Brainforge <> Magic Spoon

Date: February 18, 2026 Source: Granola Meeting ID: 72fb5c1f-67c4-4a6d-a5db-5dfe20522649 URL: https://notes.granola.ai/t/72fb5c1f-67c4-4a6d-a5db-5dfe20522649

Participants:

  • Uttam Kumaran (Brainforge)
  • Mary (Magicspoon)
  • Josh (Magicspoon)
  • Michael Thorson (Magicspoon)
  • Demilade Agboola (Brainforge)

Summary

Budget and Scope Realignment

  • Magic Spoon’s $25k/month budget significantly exceeds their capacity
  • Initial December scope expanded beyond light DBT fixes and vendor transition to include:
    • Additional SPINS modeling and ingestion requirements
    • Comprehensive Shopify modeling review (bundles, revenue model changes)
    • New data sources (Confido, potential retail sources)
    • Platform maintenance with tight SLA commitments
  • Brainforge’s pricing reflects 3-person team commitment for maintenance + net new development
    • Inheriting large repo with 5,000+ DBT models requires cautious approach
    • Half of current repo likely stale and needs archiving

Revised Partnership Approach

  • Rescoped to $15k/month for 3-month contract (Brainforge prefers fixed terms over hourly/monthly)
  • Priority focus areas:
    1. Platform reliability and vendor transition
    2. Data quality monitoring with fast incident response
    3. Retail data sources integration (biggest potential budget unlock)
  • Deprioritized items:
    • Omni AI dashboard building (Magic Spoon can handle internally)
    • Net new dashboards
    • Confido integration (roadmap item, not immediate need)
  • Partnership model: embedded team providing daily infrastructure oversight, API monitoring, and expertise access

Next Steps

  • Uttam/Demilade: Deliver revised SOW by end of day today/early tomorrow
    • Scope platform reliability requirements and time allocation
    • Pace remaining budget across 3 months for SPINS/MMM work
    • Mark removed scope items clearly
  • Mary: Identify deprioritizable items in current scope
  • Demilade: Follow up on Slack about DBT seeds configuration for MMM models
  • Roland: Run dbt build seeds to resolve failing MMM model runs