Job-Hunting AI: Leads, Outreach and Proposals That Learn

What It Does

Lead engine. Searches OpenStreetMap against a saved ideal-customer profile, imports CSV with a preview that says why a bad row was refused, and reads each business's own website for a published email.

Qualification. A model scores fit with reasons, a pain guess and an opening line. Confidence is capped by how much is actually known, and the missing fields are listed.

Outreach and autopilot. A daily run finds new leads, drafts emails and pushes a summary to the phone. Nothing is sent until a person approves it, and approval is refused until a postal address is set.

AI receptionist. Sales, booking and support agents with their own prompts and tools, handing a caller across without asking their name twice. Also available as an MCP server and an embeddable widget.

Win Engine. Records proposal outcomes and shows what the winners had in common. It shows nothing until enough proposals are decided to mean something.

Plans and usage. Quota is reserved before AI work runs and refunded if the work fails.

Stack

Next.js 14 and TypeScript, Supabase with row-level security, BullMQ workers on Redis, Stripe billing, a Chrome extension for collecting job posts, and a provider-agnostic LLM layer. 177 automated tests.

A Note on the Image

The screens are redrawn from the code with sample businesses, not real ones. Contact lookup only finds published addresses, and scores are a model's opinion against the profile.