AI-агент для квалификации лидов

Whenever we look for software to automate lead gen, we usually start by automating the wrong part of the funnel where the default playbook is typically: scrape a list of emails from a directory, plug it into an automated sequencer and blast 1000 messages a day. By week three domain deliverability crashes, reply rates hover below 1% and the team is back to square one. If you look at where modern lead gen automation works, the challenge is usually the automating the intent detection and context layer before outreach ever happens. In practice, the software approaches generally split into two main architectures: The dedicated intent & signal engine (Scale Intelligence): Instead of starting with static contact lists, this approach automates the timing and discovery process. Scale Intelligenc…

I’m trying to design an automated sales funnel for a consumer internet/telecom service, without relying heavily on sales reps manually handling every lead. The acquisition channels would be **Facebook, Instagram and TikTok**. The basic idea is: **Social media content/ads → prospect sends a message → automated qualification → recommend the appropriate plan → collect customer information → schedule/coordinate installation → follow up automatically.** I’m interested in automating as much of the process as realistically possible. For example: How would you capture leads from FB/IG/TikTok? Would you send everyone to WhatsApp, Messenger, or keep the conversation inside the platform? How would you qualify someone automatically? (location, coverage, budget, household needs, etc.) …

When teams talk about automating lead gen, 90% of the time they just mean automating cold email sending so they set up 10 domains, buy a static database of 5000 contacts, connect a sequencer and hit send. Within a month, bounce rates spike, domain health drops and reply rates sit below 1% cause the outreach has zero timing context. Real lead gen automation works much better when you automate timing detection and intent discovery before any message is sent and the software landscape for this generally splits into two approaches: 1, Autonomous intent & signal discovery (Scale Intelligence): this approach automates the research and discovery layer where Scale Intelligence monitors 75+ data sources across technical and social channel to resolve fragmented public intent back to verified compa…

This sounds like a problem that could probably be reduced by scoring the leads before doing the manual research. Things like company fit, buying intent, urgency and whether there's an actual pain point could be used to prioritise the list, so you're not spending the same amount of time researching every company. I've actually been experimenting with a system around this problem recently, and one thing I found useful was keeping the scoring rule-based so you can see exactly why a lead was prioritised rather than just getting an AI-generated score.