ON-PREMISE · NO DATA LEAVES YOUR NETWORK
Real data patterns.Zero real data.
IntactSynth reads your database schema — SQL Server, PostgreSQL, MySQL, MariaDB, Oracle or SQLite — and understands every foreign key, every check constraint, every business rule, then generates a statistically faithful synthetic database that imports back into your test environment with one command.
Designed for teams who cannot send a single byte of production data outside their network.
- Schema analyzed
- 47 constraints discovered
- 1,000,000 rows generated
- Referential integrity: 100%
Schema-faithful synthesis, table by table
01/THE PROBLEM
You need real data. You cannot use real data.
Your AI team needs millions of realistic banking transactions to train a fraud detection model. Your test environment needs production-shaped data to catch regressions before deployment. Your new analyst needs a sandbox to learn the schema before touching real records.
Sending production data to a developer's laptop violates KVKK Article 6. Anonymizing it breaks every foreign key. Generating it manually takes weeks and still looks fake.
Most teams accept the compromise. We don't think they should have to.
02/HOW IT WORKS
Four stages. One command. Zero leakage.
01 / READ
Schema Intelligence
IntactSynth connects to your source database — SQL Server, PostgreSQL, MySQL, MariaDB, Oracle or SQLite — with read-only metadata queries, never touching actual data values. It maps every table, foreign key, constraint, and trigger.
02 / UNDERSTAND
Semantic Analysis
The engine identifies what each column means: an IBAN, a credit score, a transaction timestamp. It learns business rules from check constraints and trigger code.
03 / GENERATE
Statistical Synthesis
Rows are generated table by table in topological order. Distributions match real-world banking patterns. Every foreign key resolves to a valid parent.
04 / VALIDATE
Integrity Guarantee
Before export, the Quality Guardian validates 100% of referential integrity, all check constraints, and all business rules. If it doesn't pass, you don't get it.
03/FOR AI/ML TEAMS
Better training data. Without the compliance risk.
Every machine learning model in finance, healthcare, and e-commerce is hungry for one thing: more realistic data. But the data you can train on is rarely the data you'd train on if compliance let you.
IntactSynth produces synthetic datasets that preserve the statistical properties of your real data — the distributions, the correlations, the rare-event patterns your model needs to learn — while containing zero information traceable to any real individual.
Train your fraud detection model on a billion synthetic transactions. Test your credit scoring algorithm on a synthetic loan portfolio. Develop your patient risk classifier on a hospital schema that was never a hospital.
USE CASES
- Fraud model training
- Credit risk simulation
- Recommendation system development
- Anomaly detection benchmarking
- A/B test data preparation
- Schema migration validation
- New employee training environments
- Vendor sandbox provisioning
WHAT YOU GET
- Pareto-distributed account balances
- Log-normal transaction amounts
- Realistic NPL rates (3–5% per BDDK)
- Valid TC Kimlik / IBAN / Luhn IDs
- Coherent date sequences (apply → approve → disburse)
- Status-machine transitions
- Cross-table consistency guarantees
04/ARCHITECTURE
Your network. Your hardware. Your control.
NEVER LEAVES YOUR NETWORK
IntactSynth runs entirely inside your perimeter. No cloud API calls. No telemetry. No phone-home. The license check happens offline.
NO PRODUCTION DATA INGESTED
The engine reads schema metadata only — the database's own catalog and information-schema views (sys.* on SQL Server, pg_catalog on PostgreSQL, ALL_* on Oracle, sqlite_master on SQLite). Production rows never enter the synthesis pipeline.
KVKK / GDPR ARTICLE 89 COMPLIANT
Synthetic data is not personal data. Our generation process is documented for your compliance team and audit trails.
05/PRICING
On-premise licenses. Annual or perpetual.
Every tier includes unlimited row generation and full feature access — you're paying for support, updates, and the depth of engagement your environment needs.
DEPARTMENT
For single-team usage.
$4,900 / year
- Any 1 database engine of your choice
- Up to 50 schemas
- 1 production server
- Unlimited row generation
- Email support
- Quarterly updates
ENTERPRISE
Most popularFor organization-wide rollout.
Contact us for pricing
- All 6 engines (SQL Server, PostgreSQL, MySQL, MariaDB, Oracle, SQLite)
- Unlimited schemas
- Unlimited production servers
- Unlimited row generation
- Dedicated account manager
- Priority support (4h SLA)
- Custom business rules
- Monthly updates
- Implementation workshop
REGULATED
For banks, hospitals, and critical infrastructure.
Custom pricing
- Everything in Enterprise
- Air-gapped deployment + fully local LLM backend
- On-site installation
- Compliance documentation package (KVKK, BDDK, ISO)
- Source code escrow
- Custom SLA
- Training program (5 days)
The Department price is an annual subscription per production server, billed yearly and excluding VAT; TRY and EUR equivalents are available on request. Enterprise and Regulated licensing — perpetual licenses, additional servers, air-gapped deployments — is quoted per engagement. Perpetual licenses include 12 months of updates; annual maintenance afterward is 20% of license value.
06/UNDER THE HOOD
Engineered by a DBA, not a startup.
IntactSynth was built by Onur Kahveci — 20+ years in enterprise IT, 10+ of them as a production SQL Server DBA across financial institutions in Türkiye. Every architectural decision — the read-only metadata extraction model, the topological sort for foreign-key ordering, the deferred-constraint strategy for circular references — comes from real-world database operations, not academic abstraction.
Today the same engine speaks six databases — SQL Server, PostgreSQL, MySQL, MariaDB, Oracle and SQLite — and respects each one's primitives the way only someone who has fixed broken constraints at 3 a.m. understands them.
-- IntactSynth reads only this kind of query:
SELECT
fk.name,
OBJECT_NAME(fk.parent_object_id) AS parent_table,
OBJECT_NAME(fk.referenced_object_id) AS referenced_table,
fk.delete_referential_action_desc
FROM sys.foreign_keys fk;
-- It never executes this kind of query:
-- SELECT * FROM customers; ✕
-- SELECT name, email FROM users; ✕07/QUESTIONS
Answers for the people who ask the hard ones.
Is synthetic data the same as anonymized data?
No. Anonymized (or masked) data is real data with identifiers removed or perturbed — it still originates from real records, and the moment you anonymize a child table you usually break its foreign keys. IntactSynth generates entirely new rows that never derive from any real record, yet preserve the schema's structure and the data's statistical shape. There is nothing to re-identify because nothing real was ever there.
Does IntactSynth ever read production data values?
Never. It connects with read-only metadata queries against the database's information-schema and catalog views — sys.* on SQL Server, pg_catalog on PostgreSQL, the ALL_* views on Oracle, sqlite_master on SQLite — to learn tables, columns, foreign keys, check constraints, indexes and triggers. It does not execute SELECT statements against your tables. Production rows never enter the synthesis pipeline.
Which databases and versions are supported?
Six relational engines in v1: Microsoft SQL Server 2016–2025 (including Azure SQL Database), PostgreSQL 14–17, MySQL 8.0–8.4, MariaDB 10.6+, Oracle 19c/21c/23c, and SQLite 3.35+. Engine-specific schema features — computed columns, sequences, temporal tables, filtered indexes, INVISIBLE columns, deferred (NOCHECK) constraints — are understood by the metadata layer. v1 does same-engine round-trips; cross-engine output (read one engine's schema, emit another's dump) lands in v1.1.
Is the synthetic output KVKK / GDPR compliant?
Synthetic data produced by IntactSynth contains no information traceable to any real individual, so it falls outside the definition of personal data under KVKK and the GDPR. GDPR Article 89 explicitly recognizes processing for statistical and research purposes; our generation process is documented so your compliance team can attach it to their records of processing and audit trails.
How does it handle circular foreign keys?
Tables are inserted in topological order; when the dependency graph contains a cycle, IntactSynth breaks it with a deferred-constraint strategy — inserting rows with provisional references inside a transaction, then resolving and validating them before commit. The same approach handles self-referencing tables (org charts, account hierarchies).
Can it run fully air-gapped?
Yes. IntactSynth runs entirely inside your perimeter with no outbound network calls, no telemetry and no phone-home. License validation is performed offline. Air-gapped deployment, on-site installation and source-code escrow are available on the Regulated tier.
How is the license enforced if there's no internet?
Licensing uses an offline, signed-token model: a license file scoped to your organization and server count is validated locally. There is no activation server to reach and no usage data is transmitted.
How long does generation take for a large schema?
Schema analysis on a 500-table database typically completes in seconds because it only reads metadata. Row generation scales with the volume you ask for and your hardware — generating tens of millions of rows across a realistic banking schema is a matter of minutes on a modest server, with throughput tunable per table.
Can I define custom business rules?
Yes. Beyond the rules IntactSynth infers from check constraints and trigger code, you can declare additional invariants — value domains, conditional dependencies between columns, status-transition rules, distribution shapes — and the Quality Guardian validates them before export. Custom business rules are included from the Enterprise tier.
Do you support PostgreSQL, Oracle, MySQL or NoSQL databases?
PostgreSQL, MySQL, MariaDB, Oracle and SQLite are all supported today, alongside Microsoft SQL Server — that's where the founder's deepest experience is and where the constraint semantics are richest. NoSQL stores are not on the roadmap: IntactSynth is built around relational structure and referential integrity. If you have a concrete schema in another relational engine, tell us about it.
Plain definitions
- Synthetic data
- Artificially generated records that do not correspond to any real individual or event, produced to mirror the structure and statistical properties of a real dataset.
- Referential integrity
- The guarantee that every foreign-key value in a child table points to a row that actually exists in the parent table — the property anonymization tools routinely destroy and IntactSynth validates at 100%.
- On-premise generation
- Producing the synthetic database on hardware inside your own network, so no schema metadata and no generated data ever leave your perimeter.
Synthetic generation vs. the alternatives
| Approach | Referential integrity | Privacy risk | Effort to refresh | Statistical realism |
|---|---|---|---|---|
| Copy production data | Perfect | Unacceptable — KVKK Art. 6 | Low | Perfect |
| Anonymize / mask production | Frequently broken | Re-identification risk remains | Medium | Degraded |
| Hand-written mock data | Manual, brittle | None | Weeks per schema | Looks fake |
| IntactSynth synthesis | Validated at 100% | None — nothing real exists | One command | Distribution-faithful |
08/TALK TO US
Most conversations start the same way.
“We have an AI team that needs realistic data. Our compliance officer keeps saying no.”
If that sounds familiar, send us a note. We respond within 24 hours during business days (TRT). For regulated industries — banking, insurance, healthcare — we offer a 60-minute discovery call to understand your schema constraints before talking pricing.
Onur Kahveci · Founder
Kırklareli, Türkiye
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