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The first message is easy.
Knowing what to do after the first message is the hard part.
A lead fills out a form.
Okay.
Now what?
Do you send an SMS?
An email?
Put them into nurture?
Ask sales to call them?
I don't like deciding that based only on the fact that they became a lead.
I want to see what the lead is actually doing.
Did they click the offer?
Did they check the pricing?
Did they reply to the SMS?
Did they book an appointment?
Did they cancel?
Did they become a no-show?
Those little actions are signals.
And those signals should change what happens next.
A new lead might enter nurture.
Someone showing strong interest gets prioritized.
Someone who replies should come out of the automated sequence and get to sales.
Someone who books should stop receiving “book a call” messages and start getting confirmations and reminders.
That’s how I build follow-up systems in GoHighLevel.
I don't start with:
“What automation should I send?”
I start with:
“What should happen next?”
Then I build the logic around that.
The automation is just how the system executes the decision.
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Really clean and polished app design! The booking flow looks intuitive and the overall UI feels modern and easy to use. 👏
good work
AI Assistant Using Your Business Knowledge Base — RAG on Your Documents
THE PROBLEM
Q&A bots break down when knowledge lives in documents: a 100-page manual has no "questions" to match, it can't fit into a prompt, and generic chatbots hallucinate instead of admitting what they don't know.
THE SOLUTION
A RAG (Retrieval-Augmented Generation) knowledge base: documents are split into meaningful chunks, embedded into a vector index, and the assistant answers from the right sections — by meaning, not keywords.
Any format as-is: PDF, DOCX, TXT, Markdown — 100+ pages is fine
Answers grounded in YOUR documents — it says "I don't have that information" rather than inventing
Source references — every answer shows which document and section it came from
Runs on your infrastructure — documents never leave your control
One command to re-index after updating documents — documented, no programmer needed
LIVE DEMO
Ask it about my products, pricing, deployment and support — it answers from 6 real documents with sources:
https://kb-assistant-demo.dvc-ai.workers.dev
The 'I don't have that information' line is the part most RAG builds skip, and it's the one that matters. How do you set the cutoff? On mine, a fixed similarity threshold broke once I filtered results by user role. Scores shifted and it refused questions it could answer.
GoHighLevel Architectural Review Confirmation & Notification Workflow
Engineered an automated architectural review workflow in GoHighLevel to capture incoming submission forms, dispatch automated email confirmations to applicants, and alert internal review teams immediately.
Key Deliverables & Setup:
• Form Trigger Ingestion: Automatically captures new submissions from multiple architectural review forms.
• Applicant Confirmation Emails: Triggers personalized confirmation emails to notify clients that their submission was received.
• Internal Alerts & Notifications: Sends real-time team notifications to ensure timely review and approval routing.
Results & Impact:
• Guarantees instant client communication upon request submission.
• Streamlines internal operations and removes manual follow-up delays for review teams.
