Enterprise data integration · now in private beta

Map your data at the field level — in days, not months.

Schemora works out exactly how each field in one system maps to another, writes the transformation logic and SQL for you, and keeps a complete audit trail. The work that used to take a room of consultants months, your own team finishes in days.

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Field-level mapping · payload transformation · SQL dialect translation — in one platform.

Where Schemora delivers impact

Built for the industries where integration is hardest.

Ranked by impact: insurance, banking, healthcare, logistics and retail all run on systems that were never designed to talk to each other. Here's the specific problem Schemora solves in each.

Insurance · Claims & policy consolidation

Consolidate claims and policy systems after M&A — without losing a quarter to reconciliation.

Guidewire, Duck Creek and homegrown policy admin systems all name the same concepts differently. Schemora maps claimant, policy and billing fields automatically, with a confidence score and audit trail an examiner can review.

Impact: quarters of manual reconciliation → days, with an examiner-ready audit trail.

Guidewire → Duck Creek

Source field	Target field	Conf.
clm_party.frst_nm	→ claimant.first_name	0.97
pol.eff_dt	→ policy.effective_date	0.93
clm.lss_amt	→ claim.loss_amount	0.88

Banking & finance · Core banking modernization

Modernize core banking and reporting pipelines with a mapping trail examiners can trust.

Migrating off a legacy core, or standing up a new data warehouse, means mapping account, transaction and customer fields under strict lineage requirements. Schemora scores every mapping's confidence and keeps a complete audit trail.

Impact: core modernization projects move in days, with 100% of mapping decisions on the record.

Legacy core → modern ledger

Source field	Target field	Conf.
acct_mstr.acct_no	→ account.account_number	0.98
txn.txn_dt	→ transaction.transaction_date	0.95
cust.ssn_enc	→ customer.tax_id	0.83

Healthcare · EHR ↔ payer integration

Connect EHR, claims and payer systems without PHI ever leaving your environment.

Patient, encounter and claim data live in HL7, FHIR and X12 formats that rarely agree. Schemora maps and transforms them field by field, deployed inside your own VPC so protected health information stays where it belongs.

Impact: faster payer-provider integration, with RBAC and an audit trail built for HIPAA-grade review.

EHR → Payer (X12 837)

Source field	Target field	Conf.
pat_rec.dob	→ patient.date_of_birth	0.96
enc.enc_dt	→ encounter.encounter_date	0.94
clm_837.dx_cd	→ claim.diagnosis_code	0.90

Shipping & logistics · Carrier & WMS/TMS integration

Onboard carriers and reconcile warehouse data without brittle, hand-built EDI mapping.

Every carrier, warehouse system and TMS names shipment, tracking and freight fields differently. Schemora maps the fields and translates the underlying SQL, so a renamed column doesn't silently break a shipment feed.

Impact: faster carrier onboarding, with fewer pipeline failures from renamed or reformatted fields.

Carrier EDI → WMS/TMS

Source field	Target field	Conf.
shp_hdr.trk_no	→ shipment.tracking_number	0.97
frt.carr_cd	→ freight.carrier_code	0.92
wms_ord.eta_dt	→ order.estimated_arrival	0.86

Retail · Omnichannel platform migration

Unify SKU, inventory and customer data across every channel.

A POS migration, an e-commerce replatform or a merger brings mismatched product and customer schemas together. Schemora maps and transforms the payloads automatically, so inventory and orders reconcile from day one.

Impact: omnichannel data unified in days — not a multi-quarter systems-integration project.

Legacy POS → commerce platform

Source field	Target field	Conf.
itm_mstr.sku_no	→ product.sku	0.98
ord_hdr.cst_id	→ order.customer_id	0.95
inv.qty_oh	→ inventory.quantity_on_hand	0.91

The problem

Every integration starts the same way: a spreadsheet and a guess.

Connecting two systems means someone has to work out, field by field, what maps to what — across different names, formats and business rules. Then they hand-rewrite the queries and payloads. It's slow, it breaks downstream, and the knowledge walks out the door when they do.

Months of manual work

Analysts comb through schemas line by line. A mid-size migration can burn an entire quarter before a single record moves.

Brittle, hand-written ETL

One renamed column and the pipeline fails silently. Bugs surface weeks later, in production, in the numbers leadership reports on.

Knowledge that evaporates

Why was this field mapped that way? The person who knew has moved on, and the next project starts from zero.

What Schemora does

Three jobs, one platform.

Map the fields. Transform the data. Translate the SQL. Each step builds on the one before it — and on everything you've approved in the past.

Map — field level

An AI agent proposes precise field-to-field mappings with a confidence score and the reasoning behind each one. Upload DDL, JSON Schema, XSD, spreadsheets — even an ER diagram. You approve; it learns.

Transform — the real data

Once mappings are approved, Schemora rewrites actual SQL, JSON and XML payloads into the target format — renaming, casting and computing each field automatically. Nothing is silently dropped.

Translate — any SQL dialect

Convert queries between dialects — MySQL to PostgreSQL, Oracle to Snowflake, T-SQL to Spark — preserving the logic while adapting syntax, functions and data types to the target.

Explore the full platform

See it in action

Watch Schemora map real data.

A short tour — field-level mapping, transformation and SQL translation, end to end.

A concrete example

From two mismatched schemas to working SQL.

Here's the same project, end to end: Schemora proposes the field mappings, you approve them, and the queries rewrite themselves into the target system and dialect.

Proposed mappings

Source field	Target field	Conf.
clm_party.frst_nm	→ claimant.first_name	0.97
clm_party.dob	→ claimant.birth_date	0.95
pol.eff_dt	→ policy.effective_date	0.93
clm.lss_amt	→ claim.loss_amount	0.88
abs_req.rsn_cd	→ leave_request.reason_code	0.74

MySQL → PostgreSQL

-- before: source names, source dialect
SELECT frst_nm,
       GROUP_CONCAT(clm_id)
FROM   clm_party
WHERE  dob < '1990-01-01';

-- after: mapped names, target dialect
SELECT first_name,
       STRING_AGG(claim_id, ',')
FROM   claimant
WHERE  birth_date < '1990-01-01';

Why it keeps getting faster

It gets sharper every time you use it.

Every mapping you approve becomes reusable institutional knowledge, stored in a mapping knowledge graph and replayed on the next project that touches similar systems.

Known mappings return in milliseconds — and at zero LLM cost. Schemora checks curated knowledge before it ever spends a token.
Your approvals train the next run. Accept or reject a mapping and it becomes an example the model learns from.
Ambiguity is flagged, not guessed. Uncertain fields trigger a "did you mean…?" before anything is committed.

Retrieval cascade

# trust order — stop as soon as confident
A   semantic match on past approvals
B1  curated mapping in the graph
B2  SQL lineage path (DERIVES)
B3  shared business-term bridge
C   schema search + key proximity
        └─ LLM drafts only what's left

Built for the enterprise

Designed for regulated, data-heavy organisations.

Governed by default

Role-based access, project-level permissions, and an immutable before/after audit trail on every change.

Your environment

Deploy in your own VPC or on-premise. Bring your own models and keys — your data and schemas stay yours.

Knowledge that stays

Every decision and its rationale is captured, so institutional knowledge survives staff turnover.

See enterprise & security

<1s

Lookup for an already-known mapping — no model call required.

5-tier

Retrieval cascade consults trusted knowledge before spending an LLM token.

100%

Of state-changing actions captured in the audit trail, with full attribution.

Get started

See Schemora run on your own schema.

Bring a real source and target system to a 30-minute working session. We'll map live fields together — no slideware.

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