Months of data migration.
Done in days.

Every migration is priced fixed and scoped — no billable-hour meter, no seat pricing. Consulting-led delivery for comparable migrations typically runs $75K–$600K for most programs and $600K–$1.5M+ at enterprise scale; Settle typically comes in at roughly half the consulting-led equivalent. Book a scoping call and you'll have your number.

Either. For one-time moves — an M&A consolidation, a platform switch — most teams have us run the migration end to end as a fixed-price engagement. Teams that migrate continuously, like customer onboarding or professional services groups, license the platform and run it themselves. Both run on the same engine, with the same validation and the same audit trail.

No. The model only proposes mappings and transforms. Deterministic engines validate every row, and nothing loads until your team approves the export. Settle holds no standing write access to your target.

A consulting engagement solves migration with headcount — analysts hand-building mappings and checking samples, billed by the hour, for months. Settle solves it with software: the agent does the heavy lifting, deterministic engines check every row instead of a sample, and a small team delivers in days at a fraction of the cost. You also keep a versioned, re-runnable package and a full audit trail — not a deck.

Two ways. First, Settle compares what should be in the target against what’s actually there — record counts, key resolution, and field-level values, for every record, not a sample. Second, it replays your real business workflows in the live system and records the evidence, so you see the migrated data working — a quote created, an order invoiced — before cutover, not after.

They're flagged with a root cause — source data, mapping, transformation, or target configuration — and a proposed fix. Nothing is silently dropped and nothing is silently loaded. You apply or dismiss each fix, and a workflow only counts as passed when a re-run proves it.

Files (CSV, Excel, JSON, SQL dumps), direct database connections, one-click connectors for major systems, or a DDL script when that's all you have. If your migration path isn't in the directory, ask — the engine is source-agnostic.

Data is encrypted in transit and at rest, source data isn’t retained beyond the migration, and nothing is ever used to train a model. AI processing runs on Anthropic Enterprise LLMs. Full security documentation is available under NDA.

Yes — private-cloud deployment is available on demand, so source data doesn’t have to leave your perimeter.

Days, not months — the exact number depends on volume, complexity, and how quickly your experts can answer the discovery questions. We scope it up front with a fixed price, and you see a full dry run before anything commits.

A typical enterprise ERP migration runs 6–12 months with traditional approaches; the mapping is fast, but exceptions found late in mock loads stretch the timeline. Settle compresses this to days by surfacing exceptions up front and validating every record automatically before cutover.

AI-powered data migration uses machine learning to propose schema mappings and transformations that engineers previously built by hand. Settle pairs that AI with deterministic validation engines that check every row against the target system's rules — so the speed of AI comes with proof, not trust.