u/AutumnDawn527

Hi — a little follow-up to my previous post about Competitor Scout, the competitive research tool I’ve been working on.

The original version is more of a developer/product app: a CLI plus optional Next.js web UI that lets you enter competitors, ask a research question, and get back structured competitor findings.

After that, I also made an n8n workflow version of the same idea.

The goal is similar, but the user experience is different: instead of using a custom app UI or CLI, you run everything inside n8n as an automation workflow.

Here's the demo video for it:

https://reddit.com/link/1t9xtj2/video/v4f1lmue5h0h1/player

What it does

You fill out a form with:

• Competitors, one per line
• A research question

For example:

>

The workflow then:

1. Uses OpenAI to plan the research goals
2. Sends those goals to Tinyfish Web Agent for browsing and evidence collection
3. Polls the Tinyfish runs until they finish
4. Uses OpenAI again to generate a Markdown comparison report
5. Optionally logs results to Google Sheets
6. Optionally uploads the final Markdown report to Google Drive

So instead of being a standalone product interface, this version is more like a “research automation template” that someone can plug into their existing workflow.

Why I made the n8n version

I wanted to see what this idea feels like as an automation rather than a full app.

The app version is better if you want a polished product experience.

The n8n version is better if you want something hackable, inspectable, and easy to connect with other tools like Google Sheets, Drive, Slack, Notion, email, or internal dashboards.

It also makes the workflow logic very visible: planning, browsing, polling, summarizing, and exporting are all separate nodes that can be changed.

Tech stack

• n8n for orchestration
• OpenAI for research planning and final report generation
• Tinyfish Web Agent for browsing and evidence collection
• Google Sheets for optional structured logging
• Google Drive for optional Markdown report export

What I’m looking for feedback on

I’d love to hear thoughts on:

• Whether this makes more sense as a standalone app or an n8n workflow
• What competitor research questions people would actually run regularly
• Whether Google Sheets / Drive exports are useful, or whether other destinations would be better
• How much control people would want over the research depth, sources, and report format
• Whether this kind of workflow would be useful as a reusable template

Curious to hear what people think, especially from anyone using n8n, AI agents, or automation workflows for product research.

Hi everyone — I’m working on a project called Competitor Scout, and I’d love to get feedback from this community if this project could provide any convenience for you!
Here's the demo of the project:

https://reddit.com/link/1t72kt2/video/6el4qmz8rvzg1/player

What it does

Competitor Scout helps product teams, founders, and builders research competitors faster. Instead of manually opening competitor websites, checking pricing pages, reading feature pages, and trying to compare everything by hand, you give the tool:

1. A list of competitors
2. A natural-language research question

For example:

>“How do these companies position their pricing and AI features?”

The tool then researches each competitor, gathers evidence from relevant pages, summarizes the findings, and generates a structured comparison report.

Why I built it

Competitive research often starts simple but quickly becomes repetitive: checking feature pages, pricing pages, changelogs, docs, blog posts, and positioning copy. I wanted a tool where teams can define competitors once, ask evolving questions over time, and get structured research output without manually redoing the same browsing work.

Who it helps

This is mainly built for:

• Product managers doing competitive analysis
• Startup founders researching market positioning
• Indie hackers comparing product ideas
• Growth / strategy teams tracking competitor changes
• Builders who want faster research before making product decisions

The main goal is to reduce the manual work of checking competitor sites and turn the research process into a repeatable workflow.

How it works

The web app uses a hybrid research pipeline:

• First, it uses an LLM planning layer to turn the user’s question into specific research goals for each competitor.
• Then it searches each competitor’s domain for relevant pages.
• It fetches page content and checks whether the evidence is enough.
• If the search/fetch path is not sufficient, it falls back to a web agent that browses the competitor site directly.
• Finally, it summarizes each competitor and generates a final comparison report.

There is also a simpler CLI version that uses the agent-based flow directly.

Tech stack

• Next.js for the optional web UI
• TypeScript
• OpenAI for planning, evidence assessment, summarization, and report generation
• Tinyfish SDK for Search, Fetch, and Web Agent execution
• Server-Sent Events for streaming research progress in the UI

Feedback I’m looking for

I’d really appreciate feedback on:

• Whether this workflow would be useful in your product or startup research process
• What kind of competitor questions you would want to ask
• Whether a CLI, web UI, or scheduled monitoring flow would be most useful
• What would make the generated reports trustworthy enough for real decision-making
• Any obvious gaps, risks, or features you think I should prioritize

Thanks in advance — I’d love to hear what you'd think.