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Designed a parallel, multi-agent n8n workflow to simultaneously analyze business requests from different departmental perspectives. A single user prompt triggers three distinct AI agents—Sales, Marketing, and Product, powered by Azure OpenAI. Instead of a generic AI response, each agent analyzes the input through its specific lens, using structured output parsers to ensure clean, formatted data. The system then validates and automatically merges these separate insights into one comprehensive dataset, eliminating manual analytical work.
n8n, Multi-Agent System, Azure OpenAI, Parallel Processing, Structured Output Parser, Data Automation, Workflow Architecture, Data Bundling.
The network for creativity
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Hey Contra 👋 I've been heads-down building Promptly, and I'd love your eyes on it before our launch.
From my time delivering over 20 software projects as a tech lead, I realized the biggest bottleneck in building an app isn't the code itself. It is communication. Founders speak in ideas, and developers speak in strict logic.
Here is how Promptly works. You just describe your app idea in plain English. The AI actually pushes back, asks the hard questions, catches the vague bits, and automatically writes a developer-ready blueprint as you chat.
The goal is simple. When you hand this document to a developer, the next thing you get back is an accurate, fixed-price quote. No more wasting days trying to draft technical specifications and sitting through endless rounds of discovery calls just to get on the same page.
Instead of staring at a blank document, you get three tools that update in real-time:
The Pushback Chat: It refuses to just nod along. If you say "make it fast and secure," it helps you define exactly what that means. It hunts down edge cases before you ever pay someone to fix them.
The Visual Map: A live, interactive diagram showing exactly how your features connect. It catches logic flaws and missing steps before any code is written.
The Living Blueprint: An auto-generated, professional project spec that exports straight to Notion, Linear, or PDF.
Plus, it gives your idea a "Developer-Readiness Score" out of 100 so you know if it is actually ready to build. It even recommends the best tech stack based on your specific budget and complexity.
The core engine and all of these interactive features are fully functional right now, but I am still smoothing out the final rough edges. I am aiming for a public launch in about three weeks.
Where I need your help. I have three quick questions:
Where would you want to export your project blueprint, beyond Notion, Linear, or PDF?
What is the biggest headache you face right now when trying to explain your ideas to developers?
What feels missing? The blind spots are the most valuable to me right now.
Any feedback on new features, improvements, or things I should kill entirely would be hugely appreciated. Drop a comment below.
Thank you for taking a look 🙏
building solo, in public
Product ManagementFull Stack DevelopmentSoftware DevelopmentWeb DevelopmentAI DevelopmentClaudeNext.jsFigma
This is a very interesting concept! Where did you design the UI/UX? I feel like a better choice of font is still out there. Overall good work and clean UI. 🔥
AI-Powered CRM Built To Capture And Qualify Leads
Built a lead qualification and CRM workflow that captures inquiries, filters serious prospects, and helps the team follow up faster.
❌ Before Automation
→Leads were coming in, but the process was not clean.
→New inquiries had to be checked manually.
→Some leads needed a quick follow-up.
→Others were not the right fit.
The team had no easy way to separate serious prospects from low-quality leads.
This created 4 problems:
• Slow lead response
• Manual lead checking
• Missed follow-ups
• No clear CRM visibility
Better short version for design:
✅ After Automation
We built an AI-powered lead qualification and CRM system.
The system captures new leads, verifies the information, qualifies prospects, and sends the right data to the CRM. This gives the team a cleaner pipeline and faster follow-up process.
Now the business can see:
• Who the lead is
• What they need
• How serious they are
• Where they are in the pipeline
• What action should happen next
See The Full Workflow here
An autonomous multi-agent orchestrator that reduces tailored job application generation from hours to under 5 minutes. Built on a LangGraph Planner-Researcher architecture, it features an LLM-as-a-judge feedback loop, resilient external tool calling with graceful failure, and full agent tracing via Langfuse to ensure production-level observability.
Interesting app! How does it manage the token cost?
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