DECISION SUPPORT APP CASE STUDY
Fertilizer Recommendation Engine
We transformed a spreadsheet-heavy agronomy process into a rules-driven recommendation engine so teams could deliver more consistent outputs, faster, with less manual rework.
Problem
Recommendation workflows relied on manual spreadsheet steps and repeated checks, which made delivery slower and introduced variability in outputs between client accounts.
Constraints
- Recommendation logic needed to remain transparent and reviewable by agronomy teams.
- Existing client-facing output formats had to remain familiar and stable.
- Account boundaries were required so each client’s data and workflows stayed isolated.
Solution
We implemented a structured pipeline with validated inputs, rules-based recommendation computation, and standardized output generation. The workflow is account-aware, enabling consistent delivery across multiple clients.
Visual Architecture
End-to-end flow from intake to recommendation delivery:
Module
Input Intake
Module
Validation Layer
Module
Rules Engine
Module
Output Rendering
Module
Client Delivery
Data boundaries and workflow context are scoped per client account to support multi-tenant scale without cross-client leakage.
Before vs After
BEFORE
- Spreadsheet-heavy manual calculations
- Inconsistent recommendation formatting
- Higher review overhead during busy periods
AFTER
- Validated, repeatable recommendation flow
- Standardized client-ready outputs
- Faster turnaround with reduced manual friction
Outcomes
- More consistent recommendations across client accounts.
- Faster delivery of client-ready outputs.
- A stronger operational foundation for scaling advisory work without proportional admin overhead.
Want this kind of delivery consistency?
If your team is still relying on spreadsheets for critical recommendations, we can map a practical automation path that preserves quality and reduces workflow friction.