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.
Hey frens, for years I could do much of the work around a new product:
Define the opportunity.
Clarify the audience.
Shape the positioning.
Map the customer journey.
Write the MVP brief.
Plan the launch.
But turning that thinking into a working product usually required another dependency: a developer, technical co-founder, agency budget, or a long wait.
That has changed.
AI-native building tools have become a practical technical execution partner in my work—effectively, an always-available CTO for the early stage of an idea.
Alongside my work as a Hands-On CMO, entrepreneur, and investor, I have added two new hats:
Web developer.
Software builder.
Not because AI replaces experienced engineers, product designers, security, or serious technical architecture.
But because it lets a commercially minded operator move from a clear business problem to a real, testable first version much faster.
A recent example is Viewly by Realto: a focused PropTech workflow designed to turn property interest into a structured viewing request.
The problem is familiar. Someone sees a property, asks for a viewing, and the process becomes a chain of calls, messages, email follow-ups, availability checks, delays, and sometimes a lost lead.
Viewly gives the prospect a clearer path: explore the property, choose a suitable time, submit details, and move toward a confirmed viewing—while the business receives a structured enquiry rather than another unorganised message.
The lesson is bigger than real estate.
The first entrepreneurial bottleneck is no longer always technical execution.
It is clarity:
• Who is the first user?
• What recurring problem do they have?
• What is the smallest useful outcome?
• What can we test before building too much?
The winners will not be the people who generate the most AI apps.
They will be the people who turn real friction into focused, usable products.
I wrote more about the shift from writing briefs to building the first version here: