Monitor site visitors, capture leads before chat, log conversations and leads in connected tools, and notify teams with personalized follow-ups, all via a single embeddable AI agent.
The AI agent embeds on your site and uses Gemini with Supabase memory to craft contextual replies. It pulls real-time information via SerpAPI to ensure accuracy. Lead data, chat summaries, and events are logged to Google Calendar and Sheets, while personalized follow-up emails are sent via SMTP.
Embeds on any site and handles lead capture while delivering context-aware responses.
Capture visitor name and email before chat begins.
Initialize a session with memory stored in Supabase.
Query SerpAPI for real-time information during conversations.
Generate responses using Gemini with retrieval-augmented context.
Log new leads and chat summaries to Google Calendar and Sheets.
Send personalized follow-up emails via SMTP after chats.
This AI agent replaces scattered lead collection and generic chats with a unified, memory-enabled workflow. It centralizes data, speeds up engagement, and automates follow-ups.
A simple 3-step process to deploy and operate on any site.
The widget sends user input to a production n8n webhook and routes it to the AI agent for processing.
The AI agent retrieves memory from Supabase and uses retrieval-augmented generation to craft a contextual reply.
The user sees an instant answer while the system logs the lead data, chat summary, and triggers follow-up actions via SMTP.
A realistic scenario showing time to value.
Scenario: A new visitor lands on a pricing page, enters name and email to start a chat, asks about discounts. The AI agent references real-time pricing data via SerpAPI, provides a tailored quote within 2 minutes, logs the lead in Google Sheets, creates a calendar event for a follow-up, and sends a personalized thank-you email via SMTP within 5 minutes.
Ideal roles that gain measurable results from this AI agent.
Need embedded chat to capture qualified leads on any page.
Require immediate context for faster, personalized outreach.
Want a turnkey, brandable chat widget across sites.
Need easy customization and branding controls.
Capture product-page leads and schedule follow-ups.
Convert readers with on-page lead capture and follow-ups.
Tools wired into the AI agent to automate memory, search, and outreach.
Performs real-time web searches to inform AI responses during chats.
Stores conversation memory and retrieval context for RAG.
Logs leads and schedules follow-ups as events.
Keeps structured records of leads and chat transcripts.
Sends personalized follow-up emails after chats.
Orchestrates webhook, AI processing, memory, and integrations.
Practical scenarios that demonstrate the agent’s value.
Common questions about deploying and using the AI agent.
To deploy the AI agent you need a site (any HTML, WordPress, or CMS), a production webhook URL, and credentials for SerpAPI, Supabase, Google Calendar, Sheets, and SMTP. The setup is designed to be web-friendly and requires only basic configuration values: the webhook endpoint, branding options, and embedding code. Once connected, the widget loads on page load and starts capturing leads before chat. The memory and RAG context are stored in Supabase, which enables more accurate replies over time. You can customize the embed snippet to match your branding and positioning.
Yes. The widget uses a universal embed snippet that works on custom HTML sites and popular CMS platforms like WordPress. You simply paste the snippet into your page head or footer and configure branding, webhook, and colors. The agent then loads automatically on every page where the snippet is present. Ongoing updates to the embed script are hosted in your own repository, so branding and behavior stay in sync. If you switch domains, you can reconfigure the webhook without changing the site code.
Data security depends on how you configure Supabase, SerpAPI keys, and SMTP credentials. Memory stored in Supabase is isolated per session and can be restricted with access controls. You should implement standard privacy practices and consent collection for visitors. The architecture does not transmit sensitive information to third parties beyond configured integrations. You can review data retention settings in your Supabase and SMTP configurations.
Yes. The embed supports branding options such as logo, name, welcome text, and response time messaging. You can adjust colors, position, and typography to match your site. You can also customize response prompts and the tone of the AI agent to reflect your brand voice. Branding changes propagate through the widget without modifying the core workflow.
Leads are captured with name and email and stored in your connected tools (Google Sheets, Google Calendar) for easy retrieval. The session transcripts and key metadata are logged, enabling efficient follow-up and reporting. Access to data is controlled by your platform’s permissions and the integrations you enable. You can export data from Sheets or query it from Calendar as needed. Real-time updates ensure your team sees fresh information on every new chat.
If SerpAPI returns outdated or low-confidence results, the agent relies on its memory and other available context to avoid incorrect information. You can configure the freshness window for retrieved data and implement fallback sources when necessary. The system also logs the source and confidence level of fetched data for auditing. Regular updates to the knowledge base and prompts help keep the agent accurate. You can also manually update critical data via the admin interface.
Yes. The architecture is designed to be modular: you can connect additional data sources, CRMs, or messaging channels through n8n. New integrations share a common interface and memory model so context is preserved. You should plan your data mapping and access controls when adding tools. The UI and embed can be extended to accommodate extra capabilities without breaking existing flows.
Monitor site visitors, capture leads before chat, log conversations and leads in connected tools, and notify teams with personalized follow-ups, all via a single embeddable AI agent.
