feedalyze.net: Shipping a Production AI Feature Solo

Retaining existing customers is the single most important lever for SaaS growth. Yet, most companies only realize an account is unhappy when the cancellation request arrives. We built feedalyze.net to solve this, creating an AI-driven system that monitors customer feedback channels to flag churn risks before they happen.

As a solo developer, building a multi-channel feedback parser required a highly efficient, automated architecture. Here is how we got it live.

The Brief

The goal was to build a system that acts as an automated customer success analyst. The app needed to process unstructured data from multiple sources and output a clear risk metric. The primary requirements were:

• Multi-channel parsing (emails, surveys, support tickets).
• A unified churn risk score per customer account.
• Automated sentiment analysis to track trends over time.
• A robust, multi-tiered subscription model.
• An alert system to notify team members of high-risk events.

The Scoping Phase: Narrowing Ingestion

In our first draft of the product brief, we wanted to build custom integrations for Zendesk, Intercom, Salesforce, Gmail, and Slack.

We quickly realized that building and maintaining five separate API integrations as a solo developer was an impossible maintenance burden. Each provider has unique auth flows, rate limits, and data formats.

We simplified the scope by building a single, universal webhook receiver. Instead of writing custom API integration code, we created an endpoint that accepts standard JSON payloads. Customers can route their tickets and emails to this endpoint using automation tools like Zapier or Make. This reduced our launch scope from weeks of API configuration to a single afternoon.

The Build

To manage the feedback pipeline solo, we relied heavily on serverless architecture and managed services. This minimized operational overhead and allowed us to focus on the core analysis logic.

graph TD
    A[Customer Channels: Email/Tickets/Surveys] --> B[Next.js API Routes]
    B --> C[LLM Sentiment & Classification Engine]
    C --> D[Supabase PostgreSQL]
    B --> E[Resend Alerts]
    D --> F[React Dashboard]

1. The Processing Pipeline

Customer interactions are ingested via webhook endpoints. Email sequences, support ticket text, and survey responses flow directly into Next.js API routes. These routes structure the text and dispatch it to an LLM middleware layer. The LLM evaluates the text for sentiment, detects churn signals, and returns structured JSON payload containing specific risk factors.

2. Scoring Algorithm

The risk factors are fed into a deterministic scoring algorithm. The algorithm combines the sentiment score with historical context (e.g., frequency of tickets, recent plan changes, and customer tier) to calculate a unified risk score. Keeping this step programmatic rather than purely LLM-generated ensures consistency and transparency in how scores are calculated.

3. Notification and Alerts

When a customer's score crosses a critical threshold, the system triggers an alert. We integrated Resend to dispatch structured emails directly to customer success reps, detailing why the account is flagged and providing a direct link to the interaction log.

The Result

The completed MVP of feedalyze.net successfully calculates a churn risk score from 0–100 per customer account.

To make onboarding seamless, we launched with a $0 free tier available, alongside a subscription structure consisting of 5 pricing tiers scaling up to $399/mo for enterprise teams.

The product includes automated email parsing, NPS survey analysis, support ticket sentiment classification, and QA audits, giving companies a complete picture of customer satisfaction.

Handling Serverless Timeouts

One of our biggest technical hurdles was Next.js serverless timeouts. Standard serverless endpoints on Vercel have a 10-second execution limit on hobby plans. Analyzing multiple emails and calling LLM APIs often took 12–15 seconds, causing our webhooks to fail.

We resolved this by making our processing pipeline asynchronous. When a webhook arrives, we save the raw text to Supabase and return an immediate 202 Accepted response. A background serverless worker (triggered by a Supabase database webhook or queue runner) then picks up the record and runs the LLM analysis out-of-band. This ensured that our ingest endpoint never timed out and could handle sudden spikes in user traffic.

What This Means for Your Project

Shipping complex AI features solo does not require a large engineering team or months of development.

By building on top of pre-trained LLM models and using serverless functions to orchestrate webhooks, you can ship production-ready sentiment engines in weeks. The key is automation: let webhooks ingest the data, use standard APIs to parse it, and send targeted alerts to ensure the findings are actionable.

Focus your development on building a reliable ingestion pipeline. Once the data flows cleanly, the AI can handle the analysis.