Open Devdocs VS Emporiqa

Emporiqa's answer:

Online store owners and e-commerce agencies running Drupal Commerce, Magento 2, PrestaShop, Sylius, Shopware, or WooCommerce. Mid-size to large catalogs (2,000 to 100,000 products) where keyword search stops working and shoppers need a salesperson.

Emporiqa's answer:

It's the AI chatbot that closes sales. It behaves like an online salesperson: recommends a specific product from your catalog, handles objections on price and fit, suggests variations, and walks shoppers to checkout. Not a FAQ bot that deflects. Ask it for a product your store doesn't sell and it says so, then shows what you do carry. Search expands the shopper's question with a language model, matches it against vector embeddings of your catalog, then reranks with a cross-encoder, handling both natural-language questions and exact part-number lookups. Same salesperson behavior across 65+ languages without a translation layer.

Emporiqa's answer:

Pay-as-you-go: $0/month base + $0.25 per conversation, with $25 of signup credit (~100 conversations) and a $59/month default cap you can adjust. A conversation counts only when a shopper writes to the chat, so you pay only when the chat talks to a shopper. If it never talks to a shopper, you never pay. Unlimited team members on every account. Native plugins for six e-commerce platforms: WooCommerce (WordPress), Magento, PrestaShop, Drupal Commerce, Sylius, and Shopware 6 (Composer/GitHub), plus a webhook API for anything else. And the chatbot closes sales: it names a product from your catalog, handles the objection, and adds it to the cart.

Emporiqa's answer:

For over 15 years I built software, first as an engineer and then as a CTO, and I kept running into the same gap with online stores. Walk into a physical shop and a good salesperson knows the products and helps you choose. Online, the shopper is alone with a search box that returns 200 results or none. I built Emporiqa to put that salesperson on the website. Ask it for a product you don't sell and it says so, then shows what you do carry, instead of inventing one or quoting a price you don't have. It recommends the right item, handles the objections that stall a sale, and walks the shopper to checkout. It also shows which chats turned into orders, so you can watch it earn its keep.

Emporiqa's answer:

Django, LangGraph, Qdrant (vector database), PostgreSQL, Redis, Celery. Search embeds the catalog with OpenAI text-embedding-3-small, matches it in Qdrant, then reranks the shortlist with Cohere rerank-v3.5 through OpenRouter. Qdrant is the open-source build, running on our own infrastructure.