Kaleidoscope

Walmart - Summary
Walmart - Satellite
Pricing

Inspiration

Our team was inspired by the idea that real-world activity can reveal valuable insights before they appear in traditional financial reports. Supply chains, satellite images, and public search behavior all tell a story about a company’s momentum; however, these signals are rarely analyzed together. Therefore, Kaleidoscope was created to bring these scattered signals together and provide a clearer, data-driven view of company confidence.

What it does

Kaleidoscope is an alternative data intelligence platform that analyzes companies using multiple public data sources. It tracks import activity and supply chain movement, detects changes in physical operations and locations, and measures public interest and market attention. Our website processes these signals and generates a company confidence score, helping users quickly understand potential growth, changes, and trends beyond traditional financial data.

How we built it

Kaleidoscope runs on data stored in a Turso database. Multiple public APIs feed information, which is processed by a Python script that keeps certain data cached for easy retrieval. Other widgets update live, such as the Gemini report.

Challenges we ran into

Integrating multiple data sources: each API and dataset had different formats, limitations, and reliability challenges. Data availability and consistency: finding clean, usable public data and handling missing information required additional processing. Building a meaningful confidence score: combining different signals into one score required balancing multiple factors while keeping results understandable. As our team worked under a limited hackathon timeline, we had to prioritize core features and build a functional prototype quickly.

Accomplishments that we are proud of

We built a working alternative data intelligence platform from scratch during the hackathon. We successfully connected multiple public signals into a unified company analysis pipeline. We demonstrated how non-traditional data sources can provide valuable insights beyond financial reports. We collaborated as a team to design, develop, and deploy a complete application.

What we learned

Real-world data is messy and requires strong validation and fallback strategies. Combining different data sources can reveal insights that individual sources cannot provide alone. Building a useful product requires balancing technical complexity with user experience. Rapid prototyping and communication are critical when developing under time constraints.

What is next for Kaleidoscope

Improve the confidence scoring model with machine learning techniques. Build a more advanced platform for analysts, investors, and businesses. Provide historical tracking to identify changes in company momentum over time. So please stay tuned, and support to view the details of more retailers is the next step in sight.