# Firecrawl Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/firecrawl
> Markdown URL: https://aitinkerers.org/technologies/firecrawl.md
> Technology record last updated: 2026-02-24T20:04:30Z
> Generated: 2026-08-26T08:20:56Z

Firecrawl is the Web Data API for AI, converting any website into clean, LLM-ready structured data with a single API call.

Firecrawl delivers reliable web data extraction, purpose-built for AI applications. The platform bypasses traditional scraping hurdles: it handles dynamic content, JavaScript-heavy pages, and anti-bot mechanisms, boasting coverage for 96% of the web. Developers use the API to convert raw URLs into clean, structured formats like Markdown or JSON, ensuring optimal context for large language models (LLMs). Key features include the `/extract` endpoint, which uses natural language prompts to pull specific, structured data from entire websites, and a powerful `/crawl` function for full-site data ingestion. This tool abstracts complexity: no rotating proxies or manual orchestration are required, accelerating development for AI agents and data pipelines.

- Official technology site: https://www.firecrawl.dev/
- Public AI Tinkerers demos and talks: 3
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From Cold Email to Trusted Conversation: How AI Agents Turn Outreach into Revenue](https://seattle.aitinkerers.org/talks/rsvp_Z5K4dyJQ7rE)

This talk covers the architecture of an AI-driven outbound system designed to create highly personalized B2B outreach at scale. I will walk through the design of two agent systems — IRIS, a hyper-personalized email generation engine, and Hermes, a social-channel warming system that leverages the psychological mere exposure effect to increase response rates. IRIS uses Firecrawl to extract structured information from target company websites, Perplexity for real-time research enrichment, and an LLM pipeline that synthesizes this context into personalized outreach messages rendered through React Email templates. The result is emails that read as if they were written by a human founder after researching the company. Hermes operates as a GUI-driven agent that manages warm-up sequences on social platforms, creating lightweight touchpoints before direct outreach. Together, these agents form a two-channel system designed to increase outbound effectiveness in trust-sensitive industries like healthcare. I will walk through the system architecture, data flow, and agent orchestration, including how contextual data is collected, synthesized, and translated into human-like messaging. I will also share early performance results showing how research-driven outreach can significantly outperform traditional cold email strategies.

- Event context: AI Tinkerers Seattle: GTM Track — March — 2026-03-26 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_Z5K4dyJQ7rE

### [AI-powered Inbound Lead Qualification](https://seattle.aitinkerers.org/talks/rsvp_QxPNK282fH4)

Vercel had 10 people doing inbound lead qualification. They automated it — and moved 9 out of 10 to outbound. Savings: $900K/year. The process they followed:  1. Shadow your best SDR for a few weeks  2. Document every decision they make  3. Ask "why" for each scoring choice  4. Turn it into an AI workflow I built an open-source skill for Claude Code that does this: /design-scoring — builds a scoring model for your business. Asks about your ICP, analyzes closed deals from your CRM, researches your website and competitors.  /qualify-lead — takes any inbound request (email, form, whatever format), finds the person on LinkedIn, scrapes the company website, applies your scoring model, and outputs: Hot / Warm / Cold / Disqualified + a draft response.

- Event context: AI Tinkerers Seattle: GTM Engineering Kickoff — Building AI for Growth — 2026-02-26 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_QxPNK282fH4

### [Automating Browser Usage with AI: Generating Onboarding Guides with AI](https://montreal.aitinkerers.org/talks/rsvp_0hYQ22b-SKk)

In this session, I’ll explore the cutting edge of AI for browser use in automating user onboarding. I’ll start with Browserbase, showing how AI-driven headless browsers with session replay and observability can capture onboarding flows and turn them into guides. Then I’ll look at Browser Use, an open-source project that applies AI agents and computer vision to dynamically control the browser. The demo will focus on creating on-boarding guides by asking AI to use a saas app like a human would. By experimenting with tools like Browserbase, Browser Use, Firecrawl, skyvern, I’ll show how the ecosystem is evolving to solve this problem. Rather than focusing on a single tool, this talk is an exploration of how AI-driven browser and computer use technologies can record user flows, adapt to UI changes, and automatically generate up-to-date onboarding experiences, comparing pros and cons.

- Event context: AI Tinkerers Montreal – September 2025 Meetup — 2025-09-24 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_0hYQ22b-SKk

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