Map the fields. Transform the data. Translate the SQL.
Schemora is one platform for the whole integration job — from working out what maps to what, to rewriting the real payloads and queries that move your data across.
AI field-level mapping you can trust.
Describe what you need in plain English — "map the claims tables to the absence system" — and an AI agent proposes precise field-to-field mappings, each with a confidence score and the reasoning behind it.
- Reads almost anything. DDL, JSON Schema, XSD, CSV, Excel, PDF, Word, SQL — and ER diagrams via vision OCR.
- Confidence-scored, with rationale. Every proposal shows its score and why it was made, so reviewers spend time only where it counts.
- Human-in-the-loop. Approve or reject from chat. A safety gate stops nonsense matches (no SSN → first name).
- Version snapshots & export. Freeze a mapping set before a re-run, compare versions, and export to CSV, Excel or JSON.
| Source field | Target field | Conf. |
|---|---|---|
| clm_party.frst_nm | → claimant.first_name | 0.97 |
| clm_party.dob | → claimant.birth_date | 0.95 |
| clm.lss_amt | → claim.loss_amount | 0.88 |
| abs_req.rsn_cd | → leave_request.reason_code | 0.74 |
Rewrite the real payloads, automatically.
A mapping is only half the job. Schemora uses your approved mappings to rewrite actual source payloads into the target format — renaming keys, casting types and computing values — with no extra configuration.
- SQL, JSON, XML and delimited text. Column references, field keys and element names are remapped to their target equivalents.
- Nothing dropped in silence. Unmapped fields are surfaced, and output built from not-yet-approved mappings is clearly flagged for review.
- Interactive or API. Compare before/after in the UI, or call it from your pipeline over REST.
// source record { "frst_nm": "Maria", "dob": "1986-03-04", "lss_amt": 4200 } // transformed to target { "first_name": "Maria", "birth_date": "1986-03-04", "loss_amount": 4200.00 }
One query, any SQL dialect.
A dedicated translation pipeline converts queries from any source dialect to any target — keeping the logic identical while adapting syntax, functions and data types.
- MySQL → PostgreSQL, Oracle → Snowflake, T-SQL → Spark — and more. CTEs, joins, subqueries and window functions are preserved.
- AST-based, not find-and-replace. Queries are parsed to a dialect-independent tree, field names resolved through your mappings, then rendered in the target dialect.
- Column renames come for free. Translation reuses the same project mappings, so names and dialect are fixed in one pass.
-- Oracle SELECT NVL(rsn_cd, 'NA') FROM abs_req WHERE ROWNUM <= 100; -- Snowflake SELECT COALESCE(reason_code, 'NA') FROM leave_request LIMIT 100;
Underneath: a system that compounds.
Mapping and transformation sit on a stack that gets smarter — and cheaper to run — with every project.
Mapping knowledge graph
Curated, lineage-derived and approved mappings are stored as typed relationships, so past decisions become reusable knowledge for the next project.
Hybrid retrieval
Your business rules and data dictionaries are indexed with vector and keyword search, fused and reranked — so proposals are grounded in your documentation.
Learns from feedback
Every approval becomes a scored example injected into the next run. Rejections are remembered too, so the same mistake isn't repeated.
Fast, local routing
A lightweight on-box classifier handles routine chat in under a millisecond, reserving the LLM for the decisions that actually need it.
Cost-aware by design
The five-tier cascade exhausts trusted, zero-cost sources before any model is called — known work simply doesn't cost tokens.
Built to integrate
REST APIs and per-user tokens let you drive mapping, transformation and translation from CI/CD and your own tooling.
Bring a schema. We'll map it live.
A 30-minute working session on your real systems beats any slide deck.