Chaterimo com

Chaterimo is an AI customer service platform for e-commerce scenarios, with the core goal of using AI to help customers find products and answer pre-sales and after-sales questions. It is powered at its foundation by large language models such as ChatGPT, Claude, Gemini, and Grok, and the company emphasizes that its capabilities are built on advanced product RAG (retrieval-augmented generation).

Purpose and Positioning Chaterimo is mainly used as an e-commerce AI customer service chatbot and can be deployed on a store website, FAQ, or support page. The company says it is suitable for online stores that want to quickly launch AI customer service, reduce manual waiting, and let customers complete product discovery and order inquiries within the conversation.

Main Capabilities

1. Deep product search: deeply parameterizes product attributes, combining structured attributes with normalized values and units; uses hybrid parallel retrieval of semantic search, keywords, and exact product lookup, and prioritizes results that match customer needs through reranking.
2. Agentic search: the AI can autonomously choose retrieval paths and, when information is insufficient, continue searching for missing details before answering.
3. Product recommendations and comparisons: provides recommendations and comparisons based on specifications, prices, and inventory information in the catalog.
4. Order support: after connecting to platforms such as Shopify and Shoptet, it can perform order tracking, delivery estimates, return process guidance, and access order history to provide contextual support. The company mentions that PrestaShop 8.2 can use read-only order queries, verified by order reference number and email.
5. Ticketing system: when the AI cannot resolve an issue, it can automatically collect contact information and create a ticket, with support for viewing the full conversation history, tracking resolution time, customer service performance, and customer satisfaction.
6. System prompt control: the core instructions guiding the LLM can be edited, setting the name, role, identity, tone, rules, and escalation rules for each agent, creating support-type or sales-type agents separately.
7. Multi-channel access: supports the classic floating bubble, full-page integration, and can connect to WordPress, Facebook Messenger, and workflows via Make.

How to Use According to the company, the onboarding process is simple: platforms such as Shopify, Shoptet, and Upgates can be installed with one click; PrestaShop requires enabling Webservice in the backend, creating an API Key and allowing only GET access to orders, customers, order_states, currencies, and then in Chaterimo's Integrations → PrestaShop, enter the HTTPS store URL (without /api/) and API Key to verify and save. The knowledge base can be built by directly pasting text, scraping web page content, uploading CSV, Excel, Word, PDF, or plain text files (less than 10MB); merchants using Google Shopping can also directly upload an XML product feed. The company says the first conversation can start in an average of 30 seconds.

Pricing and Integrations Chaterimo offers flexible pricing, allowing users to use their own OpenAI, Anthropic, Google Gemini, or xAI Grok API Key, pay based on actual usage, and enjoy unlimited AI messages; the platform itself charges a separate monthly fee. The company says that compared with competitors charging more than $50-100 per agent per month and limiting AI messages, Chaterimo has no hidden fees and no per-conversation charges.

Applicable Scenarios and Limitations It is suitable for scenarios such as e-commerce product inquiries, order inquiries, return guidance, and multilingual customer service. The company mentions that after integrating order data, customer feedback showed a 40% increase in issue resolution speed. In terms of limitations: the PrestaShop integration requires configuring Webservice yourself and may require assistance from the hosting provider to handle the Authorization header or URL rewriting; knowledge base files must be less than 10MB; when using your own API Key, you need to create and integrate the key yourself. Some FAQ questions (such as the specific selectable Claude models, other LLM models, and how to create multilingual multi-component setups) do not have clear answers in the provided materials and are not yet available.