Establishes what data is moving, in what shape and how clean it is, before your team commits to an approach or a date.
An AI agent for data migration scoping establishes the facts about a customer's existing data before your team designs the migration: what records are moving, how many there are, what system they sit in now, what shape the export takes, which fields have no equivalent on your side, and — the question that decides everything — how clean the data actually is. Migrations are estimated from a description and delivered against reality, and the gap between the two is where implementation timelines die. Nobody is being dishonest; the customer genuinely believes their records are in good order because nobody has looked at them in five years. The agent asks the questions that surface the truth early, and routes the approach decision to an engineer rather than making it.
Gets to the real state of the data before anybody estimates.
Establishes what record types are in scope and how many
Records the source system, version and export format
Identifies fields with no equivalent on your side
Asks the questions that reveal actual data quality
Flags anything requiring a transformation decision
Routes the migration approach to an engineer, never decides it
The single most common cause of a blown implementation date is a migration estimated against the customer's description of their data rather than the data itself. The customer is not misleading anybody. They have a system that has been running for years, staffed by people who left, containing conventions nobody documented, and their honest belief is that the records are fine. Then the export arrives with duplicate identifiers, free-text in fields that should be enumerated, and a decade of rows where somebody used a note field to record something the system had no place for. Scoping properly means asking questions that get past the description: not "is your data clean" but "how many records have no email address" and "what happens today when two customers share a name". Those questions are consistent across every migration, which is exactly what makes them worth automating.
Establish the scope, test the quality, route the approach.
Record types, volumes, source system and export format, stated rather than assumed.
Not whether the data is clean, but the concrete questions whose answers reveal whether it is.
Transformation and mapping calls go to an engineer with the findings attached.
A question about duplicate identifiers that saved the estimate.
Scenario: an implementation team had overrun on three consecutive migrations, each time because the data turned out worse than described. A new customer is moving roughly 400,000 contact records from a legacy system. Asked directly, the project lead says the data is in good shape. The agent does not stop there: it asks how the current system prevents two contacts sharing an email address, and the answer is that it does not. It asks what proportion of records were created before the current data entry process, and learns that about a third predate it and follow no convention at all. Neither answer is a surprise once asked, and neither would have surfaced from "is your data clean". The findings go to an engineer, who scopes a deduplication pass that nobody had budgeted for and moves the estimate by nine days — before the date was committed rather than after. The customer accepts the revised timeline because it arrives with the reason attached.
Anybody who has overrun on a migration that looked simple.
Migration overruns are the most common source of a blown date.
Your engineers estimate against a description they cannot verify.
The findings you need are questions nobody thought to ask.
The transformation approach depends on facts gathered too late.
An unbudgeted deduplication pass comes out of margin.
A revised estimate lands well before commitment, badly after.
Where the findings land and who makes the call.
Holds the migration scope: record types, volumes, formats, findings.
Receives the sample export the customer provides.
Routes transformation and mapping questions to an engineer.
Runs the scoping questions with the customer's data owner.
Publishes the migration scope into the project space.
Reports which data quality findings recur across customers.
The migration questions that change an estimate.
Questions about scoping a migration honestly.
An AI agent for data migration scoping establishes what records are moving, in what volume and format, which fields have no equivalent, and what condition the data is actually in — then routes the migration approach to an engineer rather than deciding it.
Because the honest answer is almost always yes and it is almost always wrong. Specific questions — how duplicates are prevented, what predates the current process — get to the real state without implying anybody has been careless.
No. Whether to transform on the way in, clean at source or migrate and remediate afterwards is an engineering and commercial decision with real trade-offs. The agent supplies the findings that decision needs.
It should ask for one, because a sample settles in ten minutes what a questionnaire argues about for a week. But it should still collect the answers, since a sample rarely covers the oldest and worst records.
Before the date is committed. A revised estimate delivered during scoping is a conversation; the same revision delivered in week five is a broken promise, even though the underlying facts are identical.
That is itself a finding worth escalating. Reluctance to look at the data before a migration is a strong predictor of what the export will contain.
Which data quality problems recur across your customer base. If the same issue appears in most migrations, it belongs in your standard scope and your standard estimate rather than being rediscovered each time.
Establishes what data is moving, in what shape and how clean it is, before your team commits to an approach or a date.