How to build a custom GPT / Claude for your company
Every industry has those massive Slack communities, Discord servers, or forums where hundreds of your ICPs hang out daily.
They're sharing pain points, asking for tool recommendations, discussing their processes, complaining about current solutions etc.
Most marketers see thousands of messages and think "too much for me to read."AI-native marketers see it very, very differently. This is the richest competitive intelligence you'll ever find, and it's sitting right there.
I work at an AI company, so I had some advantages getting started. But after iterating on this approach for a few months, the results are that people are excited about what comes out of these systems.
You can feed all this unstructured conversation data into custom GPTs or Claude projects and create something that's basically an expert employee who never forgets anything.
Think about it - an AI that has read every forum discussion, every G2 review, every sales transcript, every support ticket understands your market's language patterns, knows how people actually talk about problems, and can spot competitive opportunities across thousands of conversations instantly.
Your sales team asks: "How should I position against [Competitor X] for enterprise deals?"
Instead of scrambling through random competitive docs, they get an answer based on 50,000 actual customer conversations about that exact scenario.
Marketing wants to understand why customers switch away from a competitor? The system has already analyzed every switching story mentioned across review sites, forums, and transcripts.
It's like having an analyst who has perfect recall of every relevant conversation that's ever happened in your space.
The data sources are everywhere
Start with whatever community discussions you can access - industry Slack groups, Reddit communities, Discord servers. G2 and Capterra reviews are goldmines. Not just your reviews, but competitor reviews and the entire category. Social media conversations, sales call transcripts, customer interviews, support tickets, conference Q&As.
Just make sure it's actually your ICP talking, or at least part of the buyer group.
In our case, we focus on personal injury attorneys who own their own law firms or are managing partners, paralegals that work at those law firms, litigation and pre-litigation managers, and chief operating officers at those firms.
If you're seeing conversations from people who would never buy your product, that's noise, not signal.Each source has different signal types. Forums show you unfiltered pain points. Reviews reveal switching behaviors. Transcripts capture actual buying language.
When you combine them through AI, you get market intelligence that would take armies of analysts to generate manually.
How to build this
The process is simpler than you'd think:
Collect raw data from multiple sources. Toss it into the AI - it doesn't need to be perfectly formatted or cleaned. The AI will parse through everything and turn noise into signal.
The template I'm sharing below has gotten excellent feedback from stakeholders across different teams. It's designed to work whether you're analyzing community discussions, review sites, or any unstructured data source.
The adoption is happening slowly - this kind of thing takes time. But the positive signals are strong.
You don't need permission to experiment with custom GPTs or Claude projects. Build something useful, share it internally, and become the insights person in your organization.
The prompt template
You are an expert market intelligence analyst specializing in pattern recognition across unstructured data sources. Your task is to process and analyze the following data to create actionable insights for strategic decision-making.
Data Processing Instructions:
1. PATTERN IDENTIFICATION
Analyze the provided data sources and identify:
- Language patterns: How does the target audience naturally describe problems, solutions, and outcomes?
- Pain point clusters: What themes consistently appear across different conversations/sources?
- Sentiment patterns: What emotional indicators suggest satisfaction, frustration, or opportunity?
- Competitive mentions: How are different solutions/companies discussed in context?
- Temporal trends: What topics are gaining/losing momentum over time?
2. SIGNAL EXTRACTION
For each data source, extract:
- High-frequency concepts that appear across multiple contexts
- Unique terminology specific to this industry/audience
- Decision-making criteria mentioned in evaluations or discussions
- Unmet needs expressed directly or implied through complaints
- Success metrics that matter most to this audience
3. CONTEXT MAPPING
Create connections between:
- Problems mentioned
→ Solutions discussed
→ Outcomes achieved
- Industry challenges
→ Tool/service categories
→ Vendor preferences
- Use cases described
→ Implementation challenges
→ Success factors
4. COMPETITIVE INTELLIGENCE
Extract mentions of:
- Direct competitors and how they're perceived
- Alternative solutions and their positioning
- Switching behaviors and triggers
- Price sensitivity and budget considerations
- Feature gaps in existing solutions
Output Structure:
A. AUDIENCE INSIGHTS
- Primary language patterns and terminology
- Core pain points ranked by frequency/intensity
- Decision-making process indicators
- Preferred communication styles
B. MARKET INTELLIGENCE
- Competitive landscape observations
- Emerging trends and opportunities
- Unmet needs and market gaps
- Success factors for solutions in this space
C. STRATEGIC RECOMMENDATIONS
- Positioning opportunities based on data patterns
- Messaging framework using authentic audience language
- Product/service development priorities- Content strategy recommendations
D. MONITORING PRIORITIES
- Key phrases/topics to track ongoing
- Early warning indicators for market shifts
- Competitive monitoring recommendations
Data Sources to Process:
[INSERT YOUR SPECIFIC DATA SOURCES HERE]
Examples:
- Community forum discussions (paste conversations)
- G2/Capterra reviews (competitor and category reviews)
- Social media conversations (relevant threads/comments)
- Sales call transcripts (customer interviews)
- Support tickets (feature requests, complaints)
- Industry survey responses
- Conference/webinar
Analysis Instructions:
- Cross-reference patterns across ALL provided sources
- Quantify frequency of themes where possible
- Note contradictions or conflicting signals
- Highlight unique insights that wouldn't be obvious from single sources
- Focus on actionable intelligence over general observations
- Prioritize insights supported by multiple data points
- Flag outlier perspectives that might indicate niche opportunities
Process this data and provide comprehensive analysis following the structure above.
How to use this
Replace the "Data Sources to Process" section with your actual data - paste in forum conversations, copy competitor reviews, add transcript excerpts.
Start with one data source to test it out. Once you see the value, expand to multiple sources. The AI will start connecting patterns across different types of conversations that you'd never spot manually.
Hope this helps you make something cool with AI!