u/aiminent_ai

I've been thinking about this while working on lead workflows for real estate teams.

There seems to be a tendency to either:

1. Put everything into a traditional rules-based automation, or

2. Throw an LLM/AI agent at the entire process and call it "agentic."

I don't think either extreme works particularly well.

For example, take a new property enquiry:

Lead comes in

↓

Extract requirements

↓

Determine buying intent

↓

Prioritize the lead

↓

Decide what happens next

↓

Salesperson follows up

I'd probably split this into different layers.

The predictable parts should remain deterministic.

For example:

- Required fields

- Lead scoring weights

- Routing

- Notifications

- CRM updates

- Follow-up timing

- Status changes

AI makes more sense where the input is messy or requires interpretation:

- Understanding a WhatsApp conversation

- Extracting budget/location/timeline from natural language

- Determining buying intent

- Summarizing the conversation

- Identifying missing information

- Flagging unusual or ambiguous cases

And then I'd still keep a human in the loop for decisions where being wrong is expensive.

So instead of:

AI → decides everything → automation

I'd think of it more as:

Rules + Automation

↓

AI interpretation

↓

Deterministic decision layer

↓

Human when confidence/risk requires it

The interesting part to me is that this doesn't necessarily require replacing an existing CRM.

A lot of the value may actually be in the layer between:

"Lead entered the system"

and

"Salesperson knows exactly what to do with it."

Curious how other people working with real estate systems are approaching this.

Where have you found AI genuinely useful in the workflow, and where have you found that a normal automation/rule works better?

I built this because manually going through every inbound real estate enquiry can mean spending a lot of time on leads that aren't ready to move forward.

The workflow automatically:

• Receives an incoming lead from a website / ad form
• Extracts the relevant information from the enquiry
• Scores the lead based on things like budget, location, timeline and buying intent
• Routes the lead based on the qualification result
• Matches qualified leads with relevant properties
• Generates a personalised follow-up
• Notifies the sales team when a lead is ready for action
• Sends lower-intent leads into a follow-up/nurture path

The part I was most interested in was separating AI interpretation from workflow logic.

The AI handles the messy/unstructured part:

enquiry → structured lead data

Then n8n handles:

structured data → score → route → actions

This makes the workflow easier to debug than having the LLM decide the entire process.

Stack: n8n + OpenAI + WhatsApp + Google Sheets/CRM

Workflow:
https://github.com/jashan2512/Lead_qualifying_Workflow.git

One thing I'm still experimenting with is how much of the scoring should be handled by the LLM versus deterministic n8n logic.

How are you guys handling this? Do you let the LLM produce the final score, or use the LLM mainly for extraction/classification and let n8n calculate the score?