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3-phase verify · search → enrich → CRM

Turns hours of manual prospecting per rep into one pipeline: natural-language search → three-phase verification → AI enrichment → CRM export.

AI/ML
Engineering
Product
Founder
2025
Next.js
TypeScript
OpenAI / Anthropic / DeepSeek
Puppeteer
Stripe
HubSpot / Salesforce

The whole point of LeadForager is to collapse the unglamorous 80% of prospecting — search, verify, enrich, export — into a single pipeline a rep can trust without hand-checking every row.

The problem

Reps waste hours per prospect on research: discovering local businesses, confirming each is real, active, and on-criteria, then transcribing the keepers into a CRM. Verification is where the time goes. A listing can look perfectly valid and still be closed, duplicated, or a poor fit — and getting that wrong pollutes the pipeline downstream.

Solution & demo

LeadForager treats the lifecycle as one flow — Results → Cold Leads → Verified → CRM — with bulk operations, CSV import and export, and subscription tiers with usage-based limits. A natural-language request parses into a structured search; candidates are verified; survivors are AI-enriched with a 0–100 score across website quality, tech gaps, business signals, and market opportunity, plus a drafted outreach email; and verified leads sync to HubSpot, Salesforce, or Pipedrive with field mapping and deduplication.

Recorded walkthrough: pick a sample business, watch the three verification phases run with real timings, then see the AI enrichment card — a lead score plus a generated outreach email — on a fixed sample dataset. No live map spend.

Recorded walkthrough coming soon.

In the meantime, the architecture and tradeoffs below tell the full story — or start a conversation and I'll walk you through it.

Recorded walkthrough: pick a sample business, watch the three verification phases run with real timings, then see the AI enrichment card — a lead score plus a generated outreach email — on a fixed sample dataset. No live map spend.

Live “Try it” demo lands in a later release. For now, the recorded walkthrough above is the demo.

How it works

LeadForager pipeline. A natural-language query parses into a structured search. Candidates enter a three-phase verification engine: static DOM analysis first, then a headless browser render for JavaScript-heavy sites, then an AI analysis fallback for the ambiguous remainder. Verified leads flow into AI enrichment for a 0-to-100 score and a drafted outreach email, then export to a CRM.
The three-phase verification engine is the core: cheap checks first, expensive checks only where they are needed.

The verification engine runs in escalating tiers. Static DOM analysis (roughly 1–2s) handles the common case for almost nothing. Sites that render their content with JavaScript escalate to a Puppeteer browser render (roughly 5–15s). Only the ambiguous remainder falls through to an AI analysis pass. Criteria are weighted and configurable with confidence thresholds, and a live test harness lets you tune them against known-good and known-bad examples.

Above that sits a provider-agnostic AI layer — OpenAI, Anthropic, and DeepSeek behind one interface, with proxy-endpoint support for security and compliance — used for the enrichment scoring, the personalized outreach drafts, and an embedded assistant. Underneath is the ordinary product surface that has to be right: auth, billing, rate limits, API keys, input validation, and session management.

Tradeoffs & lessons

Links

The demo above is the recorded walkthrough; a public repo and a deeper write-up are being prepared and will appear here as they are published.