Built an automated Market Research Agent using n8n 📊 Here's how it works: → User submits a form ...

Built an automated Market Research Agent using n8n 📊 Here's how it works: → User submits a form ...Built an automated Market Research Agent using n8n 📊 Here's how it works: → User submits a form ...

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Sabbir Ahmmed

• Aug 25

Built an automated Market Research Agent using n8n 📊

Here's how it works: → User submits a form with search criteria → Agent pulls business data from Google Maps → Extracts details + reviews for each result → Loops through items, processes & combines the data → AI Agent analyzes everything and generates a summary → Final report sent automatically via email

Fully automated, zero manual work — from form submission to final report.

What kind of research tasks would you automate?

AI Automation OpenAI N8N

Dane Kowachek

pro

• Aug 25

A look at the brand guidelines for SOTO - Fashion forward wearable tech for OpenAI with a point of view on privacy 👀

OpenAI Brand Design Brand Strategy Creative Direction Adobe After Effects Figma

Kabir Ahmed

pro

• Aug 28

Really like how the guidelines give the brand a clear point of view. Wearable tech can easily look generic, but this feels much more intentional.

Sebastian Avelar

pro

• Aug 24

Hey! I just shared a write-up on something I learned while rebuilding a product with AI agents:)

The main idea is pretty simple: Don’t put all your product context in one giant agent file.

Keep the main file small, and put the real product knowledge inside structured docs that the AI can navigate when it needs them.

This has helped me a lot when working on a SaaS product with a lot of business logic!

Sharing the full breakdown here:

https://docswarp.notion.site/How-to-document-your-product-so-AI-can-actually-build-it-3c09678204c580948a52c0f4ec86d6fc?source=copy_link

I'm offering consulting services to set this up! :)

Claude GitHub Supabase AI Engineer AI Development Product Design

Mark Barliet

pro

• Aug 25

The table-of-contents-in-the-main-file pattern is the key move here - it keeps the agent's default context small while still giving it a map to go fetch the architecture or naming-convention doc only when the task actually touches that domain.