Convert & Validateyour migration — without touching your data.
Convert SQL, code, and workflows — free. Then validate any conversion, ours or yours, by running it on deterministic synthetic data and diffing the results — at any scale, up to a million rows. No database connection, no credentials, nothing leaves your boundary.
No signup · no production access · no warehouse credentials · nothing leaves your boundary · runs in seconds.
Convert to cutover, one governed path
Every artifact travels the same governed path — you always know where you are and what’s been validated.
- DiscoverMap the whole estate + dependencies
- AssessReadiness, risk & wave plan
- ConvertDeterministic-first, AI where needed
- ValidateRun it, diff the rows
- ProveAdversarial + signed evidence
- ApproveHuman decision gate
- CutoverAuthorized go-live
- ObservePost-cutover drift watch
See the agents work
A conversion isn’t one call to a model. It’s a pipeline of specialists — a deterministic converter, an AI pass for the hard parts, a validator that re-runs the result on synthetic data and row-diffs it, and a signed record of the verdict.
- Deterministic converterQueuedqueued
- AI passQueuedqueued
- Execution validatorQueuedqueued
- SkepticQueuedqueued
- Evidence ledgerQueuedqueued
Agents propose and verify. Humans approve every cutover — and the LLM never decides pass/fail.
Validation, not a score
A 90% headline means nothing if it only compiled. We show exactly how deep the validation goes — from “it parses” to “it ran and matched rows” to “it held under adversarial data.”
- DeclaredA semantic contract exists
- ParsedCompiles in the target dialect
- ExecutedActually ran — deployed AND runs
- Row-equivalentSource ≡ target on synthetic data
- Adversarially provenHeld under edge-case data
- Proven on real dataconnected evidenceReconciled on your warehouse
Synthetic simulation is never presented as real-data reconciliation.
Built to validate, not just convert
Whole-estate, not one file
We map every table, view, proc and pipeline into one dependency graph with a provable build order — then migrate in waves.
Deterministic-first, AI where it counts
Grammar-level conversion handles the bulk with zero LLM. The model is a pass for the long tail, not the whole story.
Validated, not promised
Every conversion is re-run on deterministic synthetic data and row-diffed against the source — at any scale, up to a million rows. No connection, no credentials.
Governed by humans
Agents propose; a person approves every cutover. The LLM never decides pass/fail. Read-only by default.
Query, code, ETL, schema, lineage and risk conversion are all included at no charge. They feed one thing: validation that your migration returns the same results.
JarvisQuery
JarvisCode
JarvisFlow
JarvisSchema
JarvisGraph
JarvisRisk
JarvisDataQuestions, answered
Convert it free. Validate it before you cut over.
Run any conversion — ours or yours — against deterministic synthetic data and see the row-level diff in seconds. No connection, no credentials, nothing leaves your boundary.