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Sample CRM readiness audit highlighting key spreadsheet workflow gaps before CRM setup.
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Automated LinkedIn Hiring Signal & AI Outbound Engine
Overview:
Designed and deployed an autonomous outbound lead generation system using n8n. The workflow captures hiring intent signals from LinkedIn, identifies relevant executive buyers, enriches verified contact data, and drafts contextualized sales copy through an integrated LLM.
Tech Stack:
n8n • Apify • SearchLeads API • OpenRouter (Nemotron LLM) • Google Sheets API • Telegram Bot API • JavaScript
Core Deliverables:
Automated Signal Scraping: Continuous monitoring of fresh job posts across target industries.
Granular Data Filtering: Exclusion logic for staffing agencies, company headcounts over 250, and unverified domains.
Multi-Source Enrichment: Automated discovery of decision-makers (C-Level, VP, Director) with validated work emails.
Context-Aware Email Generation: Custom prompting architecture that maps the open role's requirements directly into a tailored value proposition.
Data Synchronization & Alerts: Bi-directional sync with Google Sheets and real-time push alerts via Telegram.
Impact:
Replaces 10+ hours of weekly manual SDR prospecting with a continuous background process that delivers high-intent, enriched leads directly to the sales inbox.
I built an AI voice ordering workflow to help a restaurant capture customer orders and pass them to the kitchen with less manual work.
Using Vapi, I configured a voice assistant with the restaurant’s menu and prices to collect customer details and orders. After each call, a webhook triggers an n8n workflow, where OpenAI extracts the order into structured data. The workflow then notifies the kitchen and logs the order in Google Sheets.
I handled the voice assistant setup, webhook integration, AI prompts, and workflow implementation. The solution reduced manual order entry and made order information easier for the kitchen to access.
Tools: Vapi, n8n, OpenAI, Google Sheets, and webhooks.
Having the assistant feed orders straight into the kitchen cuts the manual handoff. The call transcript in Sheets also gives a clear audit trail.
AI-powered tenant communication and maintenance follow-up system designed to help property management teams automate repetitive communication around maintenance requests.
The system takes a tenant maintenance request, analyzes the issue, creates a structured work order, generates a professional tenant update, tracks follow-ups, and prepares completion messages as the maintenance workflow progresses.
Workflow:
Tenant request → AI analysis → Work order → Tenant update → Follow-up → Resolution
Key features:
AI maintenance request analysis
Automatic issue categorization and prioritization
Work-order workflow management
AI-generated tenant communication
Editable tenant messages
Maintenance follow-up queue
Follow-up status detection
Automatic communication drafts when status changes
Completion notifications
Database-driven workflow
The system uses stored property and work-order information to keep generated communication grounded in the actual maintenance records.
Built with: Python, Flask, SQLite, HTML, CSS, JavaScript, and AI-assisted workflow automation.
This project was built as a portfolio demonstration of AI-powered maintenance communication and workflow automation for property management operations.
