AI Driven Market Research (Easy Win)
Since I started "vibe coding" with AI, building software has become almost trivial. What used to take weeks now takes hours. But here's the thing—once you realize how powerful these tools are, you start seeing opportunities everywhere. I've been hunting for AI opportunities both at home and at my employer, looking for those "easy wins" that deliver real business value without requiring a PhD in machine learning. And I found a goldmine: customer research and lead qualification.
The Problem: Traditional Customer Research is Expensive and Slow
Our sales team was spending countless hours researching potential customers:
Manually searching for companies in our target industries
Trying to find accurate contact information
Qualifying leads based on incomplete data
Creating territory maps for regional salesmen
The process was tedious, error-prone, and expensive. We needed a better way.
The Solution: Public Data + AI Deep Research
Here's where it gets interesting. Google, Perplexity, and others are competing fiercely to build deep research tools, and they're offering them at incredibly low prices. Meanwhile, there are tons of public datasets available for different businesses and industries.
I realized I could combine these two things:
Public NAICS datasets (North American Industry Classification System) for the US, Canada, Mexico, and Brazil
AI-powered deep research from Perplexity and Google to enrich that data
The result? A customer profiling system that automatically finds, researches, scores, and maps potential customers.
The Tech Stack (Surprisingly Simple)
Here's what I used—and remember, AI wrote most of the code:
🗂️ Public NAICS Datasets
Free government databases of businesses classified by industry. I focused on NAICS code 423830 (Industrial Machinery & Equipment Merchant Wholesalers).
🔍 Google Search API
For discovering company websites automatically. Surprisingly affordable for the value it provides.
🧠 Perplexity Deep Research
This is the secret sauce. Perplexity's API can conduct comprehensive research on companies, finding contact information, business details, and more.
✨ Google Gemini
For extracting structured data from the research results—turning paragraphs into usable database fields.
🐍 Python Scripts
Simple scripts that AI helped me write to orchestrate everything. No complex frameworks needed.
How It Works: A Four-Stage Pipeline
The system runs through four stages automatically:
Stage 1: Website Discovery
Starting with just a company name from the NAICS dataset, the system uses Google's Search API to find the company's official website. It's smart enough to distinguish between the real company site and directories or review sites.
Stage 2: Deep Research
This is where Perplexity shines. For each company, it conducts comprehensive research to find:
Detailed business descriptions
Industries served (aerospace, manufacturing, warehousing, etc.)
Geographic coverage and service areas
Installation and service capabilities
Key personnel and contact information
Company size, years in business, and stability indicators
Stage 3: Intelligent Scoring
Here's where it gets really useful. I created a detailed scoring system (0-105 points) that evaluates companies based on:
Business Type (25 points): Are they installers, integrators, or just retailers?
Target Markets (20 points): Do they serve high-value industries like aerospace or manufacturing?
Geographic Coverage (15 points): Multi-state regional presence scores higher
Service Capabilities (15 points): In-house installation teams are gold
Company Stability (15 points): 20+ years in business = top score
Plus several other criteria...
The scoring breaks down like this:
95-105 points: Prime Partner (immediate outreach)
80-94 points: Excellent Partner (high priority)
65-79 points: Good Partner (medium priority)
50-64 points: Potential Partner (low priority)
Below 50: Poor fit (skip)
Stage 4: Google Maps Export
The final magic trick: the system exports qualified leads to Google Maps layers, organized by territory. Our regional salesmen can now open Google Maps on their phones and see exactly where their best prospects are located, complete with all the research data attached.
Real Results That Matter
Here's what this system delivers:
📊 Automated Qualification
Instead of manually researching hundreds of companies, the system processes them automatically and surfaces only the best prospects.
📞 Contact Information
Sales managers, operations directors, and decision-makers identified automatically. No more hunting for the right person to call.
🗺️ Territory Mapping
Regional salesmen get Google Maps layers showing their best prospects, with all the research attached. They can plan routes and prioritize visits efficiently.
🌎 Multi-Country Coverage
The same system works across the US, Canada, Mexico, and Brazil. One codebase, multiple markets.
💰 Cost Effective
API costs are minimal compared to hiring researchers or buying expensive lead lists. We're talking pennies per company researched.
⚡ Speed
What used to take days of manual research now happens overnight. The system can process hundreds of companies while we sleep.
A Real Example
Let me give you a concrete example. The system identified a company called "American Custom Lifts" (I'm using them as a benchmark). Here's what it found automatically:
Business Type: Perfect installer/dealer (25/25 points)
Target Markets: Aerospace, manufacturing, industrial (20/20 points)
Geographic Coverage: National presence, multiple service centers (15/15 points)
Service Capabilities: Full service, certified technicians (15/15 points)
Customer Base: B2B industrial focus (10/10 points)
Stability: 25+ years in business (15/15 points)
Company Size: Professional operation with certifications (10/10 points)
Positioning: Quality focus, American-made (5/5 points)
Total Score: 115/105 (yes, bonus points for exceptional matches!)
The system flagged this as a "Prime Partner" and our sales team reached out immediately. That's the kind of intelligence that used to require hours of manual research.
Key Takeaways for Your Own AI Wins
Look for data + AI combinations:Public datasets are everywhere. AI can enrich them automatically.
Start with simple Python scripts:You don't need complex frameworks. AI can write simple, effective code that just works.
Use the right AI tool for each job:
Google Search API for discovery
Perplexity for deep research
Gemini for structured data extraction
Build in stages:My system has four distinct stages. Each one is simple. Together, they're powerful.
Focus on business value, not technical complexity:The goal isn't to build the most sophisticated system—it's to solve real business problems.
API costs are surprisingly low:Deep research that would cost hundreds of dollars per company if done manually costs pennies with AI APIs.
Export to tools people already use:Google Maps integration means salespeople don't need to learn new software.
The Pleasant Surprise
When I started this project, I expected it to be useful. What I didn't expect was how easy it would be to build something this powerful.
AI didn't just help me write the code—it helped me think through the architecture, debug issues, and optimize the workflow. The entire system, from concept to production, took less time than a single manual research project used to take.
That's the real "easy win" here: AI has lowered the barrier to building sophisticated business tools so dramatically that anyone with a problem and some curiosity can build a solution.
So my advice? Look around your organization. Where are people doing repetitive research? Where are you combining data from multiple sources manually? Where could automation save hours of work?
Those are your AI opportunities. And with today's tools, they're easier to capture than you might think.
It's been a very pleasant surprise indeed.