand to manage product availability.
Structured, grounded responses using NLP and generative AI.
Tracks deliveries and updates order status automatically.
Maintains required business and transaction records.
Need a custom AI system for your specific ecommerce workflow? Talk to an expert →
FAQ
Answers to common questions about hiring an AI developer for ecommerce.
AI in ecommerce is used to automate routine tasks such as product listing, order processing, inventory management, and customer support. It helps businesses manage operations more efficiently and reduce manual work.
AI helps online stores improve accuracy, process orders faster, and manage product data more effectively. It also supports better customer experience through automated responses and recommendations.
Yes, AI systems can organize product information, update inventory, track orders, and manage customer records automatically. This reduces errors and saves time for business teams.
Yes, modern AI systems use secure data handling methods to protect product and customer information. Access controls and data protection practices help maintain security and privacy.
Get in touch
A free 30-minute discovery call. Bring your platform, your catalog size, and whatever you're doing for personalization today — you leave with a scope, a timeline, and a target metric. No deck, no pitch.
Typically replies within a few hours, IST business day
