LeadFlow AI — Intelligent Lead Qualification & Follow-Up System

Role: Frontend Developer & Automation Workflow Designer Tools: HTML5 · CSS · JavaScript · SVG

A self-initiated frontend demonstration of an intelligent lead qualification and follow-up workflow. LeadFlow AI shows how incoming leads can be captured, reviewed, prioritized and routed through a structured qualification experience, with fictional sample data and simulated AI-style outputs.

LeadFlow AI explores how a small team could handle incoming enquiries more consistently. It combines a lead inbox, an inspectable qualification result, a seven-stage workflow walkthrough and suggested follow-up drafts. The implementation is a functional frontend prototype with fictional sample leads. It was not commissioned by a client.

Leads can arrive through several channels with different levels of detail and urgency. Reviewing each enquiry manually can leave teams without a consistent way to compare intent, decide priority and prepare a response. The design challenge was to make those decisions legible while preserving human judgment.

I organized the experience around the lead’s journey: capture, analysis, qualification, routing, response preparation and human review. A dashboard exposes the fictional pipeline, while detail screens keep the original inquiry beside the simulated analysis. Explicit score breakdowns make the illustrative rules inspectable instead of presenting an unexplained AI verdict.

The working demonstration includes search and priority filters, lead selection, local status changes, a fictional intake form, qualification explanations and an interactive workflow. Suggested follow-ups can be copied or marked reviewed locally. Nothing is delivered: the interface does not claim that a message has been sent or a CRM has been updated.

Searchable, filterable fictional lead inbox with calculated demo metrics.

Original inquiry, intent, score, priority and recommended next action.

Transparent scoring weights and thresholds.

Seven interactive workflow stages with restart and progression.

Suggested response drafts with a local review action and clipboard fallback.

Fictional intake form, session persistence and confirmed reset.

Responsive desktop, tablet and mobile layouts; mobile lead cards replace the table.

Semantic controls, labelled fields, focus styles, a skip link and native dialogs.

Semantic HTML5, CSS and vanilla JavaScript power a dependency-free frontend. SVG is used for the favicon. Hash-based navigation connects the screens. Rule-based scoring evaluates budget, timeline, workflow keywords and inquiry detail. Local rules classify intent and simulate priority routing; templates produce follow-up drafts. Browser Session Storage retains local demo changes, with a fallback when storage is unavailable. Reserved example.com addresses and explicit sample labels distinguish demonstration data from customer records.

This is a self-initiated fictional frontend portfolio demonstration, not a production AI automation system. There is no backend and no production integration with an external AI model, CRM, email service or database. Qualification, intent classification, routing and suggested responses use local rules and templates. Nothing is sent externally. Scoring criteria are illustrative and have not been validated against real sales outcomes.

Mobile images are responsive browser captures. No physical Android testing or accessibility certification is claimed. No client engagement, paid work, company adoption or business-result improvement is claimed.