AIKIT SOLUTIONS
AI E-commerce & Intelligent Shopping Experiences
Help shoppers find products with language, filters, and catalog data — without pretending a demo catalog is a live store.
What this service is
Shoppers describe a trip, a budget, or a constraint. The system should parse intent, search the catalog, compare a short list, and say when nothing fits.
The same ideas apply to ecommerce support (shipping policy), product search, recommendations as a ranked list, and automation around orders — always against real catalog and inventory APIs in production.
Problems it solves
Facet filters that only work if the customer already knows the attribute names.
Chat that recommends products that are out of stock, or invents SKUs that were never in the catalog.
What I build
- AI-powered storefronts
- AI shopping assistants
- Conversational product discovery
- Product search and comparison
- Recommendations as a concept layer
- Ecommerce support assistants
- Product catalog integration
- Inventory/API integration
- Shopify / WooCommerce / custom catalogs
- Ecommerce automation
Common use cases
- “Find a backpack under $150” style discovery
- Support answers from shipping policy
- Compare two SKUs on weight, price, and stock
- Notify a merchandiser when a feed field is missing
How the workflow works
Intent → catalog query and filters → in-stock check → comparison text → shortlist. Checkout stays in the store platform.
Shopify, WooCommerce, or a custom catalog are integration targets, designed per API — not pre-installed apps on this portfolio.
A labeled DEMO DATA walkthrough is the AI Shopping Assistant. Support-style chat overlaps AI Assistants; feed sync overlaps AI Automation.
When this is useful
Useful when the catalog is structured enough to filter, and you want language as a front door — not when there is no product data.
FAQ
Do you sell a Shopify or WooCommerce app?
This is a development portfolio, not an app store listing. Shopify, WooCommerce, or a custom catalog can be integrated through the store’s API for a specific project. Checkout stays on the store platform.
Are the product cards on this site real inventory?
The cards are labeled DEMO DATA: fictional products used to show matching, comparison, and a “no fit” path. They are not a live catalog, warehouse, or checkout. A real project would query your catalog and inventory APIs.
Can recommendations respect stock?
A production design can check availability through the inventory API before ranking SKUs. Out-of-stock items can be hidden or clearly marked. The demo does not call a warehouse.
Can shoppers check order status in chat?
Order lookup can be implemented through the platform’s order API after the customer is authenticated. The assistant then reads status rather than guessing. That capability is not live on these pages; it is designed per store.
Will this increase conversion?
The job is clearer matching and honest “no fit” answers when the catalog cannot meet the request. Conversion depends on catalog quality, traffic, and merchandising. No conversion metric is claimed here.
Can recommendations stay explainable?
The assistant can show why a SKU was picked — budget, weight, weather, in-stock — instead of a hidden score. Explainable shortlists are easier to trust and to debug. Hidden ranking with no reason is a poor default for this kind of product.