Lead Intake System

Most lead forms stop at collection. This project was about the workflow behind the form: what data matters, how the first pass should be accelerated, and where human operators need control after automation has done its part.

The product needed structured capture, AI-assisted qualification, owner routing, notification hooks, duplicate hints, and a review surface where the team could correct or override the system. The goal was not just speed. It was trustworthy momentum.

The portfolio version reconstructs the product as a proof chapter instead of embedding a real app. That made it possible to show the architecture through curated UI surfaces while still focusing on real workflow decisions and the operations logic underneath them.

This project captures the kind of work I want more of: websites and internal systems that connect presentation to actual operations. The form is just the beginning. The useful part is what the system does next.

The core problem was not simply collecting inquiries. It was preserving structure from the moment a lead entered the system so routing, qualification, notification, and follow-up could happen quickly without losing context. The product had to behave like an operating layer, not like an email inbox with a nicer coat of paint.

The intake form collects structured information about scope, urgency, business type, and timeline rather than relying on a single open text field. That structure gives the downstream workflow cleaner signals for assignment rules and review states. The database model is shaped around a lead record that can accumulate enrichment, notes, ownership, and activity history without splitting that information across disconnected tables.

Incoming leads move through an enrichment step that summarizes the inquiry, assigns a category, and estimates urgency. The important decision was to expose the reasoning alongside the score. The model assists the first pass, but operators can inspect why the lead landed in a certain tier before they accept or override the recommendation.

When the model's confidence holds, the auto-response sends. When it doesn't, the AI still drafts the reply, but it lands in a Review & send queue with the reason flagged for the operator. Every AI and human action (auto-classification, draft generated, status change, reply sent, follow-up queued) joins a single Activity ledger with the reasoning attached, not just the label. That is what keeps the handoff trail queryable and debuggable.

The ops layer compresses intake volume, queue health, and ownership into fast-reading dashboard views. The review surface keeps the lead record, notes, status controls, and manual overrides close together so the system stays accountable to human operators.