E-commerce Customer Support Chatbot
Instantly answers e-commerce queries by checking order status in Supabase and generating personalized product recommendations via OpenAI.
What it does
Get instant answers to your e-commerce questions as the agent checks your order status in Supabase and suggests personalized products powered by OpenAI. It handles support tickets automatically while keeping track of your conversation history for smooth follow-ups, delivering real-time replies that reduce wait times and improve satisfaction.
Step by step
- Configure the n8n webhook node to accept POST requests and parse the incoming JSON payload for user ID and query text.
- Set up the Supabase node with service key credentials to execute SQL queries against the orders table based on the extracted user ID.
- Connect the OpenAI Chat Model node using the API key and define the system prompt template that includes instructions for tone and order lookup behavior.
Where the LLM does the work
- Generate a dynamic system prompt that instructs the model to distinguish between support queries needing database lookups and general conversational requests.
- Design the response formatting logic so the LLM outputs structured JSON containing both the natural language reply and any necessary metadata for the webhook.
Watch out for
Context window limits can truncate conversation history during long threads, so implement a sliding window strategy in Simple Memory to keep only recent turns.
Tools that fit
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal E-commerce Customer Support Chatbot: Instantly answers e-commerce queries by checking order status in Supabase and generating personalized product recommendations via OpenAI. ## Specification - What it does: Get instant answers to your e-commerce questions as the agent checks your order status in Supabase and suggests personalized products powered by OpenAI. It handles support tickets automatically while keeping track of your conversation history for smooth follow-ups, delivering real-time replies that reduce wait times and improve satisfaction. - Trigger: Triggered by an event (Event · on customer inquiry via webhook or email) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - n8n: Workflow orchestration and automation logic - OpenAI Chat Model: Generating AI responses and product recommendations - Supabase: Storing order details and customer data - Webhooks: Receiving real-time customer inquiries ## Known pitfalls, handle each one explicitly in your implementation 1. Context window limits can truncate conversation history during long threads, so implement a sliding window strategy in Simple Memory to keep only recent turns. ## Reference implementation https://n8n.io/workflows/7256 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/e-commerce-customer-support-chatbot/ via usecasesforagents.com
Frequently asked questions
Can I use a different Automation Platform than n8n?
Yes. n8n is only the example automation platform in this recipe. The same flow works with Home Assistant. Swap the automation platform connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Also fits E-commerce Operator.