Industries

AI for Retail & E-commerce

AIYatch helps retailers and e-commerce brands raise conversion and cut cost-to-serve with AI: support and shopping assistants across web, WhatsApp and voice, personalised recommendations and search, demand and stock forecasting, and review mining. We integrate directly with Shopify, WooCommerce, ERPs and helpdesks so results show up in the tools your team already uses.

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Typical challenges

Support tickets about orders, returns and delivery repeat constantly and peak in seasonal spikes.
Shoppers abandon when search and recommendations do not understand what they mean.
Over- and under-stocking erode margin because forecasts are built in spreadsheets.
Reviews, chats and social feedback contain insight nobody has time to read.

Typical use cases

Order-status and returns assistants

Chatbots and voice agents that check order status, start returns and answer policy questions by talking to your store and logistics APIs, escalating edge cases to humans.

Conversational shopping and product advice

LLM assistants that recommend products from your catalogue, compare options and answer sizing or compatibility questions with grounded, up-to-date data.

Semantic search and recommendations

Vector search and recommendation engines that understand intent, handle typos and long queries, and personalise results by behaviour.

Demand and stock forecasting

Time-series models by SKU, store and channel that inform purchasing, replenishment and promotion planning.

Review and feedback mining

NLP pipelines that classify sentiment, extract product issues and surface trends from reviews, chats and surveys into dashboards.

Marketing content generation

Generative AI that drafts product descriptions, campaign copy and localised variants inside brand guidelines with human review.

Questions from this sector

Yes. We build one assistant with a shared knowledge base and connect it to multiple channels (website widget, WhatsApp, Instagram, Messenger, email and voice) with channel-specific formatting. Conversation history and hand-off to human agents are consistent across all of them.

Semantic search maps queries and products into the same vector space, so “warm jacket for hiking in rain” finds relevant items even without matching keywords. Combined with keyword search, filters and behavioural signals, it improves relevance, reduces zero-result searches and lifts conversion.

It depends on data history, seasonality and how many SKUs have sparse sales. We benchmark against your current method during discovery and report error metrics honestly; the typical goal is a measurable reduction in forecast error and stock-outs, not perfection.