Building an Automated AI Quote System: Transform Leads into Revenue with n8n and Replit

In the world of service-based businesses—whether logistics, agencies, legal firms, or construction—speed is the ultimate competitive advantage. I’ve seen countless businesses lose hundreds of thousands of dollars simply because they were too slow to respond. A potential client emails a request, the business owner spends 20 minutes calculating prices across spreadsheets, and by the time they hit “send,” the lead has already signed with a competitor who responded in five minutes.

The solution isn’t just to work faster; it’s to build an Automated AI Quote System.

In this guide, I’m going to walk you through how to architect and build a complete end-to-end AI proposal agent. This system monitors inboxes, classifies requests, calculates custom pricing based on historical data, and feeds everything into a professional dashboard for one-click approval.

This moves beyond simple backend automation. We are building a full-stack product using n8n for the workflow, Supabase for the database, and Replit for the user interface. This is the difference between a $5,000 automation script and a $25,000 enterprise solution.

The Architecture of a High-Value AI System

To solve the “speed-to-lead” bottleneck effectively, we need three distinct layers working in harmony. Most developers stop at the backend, but to truly serve a client, you need a frontend that their team can actually use.

Here is the high-level stack:

The Backend (n8n): This acts as the brain. It handles the logic, AI processing, email monitoring, and third-party integrations.
The Bridge (Supabase): This is our central database. It stores the state of every quote, historical pricing data, and client details, acting as the sync point between the backend and frontend.
The Frontend (Replit): This is the dashboard. It allows a human decision-maker to review the AI’s work, edit pricing, and hit “Approve” or “Decline.”

By combining these three, you create a system where the AI does 90% of the heavy lifting, and the human expert spends 30 seconds doing the final 10%—quality control.

Download workflow n8n: https://romhub.io/n8n/Data_Analysis_Agent

Step 1: The Backend Logic (n8n)

The workflow starts with the trigger. We utilize a dual-path ingestion system to capture requests from two primary sources: direct emails and website forms.

1. Ingestion, Filtering, and Normalization

When an email arrives (via a Gmail trigger monitoring the inbox every minute), we can’t assume it’s a lead. It could be a newsletter, a bill, or spam.

I use an AI node (OpenAI’s GPT-4o) to act as a Smart Filter. The prompt analyzes the incoming email and returns a JSON object containing a boolean value (is_quote_request) and a confidence score.

Routing Logic: If the confidence is below 60% or it’s not a quote request, the automation stops immediately to save costs.
Data Normalization: If it is a legitimate request, it merges with the secondary path (Webhooks from a website form). We use a Code Node to “normalize” the data, ensuring that whether the lead came from a structured form or a messy email, the downstream AI sees the same data structure.

2. Intelligent Extraction & Classification

Next, we turn unstructured text into structured business intelligence. Using an AI extraction node, we pull out specific fields:

Contact Info: Name, Company, Email, Phone.
Project Details: Service type (e.g., freight, warehousing), Timeline, Budget range.
Scoring: The AI assigns a Complexity Score (1-10), an Urgency Level, and a Qualification Score (0-100) based on how complete the request is.

We immediately save this initial record to Supabase with a status of PROCESSING to ensure no data is lost.

3. The RAG Pricing Engine (Historical Context)

This is where the system becomes incredibly valuable. Instead of hallucinating a price, we use RAG (Retrieval-Augmented Generation).

The workflow queries Supabase for the last 10 similar projects where the status was WON. This gives the AI context: “Last time we did a ‘Last Mile’ logistics job like this, we charged $10,500.”

4. AI Pricing & Draft Generation

We feed the current request details, the historical “winning” data, and specific business rules (base rates, urgency multipliers) into an AI Pricing Calculator.

The AI generates a Three-Tier Pricing Strategy:

Good: Basic service (0.8x calculated price).
Better: Standard + extras (Recommended).
Best: Premium all-inclusive (1.4x price).

Simultaneously, a separate AI node drafts a professional, friendly email response incorporating these prices and specific implementation details.

5. Updates and Notifications

Finally, the workflow updates the Supabase record with the calculated prices, the draft email, and changes the status to PENDING_REVIEW.

High-Value Alert: If the AI estimates the value is over $10,000, it triggers a specific Slack Notification to the sales team, linking them directly to the dashboard.

Step 2: The Frontend Dashboard (Replit)

The biggest mistake automation engineers make is forcing clients to log into n8n or look at raw database rows. Clients want a product. They want a dashboard.

Using Replit, we build a React + Tailwind CSS web application that connects to our Supabase project.

Key Dashboard Features

Your dashboard needs to solve the “visibility” problem. It should include:

The Kanban/Grid View: Display all incoming quotes with status cards (e.g., “High Urgency,” “Pending Review”).
Detailed Review Pane: When a user clicks a quote, they see the extracted data side-by-side with the AI’s generated draft.
Pricing Editor: The user can manually override the AI’s “Good/Better/Best” prices if they have offline context (e.g., “This client is difficult, add 10%”).
One-Click Actions:
- Approve & Send: Triggers a webhook back to n8n.
- Edit: Allows modifying the email draft before sending.

Step 3: Closing the Loop (The Sending Workflow)

When the user clicks “Approve and Send” on the dashboard, the frontend does not send the email directly. Instead, it hits a specific “Send Quote” Webhook in n8n.

This secondary workflow is robust and secure:

Receive Webhook: Catches the quote_id, recipient_email, and the final_email_body passed from the dashboard.
Send Email: Uses the Gmail node to send the email as the business owner (from their connected account).
Update Database: Updates the Supabase record status from PENDING_REVIEW to SENT and logs the timestamp.
Feedback: Returns a success JSON response to the dashboard so the UI updates instantly.

This structure ensures that the business owner maintains control. They trust the system because they verify the output before it leaves the building, yet they spend 60 seconds on a task that used to take 20 minutes.

Why This Approach Commands Premium Pricing

If you are in the business of selling AI solutions, understanding the difference between a “workflow” and a “system” is how you move from charging $5,000 to charging $25,000.

Solving the Real Bottleneck

When I spoke to a logistics company owner recently, he wasn’t looking for “AI.” He was drowning in email. He was losing deals because by the time he copied data to a spreadsheet, checked three different vendor platforms, and typed out a quote, the customer was gone.

If I had pitched him “email automation,” he might have paid a few thousand dollars. Instead, I pitched him a revenue recovery system.

I listened to his pain points:

Pain: “I miss important emails in the noise.” -> Solution: AI Filtering & Slack Alerts for High-Value ($10k+) deals.
Pain: “Quoting takes too long.” -> Solution: RAG-based AI pricing using his own historical data.
Pain: “I’m losing deals to faster competitors.” -> Solution: Reducing response time from hours to under 60 seconds.

The Value of the Interface

Clients do not want to see how the sausage is made. They don’t want to maintain a Node.js script. By delivering a polished frontend (via Replit) alongside the backend automation (n8n), you are delivering a software product tailored specifically to their business logic.

Conclusion

Building an automated AI quote system is about more than just stringing together API calls. It’s about creating an architecture that mimics the decision-making process of a human expert but at the speed of software.

By utilizing n8n for the complex logic and integrations, Supabase for robust data handling, and Replit for a professional user interface, you can build systems that fundamentally change how a business operates.

Whether you are building this for your own company to reclaim your time, or offering it as a service to clients, the impact is measurable: faster responses, consistent pricing, and ultimately, more deals closed.