A real estate agency receives 200+ incoming leads per month.

Managers spend 10–15 minutes on each one: finding out budget, area, timeline, mortgage or cash, whether they have a property to sell. Half of the leads are unqualified. But you only find that out after spending time on them.

Sound familiar? Here's how it was solved 👇

😩 What was happening:
→ 3 managers processed leads manually
→ Average time to first contact: 2–4 hours ⏳
→ 40–50% of leads dropped off without waiting for a response
→ Hot clients got lost among cold ones
→ The manager couldn't see which stage each lead was at

🛠 What was built:
An AI agent in Telegram and on the website. The client submits a request — the bot starts a conversation within 8 seconds ⚡️ Not a 20-question form, but a live conversation: asks 5–7 questions, adapting to answers.

If the client says "a two-bedroom on the secondary market up to 12 million in the Southern Administrative District" — the bot clarifies floor, renovation, timeline.
If "I want to look at something" — it gently finds out budget and motivation.

The output is a lead card with a score:
🔴 Cold — "just looking," budget not defined
🟡 Warm — has parameters but not ready to deal within the next month
🟢 Hot — specific request, budget, timeline

Hot leads go instantly to CRM with a notification to the manager 🔔 Warm leads enter an automatic nurturing funnel. Cold leads receive useful content once a week.

⚙️ How it works technically:
→ AI agent based on GPT + custom prompt tailored to the real estate market 🤖
→ n8n connects the bot with amoCRM: creates a deal, assigns a tag, assigns a responsible person
→ Scoring based on 5 parameters: budget, timeline, request specificity, pre-approved mortgage status, motivation
→ Branching logic — not a linear questionnaire, but a scenario tree 🌳

📊 Results after the first month:
✔️ First contact time: from 2–4 hours → 8 seconds
✔️ Managers only work with warm and hot leads — minus 60% of empty conversations
✔️ Conversion to viewing increased by 35% 📈
✔️ Manager sees a dashboard: how many leads, what quality, where the bottleneck is
✔️ Savings: ~45 hours of manager time per month 🕐

💡 Key insight:
The problem wasn't the number of leads. There were enough leads. The problem was managers spent the same amount of time on a client with a budget of 15 million as on someone who was "just looking." AI didn't replace managers. It gave them a superpower — to work only with those who are truly ready to deal 🎯

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If you have a similar situation — many incoming leads but little result — start simple: calculate how many hours per week your team spends on leads that will never buy.
The number will surprise you 😏

🎁 Want to know how many hours your business is losing?
👉 Free express diagnostics: forms.gle/fYGMjZPpDxBaqnj69
✉️ Write personally → @dmitry_hihol

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