How an Agri-Food Group Freed Trapped ERP Data With AI

A multi-entity agri-food group piped trapped ERP data into a read-only, zero-write layer, giving owners dashboards for volume, yield, and orders without touching the live ERP.

Zero writes

Kept off the live production ERP

Implementation Time

Unmukt Raizada

Founder & CEO

TrustEvals

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The Challenge

Operational and financial data was fragmented across ERP modules and spreadsheets, leaving management with no consolidated view of farming and production. Leaders lacked unified visibility into volume, yield, order throughput, and farmer-level economics. Any solution had to access production ERP data without risking system stability.

What They Built

TrustEvals built a multi-domain business intelligence suite with a governed clean-data layer and dashboards spanning production, finance, sales and vendor, and farmer intelligence. Production KPIs track volume and yield, orders per day, and line-opening rates, while farmer intelligence covers profiles, productivity, geography, field-officer coverage, and loss-making farm economics.

A read-only, no-touch-on-production extraction layer feeds a governed, cleaned data store, so reporting can never destabilise the live ERP. This zero-write architecture replaced scattered exports and spreadsheets with a single governed layer.

AI Role

This is primarily a governed data-and-reporting build. The analytics layer consolidates KPIs — volume, yield, orders per day, and line-opening rates — and surfaces farmer-level economics across the portfolio, turning fragmented ERP and spreadsheet data into daily dashboards for named business owners. No generative model is named in the source.

AI Model

Read-only zero-write ERP extraction layer, governed cleaned data store, multi-domain BI dashboards (specific technologies not named on the page)

Infrastructure

Production ERP modules (read-only source) • Governed cleaned-data store • Multi-domain BI dashboards across production, finance, sales, and farmer intelligence (specific platforms not named in source)

Integration Points

Read-only, zero-write extraction from the production ERP • ERP data piped into the governed clean-data store • Cleaned data feeding multi-domain dashboards

Impact

Daily decisions, not monthly guesswork

Dashboards for volume, yield, and orders per day are in daily use by named business owners.

Zero-write ERP integration

A read-only extraction layer means reporting can never destabilise the live production ERP.

Farmer-level economics surfaced

Farmer intelligence exposes productivity, field-officer coverage, geography, and loss-making farm economics.

Technology Utilized

Implementation Complexity

Time Savings

Best Fit For

Best fit for multi-entity operators whose decision data is locked inside production ERP systems that cannot be touched or destabilised.

Unmukt Raizada

Founder & CEO

TrustEvals

Founder & CEO of TrustEvals. Builds AI evaluation and governance infrastructure for finance, real estate and regulated software — eval harnesses, semantic data dictionaries, and AI audits.

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Industry:

Agriculture & Food

Business Function:

Operations

Executive & Strategy

Company Size:

Project Cost:

Cost not disclosed

Ownership:

Organization Type:

AI Pattern:

Data Synthesis & Reporting

Value Type:

Time Savings

AI Model:

Frequently Asked Questions

How can a multi-entity agri-food group turn trapped ERP data into daily decisions with a governed data layer?

The group worked with the experts to build a read-only, zero-write extraction layer that feeds a governed, cleaned data store. This replaced scattered exports and spreadsheets with a single governed layer and multi-domain dashboards, so named business owners now make daily decisions on volume, yield, and orders instead of monthly guesswork.

What data and AI approach was used to build the governed data layer?

The work centered on data synthesis and reporting: a read-only, zero-write ERP extraction feeding a governed clean-data store and multi-domain business-intelligence dashboards across production, finance, sales and vendor, and farmer intelligence. Specific technologies are not named in the record.

What results did the agri-food group achieve?

Dashboards for volume, yield, and orders per day are in daily use by named business owners, reporting reads the production ERP with zero writes so it cannot destabilise the live system, and farmer intelligence surfaces productivity, field-officer coverage, geography, and loss-making farm economics.

How long did the governed data layer take to deliver?

The record does not specify a timeline. The dashboards are described as being in daily use by named business owners.

Who is a zero-write governed data layer best for?

It is best suited to multi-entity operators whose decision data is locked inside production ERP systems that cannot be touched or destabilised.