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Configure forecasting models based on data quality and account characteristics

Investment Thesis

Customers with 100+ accounts need granular control over which models run where. The current one-size-fits-all approach doesn't scale — ML works brilliantly for some categories (regular vendor payments) but poorly for others (intercompany, tax). Giving users the right configuration controls builds trust in the overall system and reduces noise from bad predictions.

  • Cash Forecasting — forecast accuracy, model configuration, variance analysis

Desired Outcomes

Current Focus

These are the outcomes we're actively investing in.

# Desired Outcome Evidence
1 Minimize forecast errors caused by poor data sources (e.g., unreliable ARP data) ON
2 Reduce time spent correcting forecasts that could have been configured better ON

Already Addressed

Existing features that help get this job done.

Desired Outcome Status Feature
Increase ability to disable/enable ML per account based on suitability ✅ Solved Forecast Settings
Minimize the effort required to demonstrate forecast accuracy ⚠️ Partial Forecast Settings
Increase the transparency of model selection and performance ⚠️ Partial Forecast Settings

Not Yet Addressed

Known outcomes we're not focusing on yet.

Desired Outcome Evidence
Provide data quality indicators per account/category to guide configuration decisions ON
Recommend optimal settings based on historical accuracy ON
Support combination forecasts (ML + ERP data for same category) ON

Current Approach

Stage: Skateboard (Validate)

Expanding forecast configuration from simple ML on/off toggle to richer per-category/per-account settings. Users should be able to set forecast method preferences (ML, manual, fixed recurring, combination) at the category level, with account-level overrides where needed.

What's Validated

  • Per-category control for forecast method (ML vs manual) — "For this account, please deactivate machine learning" (ON, 2025-11-11)
  • Fixed recurring amount option — Works for tax, salaries, fees (ON, 2025-06-26)
  • Simplicity advantage vs enterprise tools — Praised vs Kyriba/SAP (ON, 2025-06-26)
  • Global settings approach — Validated as starting point (ON, 2025-06-26)

Next Milestone

Validate expanded settings UI with ON — can they configure their 150+ accounts efficiently?

Feedback Log

Date Company Validated Summary File
2025-06-26 ON Yes Early validation, simplicity praised View
2025-07-01 Instacart (Expert) Yes Combination forecasts validated View
2025-10-07 ON Partially ML pattern detection needs improvement View