Welcome to HackerNoon’s Meet the Hackathon Winner interview series, where we peel back the layers of winning projects across our hackathons for a peek at the technical and operational decisions that propelled them to our winners’ podium.

Meet SpyderBot: Proof of Usefulness Hackathon Winner in the Bright Data Category

SpyderBot was one of four projects named winners in the first round of the Proof of Usefulness Hackathon’s Bright Data Awards. The company describes its platform as a real-time Generative Engine Optimization analytics tool that helps brands understand how large language models mention, cite, rank, and recommend them.

Following its selection as a Bright Data Award winner, we caught up with the SpyderBot team to revisit the project and explore how Bright Data could strengthen its web data collection and analytics infrastructure.

INTERVIEW

What does this Bright Data category win mean for SpyderBot?

This recognition is meaningful because it validates a problem we strongly believe will define the next generation of the internet. As AI systems increasingly become the interface between people and information, organizations need to understand how those systems perceive, recommend, and represent their brands.

For us, this award is not just recognition of a product—it is recognition of a new category. We are building AI Visibility Infrastructure to help organizations observe and improve their presence across AI systems, and it's encouraging to see that vision recognized by the community.

How would SpyderBot use Bright Data exactly?

We would use Bright Data as the web data backbone for SpyderBot, solving our need for reliable, large-scale collection of public data from social networks, e-commerce sites, and search results for market and competitive intelligence.

What specific data-collection challenges would Bright Data help SpyderBot overcome?

Bright Data’s residential and datacenter proxies, Web Scraper API, and SERP tools could help us navigate IP blocks, geo-restrictions, and anti-bot systems. This would allow us to consistently obtain structured, geographically accurate datasets without building and maintaining complex unblocking logic ourselves.

How would Bright Data integrate into SpyderBot’s existing data workflow?

SpyderBot would trigger Bright Data’s scrapers and APIs on a schedule or in response to specific events, receive normalized data in JSON or CSV format, and send it through our ingestion pipeline for cleaning, enrichment, and storage in our analytics warehouse and vector store.

How would this division of responsibilities benefit SpyderBot and its users?

This setup would allow Bright Data to handle data access and extraction while SpyderBot focuses on analytics, dashboards, and LLM-based insights. It would help us add new sources faster while improving the freshness and quality of the intelligence we deliver to users.

What’s next for SpyderBot? Is there anything on the roadmap that you’re especially excited to build or launch?

We're expanding SpyderBot beyond AI visibility monitoring into a complete intelligence platform. In the coming months, we're focused on advancing Prompt Intelligence, expanding LLM Tracking, and strengthening the infrastructure that helps organizations understand both AI-generated outputs and how AI systems interact with their websites.

I'm particularly excited about building technologies that explain why AI models mention certain brands, recommend certain sources, or change their behavior over time. We believe observability will become one of the most important capabilities for organizations operating in the AI-native web.

What advice would you give someone considering entering a HackerNoon Hackathon?

Don't optimize for the hackathon—optimize for solving a real problem.

The best projects are usually the ones that continue to grow long after the competition ends. Build something you genuinely want to use yourself, validate it with real users as early as possible, and focus on shipping rather than polishing every detail. Winning is rewarding, but building something that creates lasting value is even more meaningful.

You Could Be Our Next Winner

The Proof of Usefulness Hackathon is open until August 10, 2026, and your project could be next on our winners’ podium. Submit your work, show us the problem you’re solving, and prove its real-world usefulness.