What an eCommerce AI project includes
Aranv builds an AI shopping assistant, smarter product search and recommendations for online retailers and B2B sellers in the small and midsize range. Everything connects to your catalog, stock and order data, so shoppers get accurate answers and your store is ready for AI agents that shop on a buyer’s behalf.
Most of the work is product data. Titles, attributes, sizes, materials and compatibility notes need to be complete and consistent before any model can answer well. We audit the catalog, fill gaps with AI-assisted enrichment that a merchandiser approves, and set up a sync so catalog changes reach the assistant quickly.
The assistant and search run on your existing platform. We work with stores covered by our Shopify development and WooCommerce development teams, Magento sites and custom storefronts, using each platform’s APIs for catalog, inventory, cart and order status. The assistant lives on product and category pages, and general support questions go to your existing help channel.
How the assistant answers a shopper
Answers built from your catalog
- 01 Shopper describes a need A request like a rain jacket for cycling to work arrives in plain language.
- 02 Catalog searched by meaning Titles, attributes, specs and reviews are matched on meaning, not only exact keywords.
- 03 Live stock and price check Results are filtered by stock, variant and price from your store platform.
- 04 Answer with product links The shopper sees a short comparison with links to product pages and the cart.
The assistant only suggests products that exist in your catalog and are available to buy.
Where AI fits in an online store
Conversational AI for eCommerce is most useful when shoppers have a question your filters cannot answer.
Shopping assistant
Shoppers ask about fit, compatibility or intended use and get suggestions with reasons drawn from your product data.
Semantic product search
The search bar understands synonyms, misspellings and descriptions, so fewer searches end with nothing found.
Recommendation engine AI
Related items, bundles and reorder prompts come from what is bought together and what each shopper has viewed.
Agentic commerce readiness
Product feeds and structured data are cleaned up so AI assistants in search and chat apps can read and compare your range.
Catalog enrichment with merchandiser sign-off
AI fills gaps in titles, attributes and specs from supplier sheets and your existing descriptions, and every suggested change waits in a review list until a merchandiser approves it. Approved values are written back to your store platform, so filters, feeds and the assistant all read the same corrected data.
What we do
- Attribute gap report by category
- Suggested values with their source
- Bulk approve or reject
- Write-back to the store platform
- Change history per product
Limits on what the assistant says
The assistant names only products that come back from your catalog search and are in stock. We test it on real shopper questions, including awkward ones about rival brands, discounts and safety claims, and block the topics you do not want it to discuss.
What we do
- Answers limited to live catalog items
- Test set of real shopper questions
- Blocked topics you choose
- No made-up discounts or prices
- Handoff to your support channel
How we approach a project
Recommendations use your own order and browsing data. We start with simple, explainable signals such as items bought together and recently viewed, then test whether more advanced models add enough to justify their cost. Results are tracked as add-to-cart rate and revenue per session.
Agentic commerce is the newer piece. AI assistants in search and chat apps now read product data and, on some platforms, complete purchases for the shopper. Accurate feeds, product structured data and clear shipping and return policies make your products easier for them to find and trust. See eCommerce development for the store build itself and headless commerce for custom front ends.
How we work
- Only real, available products
- Product data cleaned first
- Results tracked in your analytics
Our Process
-
Catalog and search audit
We review product data quality, top search terms, zero-result searches and the questions your support team hears most.
-
Data cleanup and indexing
Attributes are normalized and enriched, then the catalog is indexed for meaning-based search and kept in sync.
-
Assistant and search build
We build the assistant and search interface into your theme and test answers against real shopper questions.
-
Split test and tune
We release to part of your traffic first, compare conversion with the old experience and adjust before full rollout.
Why Aranv for store AI
Product data fixed first
We spend the early weeks on attributes and feeds, because a clean catalog improves search, filters and the assistant at the same time.
Added to the store you have
Shopify, WooCommerce, Magento and custom storefronts all expose catalog, stock and order APIs, so the assistant joins your current store rather than forcing a rebuild.
Judged by a split test
Shoppers who use the new search or assistant are compared with those who do not, so the decision to expand rests on your own conversion figures.
Running cost sized to traffic
We estimate model and search costs from your sessions and query volume, and route simple queries to smaller models so cost per conversation stays visible.
Planning AI for eCommerce?
Book a 30-minute conversation with the team that would build it.
AI for eCommerce FAQs
-
What does an AI shopping assistant do that a chatbot does not?
A general chatbot answers questions. A shopping assistant knows your catalog, stock, prices and variants, and its job is to help a shopper choose and buy the right item. It links straight to product pages and the cart.
-
Will the assistant recommend products we do not sell?
No. It can only suggest items returned by your catalog search, and every answer is checked against live product data. If nothing fits, it says so and offers the closest options or a way to contact your team.
-
Does this work on Shopify, WooCommerce or Magento?
Yes. All three offer APIs for products, inventory and orders, which is what the assistant and search need. Headless and custom stores work too. Setup differs by platform, so we confirm the approach during the audit.
-
What is agentic commerce?
It is shopping carried out by an AI agent for a person: the agent searches, compares and in some cases checks out. For a store, being ready means accurate product feeds, structured data and policies an agent can read.
-
How do we know if it is working?
We compare shoppers who use the assistant or new search with those who do not, looking at conversion rate, average order value, search exits and support questions. Those figures decide whether to expand or change the feature.
-
What does an AI shopping assistant cost to run?
Running cost is mostly model usage per conversation plus search hosting, and it grows with traffic. We size it during the audit and use smaller models where they give the same answer quality.
Let’s talk about your next step
Contact Aranv to discuss your workflow, current tools, and goals. Meet with our team or send an inquiry.
Book a meeting with our team
Choose an available date and time for a 30-minute conversation about your project.
Tell us about your project
Share a few details so we can discuss a useful next step.
Prefer email? sales@aranv.com