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

Where the mismatch actually hides

A system migration almost never fails at the plan stage. It fails three weeks after go-live. 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. By then it is the most expensive point to discover it, since the old system is often half decommissioned.

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 category in the other. A relationship that was implicit in one schema has to be made explicit in the other. Whoever is doing the migration has to make hundreds of small mapping decisions. Any one of them 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 still means the same thing.

The second failure mode is duplicates and partial records that accumulate over the old system’s life, things nobody 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. Duplicate customers, orphaned records, inconsistent formatting: whatever it finds becomes the migration’s problem to solve, even though it was not created by the migration.

What the agent checks before and after the move

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. It validates records against the old system’s own rules before anything moves, catching a mismatch while it is still cheap to fix. The migration itself runs in batches with rollback on failure, so a bad batch does not leave the destination system half-migrated.

After the move, the agent reconciles every migrated record against the source, one by one, not a sample. It 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. The mapping and validation logic gets tested against your actual data’s quirks before the real migration happens. Every record moved and every exception raised gets written to a full log.

Where the call still sits with your team

Deciding how to resolve a specific mismatch stays with a person who knows the data’s history. Same for whether a duplicate record should be merged, deleted or kept as two separate entities, and whether an unusual legacy record is even worth migrating. 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.

Why “zero silent failures” is an actual property, not a slogan

Reconciliation runs against every migrated record, not a sample. That is what makes “zero silent failures” real: every mismatch gets reported, nothing gets quietly dropped or guessed at. A dry run on a sample always happens first, before the real migration touches live data. 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. It tapers 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 turns 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 does not repeat the same loss. Internal knowledge base Q&A helps a team 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, see the factory ERP recovery case study. For infrastructure stood up cleanly from scratch, see 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.

Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →