Case Study — YieldSphere

Operational Intelligence Platform

Unifying Farm Operations, Finance, and Market Intelligence in One Platform

3 disconnected systems unified into a single operational picture for a vertically integrated agribusiness

Industry

Vertically Integrated Agribusiness

Timeline

18 weeks

Team

7 engineers

Tech

React + PostgreSQL + dbt

The Challenge

A vertically integrated agricultural enterprise managing 80,000 hectares of production, 3 processing facilities, and commodity sales across 22 markets was running farm management, finance, and market intelligence on completely separate systems with no data sharing. The 4-day monthly close involved 3 separate teams reconciling between systems with a 15-20% error rate in initial drafts.

Our Approach

How We Solved It

Data Integration Architecture

Built a unified data integration layer connecting the ERP (farm management), accounting system (finance), and market data feeds — establishing a single operational data store with consistent entity references across all three systems.

Unified Cost Accounting Model

Designed a granular cost accounting model that allocates costs at the field, crop, and production run level — replacing the current practice of farm-level cost averaging that obscured profitability differences between field zones.

Market Intelligence Integration

Connected commodity price feeds, futures curves, and basis data into the operational platform so margin calculations use current market prices rather than the previous-day batch from a separate system.

Integrated P&L Dashboard

Built a real-time P&L dashboard that shows margin per hectare, per crop, and per market segment with full drill-down — eliminating the 4-day close and replacing it with a continuously updated operational P&L.

Engineering Process

How We Built It

dbt Semantic Layer for Cross-System Metrics

dbt semantic models define business metrics (cost per bushel, margin per hectare, production efficiency) in one place, ensuring consistent definitions across finance, operations, and executive views.

Event-Driven Cross-System Sync

Farm operations events (planting, applications, harvest) trigger automatic updates to cost accounts and P&L, replacing the weekly manual reconciliation that was the primary source of the 15-20% error rate.

Hierarchical Cost Center Architecture

Cost centers follow the natural business hierarchy (enterprise → farm → field → crop × season), enabling drill-down from enterprise P&L to individual field profitability without custom reporting work.

Architecture Decisions

Key Technical Choices

Operational Data Store Over Data Warehouse

An operational data store (ODS) with dbt transformations rather than a pure analytical data warehouse — the ODS serves both operational dashboards (low-latency read) and analytical reporting (batch aggregation).

Market Data as Real-Time, Not Batch

Delayed market price data in margin calculations caused systematic misvaluation of inventory and forward sales. Real-time price feeds were non-negotiable for accurate intraday margin visibility.

Rolling Forward P&L Over Period-Close Only

The platform maintains a continuously updated rolling P&L rather than computing P&L only at period close — a design choice that eliminates the close process while improving management decision-making.

Results

What We Delivered

Systems Unified

100%

Data Consistency Across Systems

Same Day

Close Visibility (was 4 days)

35%

Reduction in Operational Overhead

Solution Blueprint

How It All Fits Together

Integration Layer

ERP farm management connector
Accounting system sync
Real-time market data feeds

Analytics Layer

dbt semantic cost models
Field-level P&L attribution
Commodity basis analytics

Operations Layer

Integrated P&L dashboard
Cost center drill-down
Market intelligence view

Lessons Learned

What We Improved

Entity Resolution Is the Core Technical Challenge

The three systems used different identifiers for the same fields, crops, and seasons. A 3-week entity resolution process to create a unified master data layer was the critical path item for the entire project.

Finance and Operations Have Different Granularity Needs

Finance wanted period-end cost summaries; operations wanted daily cost visibility per field. Building both views on the same underlying data model satisfied both without compromise.

The Market Data Integration Changed Decision Culture

When managers could see current-market margin per crop in real time, they began making planting and sales decisions based on forward margin rather than historical cost-plus. This was the highest-value outcome — and was discovered, not planned.