Replacing the Static FAQ Widget With a Sales Assistant That Actually Sells
Most ecommerce chat widgets are little more than a decorated FAQ page. The moment a shopper asks a real question — will this fit a narrow foot, will this work with what I already own — the bot stalls, and the shopper leaves to find the answer somewhere else, usually a competitor.
Why this keeps costing you
Every unanswered product question is a lost sale in progress. Generic scripted bots cannot compare products, handle objections, or reason about fit and compatibility, so they end up routing far too many shoppers to a support queue for questions a knowledgeable sales associate would answer in seconds. That gap between what a shopper needs to know and what the chat widget can actually tell them is where conversions quietly disappear.
How we build it
The product catalog — specifications, reviews, sizing guides, and policies — is embedded into a vector index and refreshed on a sync schedule tied to the store's inventory feed, so the assistant is never recommending something out of stock or citing a spec that changed last week. A retrieval layer pulls the relevant catalog passages for each question, and a generation layer, grounded strictly in that retrieved content, handles comparisons and objections rather than falling back to a canned script. The same conversation state carries across channels — website chat, WhatsApp Business API, and WordPress storefronts — so a shopper who starts on the website and continues on WhatsApp does not have to repeat themselves.
What this looks like once it is running
- 1Trained on the live catalog with real time inventory sync, never recommending an out of stock item
- 2Handles product comparisons and objection handling, not just scripted FAQs
- 3Deployed across website, WhatsApp Business, and WordPress from one conversation engine
- 4Guided handoff into checkout at the right moment in the conversation
- 5Conversation analytics that show exactly what shoppers are actually asking
Response times under two seconds and a genuinely helpful conversation, not a canned script, translate directly into fewer abandoned browsing sessions and a measurably higher share of chat conversations ending in a completed order.
For more details, click the relevant case study link below.
View Eva AI Sales Assistant case studyEva AI Sales Assistant (ElitePed)
This is the exact architecture behind Eva, Zaltech's AI sales assistant built for ElitePed, a Canadian research chemical company. Eva runs RAG powered product recommendations against the live catalog, integrates with WhatsApp Business API and syncs with WooCommerce, captures leads automatically from conversation context, and reports through a real time analytics dashboard. It is live in production today, holding sub two second response times across three or more channels.
More in E-commerce & Retail
The 70 Percent Problem: Recovering Revenue That Is Already Walking Out the Door
Objection-aware recovery copy generated from the actual cart contents, timed by intent score across email, SMS and WhatsApp — without training shoppers to wait for a coupon.
03Support That Never Clocks Out: Automating the Repetitive 80 Percent
Order status read live from the order management API, returns actioned within explicit policy limits, and anything outside those bounds handed to a human with full context.
04Selling Regulated Products Online Without Betting the Business on a Chatbot’s Mistake
A generate-and-verify dual agent architecture: an independent verification agent checks every draft against a configurable rule set, and a failed check is held for human review, never auto-corrected.
Want this one built for your business?
We will walk you through the architecture, what it takes to integrate with your systems, and a realistic timeline — before anyone signs anything.