Data-quality and migration agents for S/4HANA where the model proposes and a deterministic validator disposes. Every rule and every field mapping leaves the pipeline with a proof attached — or it doesn't leave at all. This is what removes the hallucination risk that keeps generative AI out of your master-data path. The cloud is the seed, not the engine.
each deliverable · validated against schema · on-box · signedYour teams don't hesitate to put an LLM near SAP master data because it's slow — they hesitate because a confident wrong answer in a customer address rule or a field mapping surfaces at cutover, as rework or a failed load. A copilot that is right 95% of the time still needs 100% human review, so the promised savings evaporate.
Canonic changes the guarantee. Instead of asking a model to be careful, we make an invalid rule structurally unable to reach the deliverable. A candidate that references a field that doesn't exist in the target, a mapping that silently drops data, a transform that can't round-trip — is rejected by construction, before a data steward ever sees it.
Reads functional documentation, business rules, and a seed rule catalog; emits a far larger catalog adapted per data domain (Customer, Material, Finance…) and per industry.
Analyses the legacy and target models and proposes correspondences in both directions — Legacy → S/4HANA and back — completing partial mappings and flagging gaps.
One model proposes; a deterministic engine disposes. The intelligence is free to be creative because nothing it invents can escape the check.
The model reads the docs and generates candidate rules, SQL, mappings, transforms.
Each candidate is compiled and checked against the real schema. Fails → rejected, never surfaced.
What survives is sealed: a tamper-evident signature plus a plain-language statement of the rule it enforces.
The data steward signs off at the gate. Nothing auto-applies to a system of record.
| Dimension | Typical GenAI copilot | Canonic — proof-carrying |
|---|---|---|
| Trust in output | Plausible; needs full human review | Validated against schema before you see it |
| Failure mode | Confident, wrong answer | Refuses or flags — never fabricates |
| Data exposure | Client data sent to a cloud model | Runs on-box; processing stays on-premise |
| Auditability | Prompt/response logs | Signed, content-addressed artifact per rule |
| Migration safety | Mapping asserted correct | Reversibility tested per field |
| Scale | Prompt-per-rule, effort grows linearly | Distill a rule-class once, generate the catalog |
On-box executionClient SAP data is processed within the client boundary — it does not leave to reach a cloud LLM. This clears the security and procurement gate that stops most generative-AI pilots in regulated SAP estates.
Dual attestationEach deliverable carries two proofs: a cryptographic signature (who, what, when — tamper-evident) and a readable governance statement of the business rule it enforces.
Drop-in audit evidenceBecause every rule and mapping is a signed, content-addressed artifact, the migration's evidence trail is generated as a by-product — ready for GDPR, SOX, and internal audit.
Human in the loopThe steward approves at the gate. The agents accelerate analysis and design; they never silently write to a system of record.
Pick one data domain — Customer or Material. Seed us with your own field-mapping sheet and rule catalog. We return the enriched, schema-validated catalog, the reversible mapping, and the full attestation trail — on your infrastructure, on your data.
You measure the two numbers that decide whether this belongs in a delivery: how many unproven rules reach the deliverable, and how much analyst time it removes.