Data moved between systems,
checked before it lands, not after

The process today

A system migration almost never fails at the plan stage. It fails three weeks after go-live, when someone notices a customer record with two different phone numbers in two different places, or a report that no longer adds up because a field was mapped to the wrong column. The mismatch was there the whole time; it just was not visible until the new system had already become the one people rely on, which is the single most expensive point to discover it, since by then the old system is often half decommissioned and reconstructing the original record is real work.

The core difficulty is that the old and new systems rarely model data the same way. A field that was free text in one system might be a strict enum in the other, a relationship that was implicit in one schema has to be made explicit in the other, and whoever is doing the migration has to make hundreds of small mapping decisions, any one of which can silently drop or distort data if it is wrong. Spreadsheets and export scripts can move the data; they rarely check whether what landed on the other side actually still means the same thing.

The second failure mode is duplicates and partial records that accumulate over the old system’s life, things that were never cleaned up because they never caused a visible problem. A migration is often the first time anyone actually looks closely at years of accumulated data, and what it finds, duplicate customers, orphaned records, inconsistent formatting, becomes the migration’s problem to solve even though it was not created by the migration.

What the agent does

The agent builds and applies a field mapping between the source and destination systems, whatever those are, a CRM, an ERP, a spreadsheet or a custom legacy system, and validates records against the old system’s own rules before anything moves, catching a mismatch while it is still cheap to fix rather than after it has landed. The migration itself runs in batches with rollback on failure, so a bad batch does not leave the destination system in a half-migrated state.

After the move, the agent reconciles every migrated record against the source, one by one, not a sample, and produces a mismatch and duplicate report instead of assuming silence means success. Before any of this touches real data, it runs a full dry run on a representative sample, so the mapping and validation logic get tested and adjusted against your actual data’s quirks before the real migration happens. Every record moved and every exception raised along the way is written to a full log.

What stays with humans

Deciding how to resolve a specific mismatch, whether a duplicate record should be merged, deleted or kept as two separate entities, and whether an unusual legacy record is even worth migrating, all stay with a person who knows the data’s history. The agent reports every mismatch; it does not guess at a resolution or silently pick one side. Signing off that a migration is complete and the old system can be retired is also a human decision, made after reviewing the reconciliation report, not before.

Guards

Reconciliation runs against every migrated record, not a sample, which is what makes “zero silent failures” an actual property of the process rather than a slogan: every mismatch gets reported and nothing gets quietly dropped or guessed at. A dry run on a sample always happens first, before the real migration touches live data, and batches roll back on failure instead of leaving a partially migrated system behind. Data moves only through the access granted to both systems, and we do not keep a separate copy once the migration is verified and closed.

Price and timeline

Running cost is usually $20 to $70 a month in model usage for the migration itself, tapering off once the move is reconciled and closed, with a budget cap set before launch.

Related

If the source system holds scanned documents rather than clean records, document OCR and extraction handles turning those into structured data before this agent maps and moves it. Once data lands safely in the new system, SaaS backup and monitoring automation makes sure a lockout or sync failure on the new platform does not create the same kind of loss all over again, and internal knowledge base Q&A helps a team actually find things in the new system once the structure has changed. For a real-world case where a full system had to be rebuilt from an export rather than a clean migration, see the factory ERP recovery case study, and for infrastructure that had to be stood up cleanly from scratch, the private network service case study. More on the broader approach is on the AI agents service page and the automation-everything overview.

Planning a migration you would rather not find mismatches in three weeks from now? Get in touch and we will look at your source system first.

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