Case Study

Deep Sage

An AI-powered Flutter desktop application that centralizes the data science workflow from discovery through visualization.

View Repository

Executive Summary

Deep Sage is a cross-platform desktop experience for data scientists who want an integrated hub for dataset discovery, exploration, and storytelling. Built with Flutter and supported by a dedicated backend layer, the platform removes the friction of juggling multiple tools by converging workflows inside a single, polished interface.

Problem

Typical analysis pipelines demand context switching: sourcing data from public repositories, cleaning it in notebooks, testing hypotheses in ad-hoc scripts, and assembling visualizations elsewhere. This fragmentation delays insight generation and creates handoff overhead for teams.

Solution

Deep Sage streamlines the journey by connecting users to curated dataset catalogs, surfacing AI-generated insights, and offering interactive visualization tooling without leaving the app. A Supabase-backed backend coordinates authentication, dataset storage, and collaborative state, while Google Cloud infrastructure handles durable storage needs.

Highlighted Capabilities

  • Built-in search across Kaggle, Google Dataset Search, and UCI repositories.
  • Data preparation workflows with profiling, cleaning, and transformation utilities.
  • AI-assisted exploration that recommends metrics, charts, and statistical summaries.
  • Interactive dashboards for visual charting and report generation.
  • Cloud sync to keep datasets and session state consistent across devices.

Product Gallery

Technical Foundations

  • Flutter desktop client targeting Windows and Linux.
  • Backend service layered behind configurable dev and prod base URLs.
  • Google Cloud Storage for secure dataset persistence.
  • Supabase for authentication and metadata management.
  • Hive for local caching of user preferences and chart state.

Next Steps

  • Launch collaborative editing modes for shared dashboards.
  • Layer in experiment tracking for reproducible model experimentation.
  • Publish a plugin architecture so teams can extend Deep Sage with custom integrations.

Challenges

  • Data scientists rely on fragmented tooling for dataset discovery, cleaning, analysis, and reporting.
  • Context switching between local scripts, notebooks, and visualization tools hurts productivity and collaboration.
  • Synchronizing datasets and credentials across devices introduces risk and slows experimentation.

Outcomes

  • Delivered a single desktop workspace that orchestrates dataset search, exploration, visualization, and reporting.
  • Integrated AI-assisted insights to shorten the gap between importing data and extracting meaning.
  • Established a cloud-backed data layer so analysts can pick up work seamlessly across machines.