AI Development for
E-Commerce
Personalisation engines, dynamic pricing, demand forecasting, and AI support bots for e-commerce teams. Measurable results. India based engineering team.
Every e-commerce platform sitting on transaction and user behaviour data is sitting on a goldmine it is not fully using. AI turns that data into better product recommendations, smarter pricing, lower support costs, and more accurate inventory decisions.
We have built AI for e-commerce products that shipped to real users and moved real metrics. Here is what we do.
What We Build for E-Commerce Teams
Personalised Recommendation Engines
Collaborative filtering and hybrid recommendation models that surface the right products to each user. Trained on your catalogue and transaction history to lift average order value and repeat purchases.
Dynamic Pricing and Margin Protection
ML models that adjust prices based on demand signals, competitor data, and inventory levels. Captures revenue in peak demand without eroding margins in off-peak periods.
Demand Forecasting
Time series models that forecast product demand at SKU level. Reduces stockouts, cuts excess inventory holding cost, and improves supplier order accuracy significantly.
AI Customer Support Bots
Chatbots trained on your product catalogue, policies, and order data that handle returns, tracking queries, and product questions without escalating every request to a human agent.
Visual and Semantic Search
Let shoppers search by uploading an image or describing what they want in natural language. Finds the closest matching products using computer vision and semantic embeddings.
Inventory Optimisation
AI-assisted inventory management combining demand forecasting, lead time modelling, and safety stock optimisation to keep the right products in stock at the right locations.
ShopHub: A full-stack e-commerce platform with AI recommendations that moved conversion
We built ShopHub end to end for a US client: a React and Node.js web platform, native iOS and Android apps, Elasticsearch-powered search, Stripe payments, and an AI recommendation engine. Users who engaged with personalised recommendations had a significantly higher average order value than those who used only search and browsing. The platform handled real production traffic from launch.
Read the ShopHub Case StudyHow we approach e-commerce AI differently
Most recommendation engines get built as an afterthought, bolted onto an existing product after launch. The results are usually mediocre because the data architecture was not designed to support personalisation from the start. We think about the data model, the event tracking layer, and the recommendation architecture at the same time as the product itself. That means by the time the AI features go live, they have clean, complete training data to work with rather than months of inconsistently tracked events that need cleaning before a model can touch them.
E-commerce products we are most experienced building
- Full-stack multi-vendor e-commerce platforms with web and mobile apps
- Product recommendation and personalisation engines
- Dynamic pricing models and margin protection tools
- AI-powered search with visual and semantic capabilities
- Demand forecasting and inventory management systems
- Customer support chatbots trained on product and policy data
Explore related services
Ready to make your e-commerce data work harder?
Tell us what you are selling, what data you have, and what metric you most want to move. We will tell you what is worth building and what is not.
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Tell us what you are building and we will come back within one business day with a plan, a timeline, and an honest cost estimate.
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A118, Sector 63
Noida, UP 201301
304 Krishna Classic, A.B Road
Indore, MP 452008