About the customer

A global risk and compliance software platform running around 1,000 customer data migrations a year across 45 project managers. Every customer they sign has to have their risk and control data loaded before go-live: five levels of hierarchy, cross-linked, arriving as a spreadsheet the end customer fills in themselves. That load is the gate every implementation has to pass through.

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The Challenge

A setup that worked was being asked to do more than it was built for

They had already invested in a licensed enterprise ETL platform, and it moved the data. As volume and schema complexity grew, the work around the load rather than the load itself became the cost: roughly 7,000 hours of rework a year, or about $840,000.

Errors surfaced after the run, not before it

Around 70% of the time a user hit an error mid-process, the message did not point to anything they could act on. It escalated to the technical team, or the whole file went back to the customer to redo. Either way the migration paused, and the project manager waited.

Loads that stopped halfway had to be reconciled by hand

Their own API is rate-limited. On a large file the pipeline could stop mid-run, with no clear record of what had been written and what had not, so someone had to work that out manually before anyone could restart.

Every product change meant reworking the pipeline

Add a field to the platform, and an engineer reworked the ETL flow. Professional services leadership described it as a standing tax on the team, paid out of the same capacity that was meant to go to customers.

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The Solution

Validation before a single record is written

The project manager uploads the spreadsheet and works with the agent in plain English. Before writing anything, the agent checks the whole file against the live system, reports what it will create and what needs attention, then offers three choices: load everything, load only the clean rows, or stop and fix the file first.

A fix list sorted by who can act on it

Instead of one output file of errors for someone to decipher, problems come back named by row, column and field name, grouped by owner: what the customer needs to correct, what an admin can resolve, what belongs in user setup, and what needs a review.

Loads that resume where they stopped

When a run is interrupted, the agent picks up from the last written record with no duplicates. Nobody reconciles the state by hand, and nobody restarts a large file from the top.

No rebuild when the product changes

The agent reads the field list from the file rather than a hardcoded mapping, so adding a field to the platform no longer pulls an engineer into the migration path.

Verification inside the run

Every written field is read back and compared automatically, rather than waiting for a separate QA pass once the load is done.

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The Impact

Migration rework down between 52% and 72%

Rework fell from around 7,000 hours a year to between 1,900 and 3,400, worth roughly $231,000 to $405,000 against a starting cost of about $840,000. That is 3,600 to 5,100 hours a year back with the delivery team.

Accuracy proven on their own data

They ran a full migration through the agent and verified it independently against their existing ETL tool: 737 records with the full hierarchy and cross-links intact, 4,562 field-level comparisons in their own QA, and zero discrepancies.

Errors caught before the load, not after

They also tested the agent adversarially, hiding six deliberate errors in a file: a duplicated column, a blank identifier, an invalid ID, and records sharing an ID while disagreeing on their contents. The agent found five of the six before writing anything, named the row and column for each, and refused to load until they were fixed.

A return that starts in month one

Projected annual value of $435,000 conservative to $609,000 realistic, against an annual cost of about $100,000. A net position of $335,000 to $509,000, or 4.4x to 6.1x, from the month it goes live.