RivalMind

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  the issue page

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  the deploy page

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  the skill page

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  main page

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  the level page

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  Posters

Inspiration

We built RivalMind because competitor research takes a lot of time. Product teams often need to search online, read many pages, compare products, collect evidence, and write reports by hand. We wanted to make this process faster and easier with AI.

What it does

RivalMind is an AI competitor research tool. Users enter a product idea or market direction, and RivalMind helps find similar products, analyze competitors, compare features, and generate a clear report.

It can search for competitors, read product information, create comparison tables, check report quality, and save useful knowledge for later use.

How we built it

We built RivalMind as a full-stack web application with a modern frontend and a Python-based backend. The frontend provides an interactive workspace where users can create competitor research tasks, configure analysis settings, monitor progress through live logs, select relevant competitors, and review the final generated reports in a structured format. On the backend, we designed a multi-agent workflow to handle the research process step by step. Instead of using one single AI model to do everything, we separated the work across different specialized agents. Each agent is responsible for a specific part of the pipeline, such as web searching, competitor discovery, product analysis, report generation, quality checking, and questionnaire creation. This made the system easier to control, debug, and improve. The workflow starts when a user submits a company, product, or research goal from the frontend. The backend then processes the request and coordinates the agents. The search agent gathers relevant market and competitor information, while the analysis agent extracts key insights such as product features, pricing, positioning, target customers, strengths, and weaknesses. After that, the report-writing agent organizes the findings into a clear and readable report. A quality-checking agent reviews the output to make sure the information is useful, consistent, and aligned with the user’s request. We also added support for configurable settings so users can adjust how the research is performed. For example, users can influence the depth of analysis, choose which competitors to focus on, and inspect logs while the task is running. This gives users more transparency into the AI workflow and makes the research process feel more reliable. Overall, RivalMind was built to turn a complex competitor research process into a guided, automated workflow. By combining a clean frontend interface with a multi-agent Python backend, we created a system that can help users quickly understand competitors, compare products, and generate actionable business insights.

Challenges we ran into

One challenge was that online product information is often messy. Some websites have missing details, repeated content, or unclear product names.

Another challenge was making the AI output reliable. We did not want RivalMind to only generate nice-looking text. We wanted the reports to include useful evidence and clear comparisons.

We also had to balance automation and user control, so users can still choose competitors manually and add their own product information.

Accomplishments that we're proud of

We are proud that RivalMind can turn a simple product idea into a full competitor research workflow. It does not only generate a report, but also helps with search, analysis, comparison, quality checking, and knowledge saving.

We are also proud of the multi-agent design. Each agent has a clear task, which makes the system easier to understand and improve.

What we learned

We learned that AI systems work better when big tasks are split into smaller steps. Search, analysis, writing, and checking should not all be handled by one prompt.

We also learned that evidence is very important. A good competitor report should show where its information comes from and should be easy for users to verify.

What's next for RivalMind

Next, we want to improve the search results, make the reports easier to read, add more visual charts, and support more data sources.

We also want to make RivalMind faster and more reliable, so users can get high-quality competitor research with less manual work.

Built With

• agent
• beautiful-soup
• crawl4ai
• html
• javascript
• llm-apis
• playwright
• python
• trafilatura