HL Tech Insight Case Study

How We Built an AI Services Contact Router for HL Tech Insight

A practical automation case study showing how HL Tech Insight upgraded a contact form into a tested enquiry-routing workflow using Make.com, Cloudflare Turnstile, Google Sheets, Microsoft Outlook, and route-specific auto-replies.

Make.com Automation Smart Website Workflow AI Services Intake Contact Routing Service-to-Product Pipeline

Dark navy AI automation dashboard showing contact form routing, workflow nodes, email notifications, spreadsheet tracking rows, and service enquiry routing logic. — A visual representation of the HL Tech Insight Contact & AI Services Router workflow: validated submissions, route logic, notifications, and tracking.

The Problem

A simple contact form can collect messages, but it does not automatically separate urgent website issues, AI service enquiries, recommended tools questions, collaboration requests, Fit Ogo™ resource questions, privacy concerns, or general messages.

Without routing logic, every message requires manual review. That slows down response time and makes it harder to build a scalable service intake system.

The Goal

The goal was to turn the HL Tech Insight contact form into a smarter intake system that could validate submissions, classify enquiry topics, notify the right internal pathway, send relevant user auto-replies, and keep a structured record for follow-up.

Tools Used

This workflow connects practical business tools into one coordinated enquiry-routing system.

HL Tech Insight Contact Form Captures user enquiries from the website.

Cloudflare Turnstile Supports human verification and spam reduction.

Make.com Runs routing logic, filters, and workflow actions.

Google Sheets Stores structured enquiry records and status fields.

Microsoft Outlook Sends internal notifications and user auto-replies.

HL Tech Insight Logic Matches enquiry topics to service and support pathways.

The Workflow

1. User submits the contact form.
The form captures source, timestamp, website/honeypot field, name, email, topic, related page, message, and Cloudflare Turnstile response.
2. The automation validates the submission.
The router checks anti-spam signals, including the honeypot field and Cloudflare Turnstile token.
3. The submission is stored in Google Sheets.
Each enquiry is recorded with tracking fields such as status, priority, notification_sent, auto_reply_sent, follow_up_due_at, make_processed_at, and updated_at.
4. Make.com routes the enquiry by topic.
The workflow sends the submission through route logic for AI Services, Website/Page Issue, Recommended Tools, Fit Ogo / Project Resource, Collaboration, Privacy / Email List, or General / Manual Review.
5. Outlook sends route-specific internal notifications.
Each topic produces a relevant internal message so the enquiry can be reviewed with context.
6. Outlook sends route-specific user auto-replies.
The user receives a more relevant response instead of a generic one-size-fits-all acknowledgement.
7. The sheet is updated after processing.
The automation records notification and auto-reply status so the system can be audited and improved later.

What Was Built

HL Tech Insight now has a tested Contact & AI Services Router v1.1 that supports structured enquiry intake, automated routing, spam filtering, internal notifications, user acknowledgements, and follow-up tracking.

Current Status

Status: Completed and tested.

The router passed test cases for AI Services, Website/Page Issue, Recommended Tools, Fit Ogo / Project Resource, Collaboration, Privacy / Email List, and General / Manual Review.

Why This Matters

This automation is more than a contact form upgrade. It is a working proof asset for the future HL Tech Insight AI Services and AI Automation Agency pathway.

It shows how a small business website can become a smarter operating system: capturing leads, protecting the form from spam, classifying enquiries, notifying the right workflow, and keeping follow-up records.

Lessons Learned

Contact forms become more valuable when they are connected to workflow logic.
Route-specific auto-replies feel more professional than generic confirmations.
Status fields make automation easier to audit and improve.
Spam prevention should be handled before storage and notification.
A tested internal workflow can later become a service offer, template, or automation blueprint.

Possible v1.2 Improvements

Add an Unknown Topic / Manual Review fallback route.
Add stronger error logging.
Add an automation_error route.
Add make_error, error_module, and error_time fields.
Consider a later Supabase migration for stronger app-style data management.

Trust and Scope Notes

This case study documents a real internal automation workflow built for HL Tech Insight. It is not presented as a guaranteed business result or income claim. The value is in the working system, the tested routing logic, and the repeatable automation pattern that can be adapted for future smart website and service enquiry workflows.

Need a Smarter Contact or Service Enquiry Workflow?

HL Tech Insight builds practical AI and automation workflows that help small businesses, creators, and digital projects capture enquiries, route messages, reduce manual admin, and create clearer follow-up systems.

Discuss an Automation Workflow Explore the AI Resource Library View Recommended Tools

Related pathways: Case Studies · Automation · AI Services · Recommended Tools

Note: Some recommended tools or future resource links may include affiliate links. HL Tech Insight aims to recommend tools based on usefulness, relevance, and responsible implementation — not commission alone.