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Synthetic RFQ-to-quote control workflow: normalizes request lines, matches only approved catalogue codes and prices, blocks unapproved pricing, flags duplicates and unit mismatches, and routes uncertain candidates to human review. Imported and executed successfully in n8n 2.38.5 with ten acceptance checks passing. Self-initiated proof using synthetic data, not client work.
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AI Voice Agent for Real Estate Lead Booking
An AI-powered phone receptionist built for real estate agencies that answers inbound calls, qualifies leads in real time, and books showings directly to Google Calendar — all within the call itself, with no manual follow-up required.
The problem: Real estate agencies routinely lose leads to missed calls — after-hours inquiries, calls during showings, or overflow during busy periods. A slow callback often means the lead has already moved on to a competitor.
What it does:
Answers every call instantly, 24/7
Naturally qualifies the caller (buy/sell intent, area of interest, showing vs. agent call)
Collects contact details and preferred timing conversationally
Checks real-time calendar availability before booking
Prevents double-bookings and duplicate entries automatically
Confirms the appointment out loud before ending the call
Built with: Retell AI (conversational voice layer), n8n (workflow automation and business logic), Google Calendar API (scheduling)
Available for: Custom builds for real estate agencies, brokerages, or property management companies looking to stop losing leads to missed calls
Beautiful workflow by the way. My nicheeee
Fieldwork now works from request to approved quote.
Try the demo: https://launchinseven.com/samples/fieldwork
Enter a fictional service request, see the itemized estimate, then open Operations to edit the quote, review it and export a printable quote or CSV. Records survive a reload in your browser. The demo uses fictional data and local storage; a shared live system would need its own agreed backend and access controls.
The point is to make a business workflow concrete enough to test: what the customer enters, what the team reviews, what changes status and what leaves the system.
I direct scope and review; GPT-6 Astra handles implementation and many checks. This is an original working sample, not a paid client case.
If an existing web workflow is causing friction, message me on Contra with the URL or redacted screenshots and what should happen. We can agree a small paid first milestone—such as a documented workflow review or a defined interface fix—with its deliverable, fee, inputs and timing confirmed before work.
I'd keep a short change history beside the itemized estimate, so the team can see what changed before approval and which version was exported.
Save hours of manual administrative work with AI automation.
I built this independent AI automation prototype to demonstrate how an administrative quality review process can be streamlined using n8n, OpenAI, Gmail, and Supabase.
Instead of manually receiving emails, checking attachments, reviewing documents for missing information, recording case details, deciding whether a follow-up is required, and writing follow-up emails, the workflow automates these repetitive steps.
The workflow:
Gmail Intake → Document Routing & Extraction → AI Quality Review → Case Record & Decision → Professional Follow-up Email
The AI analyzes the submitted information, identifies missing or inconsistent data, structures the results, and determines whether a follow-up is required. If necessary, it automatically generates a polished follow-up email for the management team.
The goal is simple: reduce hours of repetitive administrative work, speed up the review process, and allow teams to focus on work that actually requires human judgment.
Tech Stack: n8n • OpenAI • Gmail • Supabase • APIs • AI Workflow Automation
This is an independent portfolio prototype using fictional/sample data. It is not an official ISYS system, project, or client engagement, and is not affiliated with ISYS Solutions.
Watch full dem video here:
