How We Systematized AI Market Intelligence for a Founder

Client Profile

A founder running a niche consulting business wanted faster and more structured insights on industry trends, competitors, and customer signals. Most of their market research was done manually, jumping between Google searches, newsletters, and LinkedIn posts. It was time-consuming and inconsistent.

The Problem

The founder spent 5–7 hours a week gathering market intelligence but struggled to turn that information into usable insights.

Key challenges:

• Scattered research sources (news sites, blogs, social media)

• No single place to store or summarize findings

• Repetition and missed updates

• Delayed decision-making due to fragmented data

  
  

They didn’t need another tool they needed a system that gathered, analyzed, and summarized insights automatically.

The Approach

We started with process mapping.Before writing a single line of code, we documented how insights were being found, filtered, and used.

Our design process included:

1. Define Intelligence Goals – What decisions should this data inform (e.g., positioning, offers, messaging)?

   
   

2. Identify Reliable Sources – Added 20+ high-signal sites and feeds using RSS and custom scrapers.

   
   

3. Create the AI Workflow – Designed a Relevance AI pipeline that:

   • Pulled fresh content daily

   • Summarized each article using GPT-based models

   • Scored insights based on relevance and recency

     
     

4. Deliver via Daily Report – The system pushed a morning digest to Slack and email summarizing the top 5 actionable insights.

   
   

The Solution

The final system connected:

• Data Layer: RSS feeds, custom web scrapers, and APIs from top AI and business sources.

• AI Layer: GPT-powered summarizer and classifier for categorizing insights by topic (e.g., “Competitors,” “Funding,” “Product Launches”).

• Delivery Layer: Automated Slack and Gmail updates using Zapier and Relevance AI integrations.

Systemizing AI Market Intelligence: From fragmented searches to daily actionable insights

Stage	Label	Description (use in small font)
1. Define	Map Intelligence Goals	Identify what insights actually drive decisions — pricing, positioning, or partnerships.
2. Source	Select High-Signal Channels	Add curated RSS feeds, analyst reports, and trusted niche sources.
3. Systemize	Design AI Workflow	Automate data collection, summarization, and categorization using GPT models.
4. Deliver	Automate Distribution	Push daily digests to Slack or email for fast decisions.
5. Learn	Refine with Feedback	Capture user interactions and continuously tune relevance scoring.

Results

After 3 weeks of use:

• Time spent on research dropped by 80%

• The founder’s weekly briefing was auto-generated and saved in Notion

• Insights were structured and easy to share with their team

• The system surfaced relevant opportunities the founder had previously missed

Most importantly, the founder stopped collecting random data and started acting on context-rich intelligence.

The Takeaway

AI adoption isn’t about adding tools. It’s about designing systems that make information flow from data to insight to action.

That’s what we do at LeadAi Solutions: We help founders and small teams turn repetitive, manual work into intelligent, scalable workflows powered by AI.

At LeadAi, we don’t just recommend tools we build, test, and refine end-to-end AI solutions tailored to your realities. Want to see what’s possible in your workflows?

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