I transform concepts into high-performing digital realities.

Real estate teams often manage incoming leads manually, resulting in inconsistent qualification, incomplete CRM data, delayed follow-up, and limited visibility into pipeline status. Qualification errors may also go unnoticed without a defined human-handoff process.

Designed and configured an AI-assisted real estate lead qualification and CRM routing system in GoHighLevel. The system captures structured lead information, processes qualification data, organizes opportunities in the pipeline, escalates qualification errors for human review, and tracks lead outcomes through dashboard reporting.

• ✓ Captures lead intent, budget, timeline, location, and qualification status.
• ✓ Stores qualification information in structured CRM fields.
• ✓ Supports AI scoring and qualification status tracking.
• ✓ Processes qualified lead data into opportunities and pipeline stages
• ✓ Organizes leads across New Lead, Qualified, and Nurture stages.
• ✓ Updates CRM records and opportunity stages based on qualification status
• ✓ Escalates errors using the AI Qualification Error tag.
• ✓ Updates the contact field to AI Handoff Required.
• ✓ Adds the Human Handoff tag and sends internal notifications.
• ✓ Tracks lead outcomes through eight dashboard widgets.

Managing franchise operations across multiple locations manually leads to operational bottlenecks. Inspection results can be difficult to track, critical business permits can approach expiration unnoticed, and missing monthly reports require compliance teams to manually cross-reference records and follow up with operators.

A centralized, multi-Zap automated operations architecture that manages the franchise compliance lifecycle. The system captures form submissions, updates tracking databases, monitors deadlines using scheduled workflows, triggers operational escalations, and feeds status metrics into a centralized operations dashboard.

Customer support teams often spend significant time manually reading incoming requests, categorizing issues, creating tickets, drafting responses, and identifying cases that require escalation.

Built an AI-assisted support intake and triage workflow using n8n and Google Gemini. The system receives customer messages through a webhook, checks for duplicates, classifies the request, generates a draft response, logs structured ticket data in Airtable, and routes sensitive or uncertain requests for human review.

• ✓ Classifies Billing, Access, Scheduling, Technical Support, General Question, and Refund Request.
• ✓ Generates confidence scores and classification reasons.
• ✓ Creates AI-generated customer response drafts.
• ✓ Escalates refund requests and low-confidence cases.
• ✓ Sends Gmail notifications for human review.
• ✓ Tracks ticket status and processing timestamps.
• ✓ Includes dedicated n8n error handling.
• ✓ Prevents duplicate processing through Message ID validation.

Real estate lead intake introduces major response delays when done manually. Hot buyer interest can cool down within minutes, while manual follow-ups can create calendar conflicts and inconsistent customer communication.

Built a real-time, event-driven pipeline on Make.com connected to Gemini AI. The engine captures Facebook Lead Ads data packets, sorts client profiles by property choice using filters, logs records to Google Sheets, checks calendar availability, and drafts custom property proposals sent to the buyer via Gmail.