Ledger Ai — Scalable AI Lead Extraction & Scoring Engine

Turning scattered web data into ranked, sales-ready leads.

Overview

Ledger Ai is a scalable lead-generation engine that automates one of the most time-consuming parts of sales prospecting: finding and qualifying leads. Built with Node.js and Puppeteer, it crawls dozens of online sources, extracts structured contact and company data, and applies an AI-driven scoring model to rank prospects by how likely they are to convert.

The goal was simple — replace hours of manual research with a pipeline that runs in minutes and hands sales teams a prioritized list instead of a raw spreadsheet.

The Problem

Manual lead generation doesn't scale:

Sales teams spend hours browsing directories, company sites, and social profiles just to build a prospect list.
Data comes back inconsistent — missing fields, duplicate entries, no clear priority order.
Even after collection, teams still have to guess which leads are actually worth pursuing first.

Ledger Ai was built to remove all three bottlenecks at once — collection, cleanup, and prioritization.

How It Works

The system runs as a five-stage pipeline, from raw web sources to a ranked output ready for outreach.

Source Discovery — Targets 50+ web sources including company sites, business directories, and public profiles.
Automated Scraping — Puppeteer drives a headless browser to navigate pages, handle dynamic content, and pull raw data reliably.
Extraction & Cleaning — A Node.js pipeline parses the raw HTML into structured records, removing duplicates and normalizing fields.
AI-Driven Scoring — An AI scoring model evaluates each lead against conversion signals and assigns a readiness rank.
Ranked Output — The final list is sorted by priority, so sales teams start with the leads most likely to close.

Key Features

Multi-source aggregation — pulls data from 50+ distinct sources in a single run.
High extraction accuracy — achieves ~90% accuracy on structured data extraction.
AI lead scoring — ranks prospects automatically instead of leaving prioritization to guesswork.
Headless automation — handles JavaScript-heavy, dynamically loaded pages without manual intervention.
Scalable design — built to add new sources or scoring criteria without reworking the core pipeline.

Tech Stack

Backend: Node.js
Automation: Puppeteer
Core Techniques: Web scraping, data extraction, AI-based lead scoring
Integration: REST APIs for feeding output into downstream sales/CRM tools

Impact

Aggregated data from 50+ sources in an automated run.
Reached 90% extraction accuracy, cutting down manual data-cleaning work.
Gave sales teams a ranked, ready-to-use lead list instead of an unsorted contact dump.