TL;DR
I built RivalRadar, an AI tool that generates full competitor analysis reports in under 60 seconds. You enter a company name and industry — it discovers competitors, runs SWOT analysis, compares pricing, and gives actionable recommendations. The entire infrastructure costs $0/month.
Here's how I did it, what I learned, and why N8N is seriously underrated.
The Problem
Competitor analysis tools like Crayon, Klue, and Kompyte cost $12,000–$47,000/year. Small businesses, freelancers, and startup founders can't afford that. But everyone needs to understand their competitive landscape.
I wanted to build something that gives you 80% of the value for 0% of the cost.
The Architecture
Here's how RivalRadar works under the hood:
User enters company + industry
↓
Next.js API Route (auth + validation)
↓
N8N Webhook Trigger
↓
AI Agent #1: Competitor Finder
→ Discovers top 5 competitors via web search
↓
AI Agent #2: Company Analyzer (per competitor)
→ Profile, products, pricing, target audience
↓
AI Agent #3: Sentiment Analyzer (per competitor)
→ Strengths, weaknesses, public perception
↓
AI Agent #4: Report Generator
→ SWOT analysis, pricing table, recommendations
↓
JSON response → Frontend renders report
The key insight: N8N orchestrates everything. Each AI agent is a separate node in the workflow, processing data in sequence. If one fails, the error handler catches it. If the primary AI model hits a rate limit, it falls back to a secondary model automatically.
The Stack (Zero Cost)
| Layer | Technology | Cost |
|---|---|---|
| Frontend | Next.js 14 + Tailwind + shadcn/ui | $0 |
| Auth | NextAuth.js v5 (Google OAuth) | $0 |
| Database | Neon PostgreSQL (free tier) | $0 |
| ORM | Prisma | $0 |
| Automation | N8N (self-hosted on Render) | $0 |
| AI Model | Groq Llama 3.3 70B | $0 |
| Uptime | UptimeRobot (keeps N8N alive) | $0 |
| Deploy | Vercel | $0 |
Total: $0/month.
Why N8N?
I've used N8N in previous projects (FormJet, a smart form builder), but RivalRadar pushed me to learn it deeply. Here's why N8N was the right choice:
Visual workflow design. You can see the entire data flow. When something breaks, you click the node and see exactly what input it received and what output it produced. Debugging is incredibly fast.
AI Agent nodes. N8N has built-in LangChain integration. You define a system prompt, connect an LLM, give it tools (like HTTP Request for web search), and it becomes an autonomous agent.
Fallback logic. My workflow uses Groq (Llama 3.3) as the primary model. If it hits a rate limit, an IF node catches the error and routes to Google Gemini as a fallback. This took 5 minutes to set up in N8N — in code, it would have been a whole error handling architecture.
Self-hosted and free. I run N8N on Render's free tier. The only catch is cold starts (Render sleeps free services after inactivity). I solved this with UptimeRobot pinging the instance every 5 minutes.
The N8N Webhook Fix Nobody Talks About
I hit a nasty bug: N8N's webhook paths weren't working as expected. After digging into the source code, I found the root cause in getNodeWebhookPath(). When a webhook node doesn't have a webhookId, it generates a compound path (workflowId/nodeName/path) instead of the clean path you defined.
The fix: insert the webhookId directly into N8N's SQLite database. The isFullPath: true flag only works when webhookId exists. This is a self-hosted specific issue that I couldn't find documented anywhere.
Key Technical Decisions
Why NextAuth.js v5 instead of Supabase Auth?
I originally planned to use Supabase for everything, but hit the free tier project limit (2 projects). Instead of paying, I switched to Neon (PostgreSQL) + Prisma + NextAuth.js v5. This actually turned out better — Prisma gives me more control over queries and NextAuth's session handling is rock solid.
Why Groq over OpenAI?
Cost. Groq's free tier gives you Llama 3.3 70B — a model that rivals GPT-4 for analysis tasks. The speed is also insane (Groq's LPU inference is significantly faster than GPU-based alternatives). For a tool that promises "results in 60 seconds," speed matters.
Why a credit system?
Each user gets 3 free reports. This prevents abuse while letting people experience the tool before any paywall. The credit system uses a transaction table for full auditability.
What I Learned
N8N is production-ready. It's not just a toy for simple automations. Multi-agent AI pipelines with error handling, fallbacks, and data transformation — it handles all of it.
Zero-cost infrastructure is real. Neon, Vercel, Render, Groq — the free tiers of these services are generous enough to run a real SaaS product.
AI agents are only as good as their prompts. I spent more time writing and refining agent prompts than writing code. The difference between a mediocre report and a great one is entirely in the system prompt.
Self-hosting has trade-offs. Render's free tier sleeps, so UptimeRobot is essential. The webhook bug cost me hours. But $0/month makes it worth it.
Try It
- Live app: rivalradar-three.vercel.app
- Source code: github.com/akincskn/rivalradar
Enter any company name and industry. Get a full analysis in 60 seconds. No credit card needed.
What's Next
I'm building a series of AI-powered tools. Next up: a GEO (Generative Engine Optimization) analyzer that helps content rank in AI search results like ChatGPT and Perplexity. If you're interested in N8N, AI agents, or zero-cost SaaS architecture, follow me — more coming soon.
I'm Akın Coşkun, a full-stack developer from Turkey building AI-powered tools with zero-cost infrastructure. Find me on GitHub or check my portfolio.
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