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AI Detection of Business Signals: Building a Useful RevOps System

There comes a point where commercial databases are no longer enough.

They can tell you a company exists. They sometimes give you an industry, a size, a website, a LinkedIn account, a few contacts.

But they rarely answer the most interesting question:

Why does this company deserve to be contacted right now?

That question is what gradually led me to work on a broader topic: business signal detection using AI, scraping, automated workflows and some RevOps logic. Not to build a magic machine to sell. Not to replace salespeople. But to better prepare sales work, understand the movements of a company, and turn fairly noisy public data into actionable context.

This page is an entry point. It connects several technical experiments around scraping, competitive monitoring, expansion signals and semi-automated prospecting, and explains the overall reasoning behind these projects.

Redesigning dolist.com from the Ground Up

Some web projects start with a blank page.

This one started with four websites.

Four websites for a single company.
Four entry points.
Four ways of presenting offerings that, in reality, were all part of the same ecosystem.

On one side, there was dolist.com, the main site: the brand, the blog, resources, customer stories, a significant share of the historical SEO equity.

Alongside it:

  • a site for Campaign, the email and SMS activation platform;
  • a site for Welkom Editor, the email builder;
  • a site for Dolist services, covering consulting, Studio, deliverability, data, and training.

On paper, each site had its own logic.

In practice, for someone discovering Dolist for the first time, things could quickly get confusing.

  • dans Sales Ops
  • 10 min de lecture

The SDR Lead Machine

It started with a simple problem to solve: SDRs were spending too much time finding companies, identifying contacts, crafting messages, and following up manually.

The real problem wasn't the sales work itself. It was everything that comes before it: the preparation.

Building a Web Technology Detector with Scrapy

This project started in the simplest way possible: avoiding having to open 15 tabs manually just to understand a company's stack.

It's probably the project that taught me the most about real-world scraping, technical signals, the limits of the modern web, and the difference between a script that works and a system that holds up over time.