Abstract:
DEEPINSIGHT generates ultra-detailed, 3000-4000 word strategic audit reports – complete with financial models, SWOT analyses, and persona cards – in minutes, just by inputting a product name and region. Here’s a full breakdown of how I built it on MuleRun.
1. The Problem DEEPINSIGHT Solves
Accessing professional market strategy consulting (like McKinsey or BCG) is incredibly expensive and time-consuming. For startups and solopreneurs, getting a comprehensive report that includes TAM/SAM/SOM estimations, competitive analysis, and 5-year financial projections within hours is almost impossible.
DEEPINSIGHT aims to automate and democratize this process.
2. How I Built It (The Build & Tech Stack)
My core architecture is lean and powerful, orchestrated by n8n:
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The Brain (Core Engine): DeepSeek V3 (accessed via SiliconFlow API). I chose V3 for its excellent cost-performance ratio in long-form text generation and complex reasoning. -
The Orchestrator (n8n Workflow):-
Node 1: Form Trigger: Utilizes n8n’s native form to collect Product Name, Region, and Features.
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Node 2: HTTP Request: This is where the magic happens. I configured an incredibly detailed System Prompt (over 1000 tokens of instruction) that forces the AI to act as a “McKinsey Principal Consultant,” demanding strictly structured outputs (tables, scorecards, KPIs).
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Node 3: Code Node: A simple JavaScript snippet to process the JSON response from the API, ensuring stable and clean output formatting.
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Node 4: Respond: Delivers the fully rendered, long-form report directly back to the user.
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3. Key Decisions & “Secret Sauce” (What I Learned)
Throughout the build, I discovered that Prompt Engineering was the ultimate game-changer.
As you can see in my n8n node, I didn’t just ask for “a report.” Instead, I enforced strict requirements:
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No vague descriptions; specific data source citations like “according to Statista 2024” or “IBISWorld reports” are mandatory. -
Must generate ASCII-style visual scorecards and data boxes. -
Must include concrete Break-even calculations and CAC/LTV ratios. -
Must create detailed persona cards with first-person quotes.
4. What Worked and What Didn’t (My Wins & Fails)
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What Worked: Setting DeepSeek V3’s temperature to 0.3. For strategic reports, too much creativity can lead to fabricated data. A low temperature ensures logical coherence and data integrity. -
What Didn’t Work: Initially trying to accomplish everything in a single, overly complex prompt often led to truncated outputs. I later optimized max_tokens to 12000 and refined the prompt structure for full, complete generation in one go.
5. Conclusion
This Agent proves that with well-structured prompt architecture and a flexible tool like n8n, we can transform expensive professional services into reproducible APIs.