Magic Spoon <> Uttam

Date: February 3, 2026 Source: Granola Meeting ID: 9bbb56f2-1c5d-4cc4-8f14-d374414a77b8 URL: https://notes.granola.ai/t/9bbb56f2-1c5d-4cc4-8f14-d374414a77b8

Participants:

  • Uttam Kumaran (Brainforge)
  • Justin Tabarini (Magicspoon)
  • Mary (Magicspoon)
  • Josh (Magicspoon)
  • Michael Thorson (Magicspoon)

Summary

Data Mart Deliverable Feedback

  • Friday delivery timeline issues
    • Final CSV delivered 9pm Friday vs expected earlier completion
    • Half-deliverable Wednesday didn’t include complete logic
    • JT waited for full product before QA, causing delays
  • Internal QA gaps identified
    • Three logic discrepancies requiring clarification
    • Missed entire channel in analysis
    • Insufficient internal review before client delivery
  • Process improvements agreed
    • Book calls immediately when deliverables ready vs Slack back-and-forth
    • Flag blocking QA requirements explicitly upfront
    • Use preliminary reviews/co-development sessions for faster iteration
    • Escalate review urgency when Friday deadlines involved

SPINS API Integration Challenges

  • Unique technical difficulties vs typical APIs
    • 26-week change window complicates data consistency
    • Multiple aggregation options (1-week, 4-week, 12-week) with unclear differences
    • Rate limiting requires custom Python looping through combinations
    • Cannot create fixed reports - must call API for every combination
  • Prefect orchestration tool limitations
    • Required local testing setup causing engineering overhead
    • Now resolved with Python pipelines running locally
  • Next steps with SPINS support
    • Schedule call with Ugo (product manager) for best practices guidance
    • Michael to facilitate introduction and join call
    • Document current API approach for their review and future documentation

Project Status & Next Steps

  • Current progress
    • SPINS pipeline ready for local testing and backfill
    • Parallel development on DBT models while resolving data issues
    • Good understanding of Prefect scheduling established
  • Additional data sources discussion
    • Costco requires regular + advanced technical data packages
    • Magic Spoon uses scheduled reports + workflow automation (no API)
    • Brainforge has experience with Uber Eats UI scraping, Browser Base for AI agents
    • Potential to replace Gum Loop automations with code-based solutions