AI for ecommerce that cleans the catalog and clears the queue
Custom AI software for catalog, search, returns, and fraud, wired into the storefront and ERP you already run. Fixed cost, 4 to 12 weeks, and you own the build.
How it works
Online sellers rarely lose money on the storefront. They lose it in the work behind it: thin product data that never ranks, a search bar that returns nothing for half its queries, a returns desk staffed to handle whatever arrives, and fraud caught only after the chargeback. APPIT builds custom AI software for those operations, connected to your platform, PIM, and ERP, so catalog work, discovery, post-purchase handling, and risk screening run without adding headcount every time volume steps up.
Catalog data that enriches itself
Supplier feeds, spec sheets, and product images become structured attributes and merchandising copy in your voice, at whatever volume the catalog throws at you.
- Attribute extraction from PDFs, spreadsheets, and product photography
- Titles, bullets, and metadata generated against your brand and SEO rules
- Duplicate and variant collapsing across overlapping supplier feeds
- Taxonomy mapping to Amazon, Google Shopping, and marketplace schemas
Search and recommendations tuned to margin, not just clicks
Discovery is rebuilt around what a shopper meant and what you can profitably ship, with the results measured on revenue per session rather than click-through.
- Semantic search that handles misspellings and natural-language queries
- Zero-result recovery with substitutions from in-stock inventory
- Recommendations weighted by margin, stock cover, and return rate
- Built-in A/B harness so every ranking change is proven before rollout
Returns and fraud resolved before an agent is involved
Policy decisions, refund routing, and risk scoring happen automatically, and only the genuinely ambiguous cases reach a person with the evidence already assembled.
- Return requests adjudicated against policy, including photo condition checks
- Automatic refunds, exchanges, and label generation inside your rules
- Risk scoring at checkout and again at return to catch serial abuse
- Chargeback evidence packets compiled and filed on time, every time
Where teams use it
Built for real revenue work.
Enrichment for a 90,000-SKU distributor catalog
Supplier data arrived in twelve different formats and half the listings had no usable attributes, so filtered browsing barely worked. An enrichment pipeline now normalizes incoming feeds, fills missing specs from source documents, and flags only the products where confidence is too low to publish.
Support deflection on order status and changes
Where-is-my-order, address edits, and cancellation requests made up most of the ticket queue and all of the seasonal hiring pressure. An operations agent now reads live carrier and OMS data, answers or actions the request in the customer's channel, and escalates with full context when a human is genuinely needed.
Return abuse and payment fraud screening
Wardrobing and empty-box returns were absorbed as a cost of doing business because nobody could prove a pattern. Behavioral scoring across accounts, devices, and return histories now flags repeat offenders for review and adjusts the refund path automatically.
“We were about to hire six more support agents for peak season. Instead the automation handles order status and returns end to end, and the team we already had spent Q4 on the complicated cases rather than copying tracking numbers.”
FAQ
Questions, answered.
How is AI used in ecommerce beyond a chatbot on the storefront?
The highest-return work is usually invisible to the shopper. Product data enrichment makes a catalog findable, search and recommendation models decide what gets seen, returns adjudication and fraud scoring protect margin after the sale, and service automation absorbs the volume those systems generate. A conversational layer is often part of the build, but it is the last piece, not the project.
Do we need to move off Shopify, BigCommerce, or our current platform?
No. We build around your platform through its APIs and webhooks, and integrate with the PIM, OMS, ERP, helpdesk, and payment stack sitting behind it. Shopify Plus, BigCommerce, Magento, WooCommerce, and custom headless storefronts are all normal starting points. Nothing we deliver requires a replatform, and everything we write is yours at handover.
How do you stop AI from publishing wrong product information?
Every enrichment step carries a confidence score and a source reference back to the supplier document or image it came from. Anything below your threshold goes to a review queue instead of the live catalog, and we run automated evaluations against a labeled sample before each model change ships. For regulated categories we hard-block generation on attributes like compliance claims and dimensions.
What does an ecommerce AI project cost and how fast can it go live?
A focused build such as catalog enrichment or returns automation typically runs $45,000 to $95,000 and ships in 4 to 6 weeks. Broader programs combining search, service deflection, and fraud screening usually land in the 8 to 12 week range. You get a fixed project cost, a named team, and a delivery date before anything is signed.
Keep exploring
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