Two integrated modules, both supported by AI. Explore model engineering and data management, from inventory to privacy.
From visual editing to documentation, with contextual AI and organization-specific standards.
Modern ER editor built for large models, with version governance and efficient search.
International standard for ER diagrams. Cardinality and optionality represented unambiguously.
Group objects by domain (HR, finance, etc.) with navigation independent of the macro diagram.
Find any object or attribute in milliseconds. Works in models with over a thousand tables.
Change history with visual diff between versions. Commit comments. Granular rollback.
Diagrams with hundreds of entities without slowdown. Incremental rendering as you zoom.
Diagram organization algorithms. Suggests arrangements that reduce line crossings.
The editor is desktop-first. It works on tablets with productivity limitations. Mobile modeling is not an intended use case.
Real-time collaborative editing (multiple users on the same diagram simultaneously) is on the roadmap, not yet available.
Configurable DDL generation for major relational and analytical databases on the market.
Oracle, PostgreSQL, SQL Server, MySQL, MariaDB, DB2, Snowflake, BigQuery, Redshift, Synapse, Teradata, SQLite, MongoDB.
Configure prefixes, suffixes, and conventions per organization. Automatic application during generation.
ALTER statement generation based on version diffs. No need to rebuild everything from scratch.
Tablespaces, partitioning, specific indexes, and canonical types can be configured per target.
Define abstract types (e.g., "monetary_value") that translate to the native type of each database.
Generation templates can be adjusted to reflect internal standards without rewriting the tool.
Legacy or niche databases (e.g., Informix, Sybase ASE) may require adaptation. Evaluated case by case.
Extreme proprietary features (e.g., database-specific geospatial types) may not have direct equivalents in other targets — generation explicitly warns.
Three native enterprise protocols, with role-based access control and full audit.
OpenID Connect with Keycloak, Auth0, ADFS, Okta, and other compatible providers. Claims mapping.
Direct corporate directory connection. Customizable filters. LDAPS support.
SAML federation with attribute-to-role mapping. Multiple IdPs supported.
Customizable roles per organization. Permissions by module, object, and operation.
First-time authenticated users are provisioned with a configurable default role.
Login, model changes, DDL generation, exports — all recorded with timestamp and user.
MFA (multi-factor) is the corporate IdP's responsibility — 4dbAI delegates authentication to the configured provider.
Direct Kerberos (without federation via LDAP/SAML) is under analysis for the roadmap.
AI grounded in the modeling task — not a generic chat sitting next to it.
The AI "sees" the current model and responds based on it. Not a generic documentation search.
Compares naming, types, and structures against the organization's configured standard.
Identifies missing relationships, recommended normalization, useful indexes.
Commands like "add FK from ORDER to CUSTOMER" are executed as real actions on the canvas.
Connects with your corporate LLM or runs local models — you choose where the processing happens.
History of AI interactions per user. Compliance with internal policies.
AI quality depends on the chosen model. Smaller local LLMs have reduced capability vs. large corporate models.
The AI assists, doesn't decide. Suggestions are presented for the modeler to approve — never applied automatically without confirmation.
Technical documentation generated from the model itself — always up to date, in multiple formats.
Cover, table of contents, executive summary, data dictionary, diagrams, relationships, and business rules.
Documents generated in English or Portuguese from the same model. Translation of descriptions supported.
Export as PDF (distribution), HTML (web/internal portals), and PNG (presentations).
Cover, header, footer, and visual identity configurable per organization.
Update only modified sections. No need to regenerate everything on each change.
Each generated document is associated with a model version. Full traceability.
Full deployment in the client's infrastructure, with real data sovereignty.
Runs on your own servers or private cloud. No multi-tenant SaaS.
No mandatory usage data sent outside the environment — not anonymized, not aggregated.
Use your corporate LLM, host local models, or disable AI — your choice by policy.
Deploy in Docker / Kubernetes environments. Compatible with modern DevOps practices.
Models, metadata, and configurations in exportable format. Backup is the client's responsibility, using their own tools.
Versions made available for deploy when the client decides. No forced updates.
A practical application of DAMA-DMBOK® concepts: accountability guides metadata management, architecture, quality, and security. Explore how each discipline maps to 4dbAI features below.
About DAMA-DMBOK® at DAMA InternationalDiscover assets across sources and give each term an agreed definition.
Index metadata from relational databases, data lakes, and BI tools.
Find sources, tables, attributes, and descriptions in one catalog.
Corporate terms and approved definitions align business and technical vocabulary.
Link terms to data assets so meaning stays connected to usage.
Trace how data moves and assess the effect of a change before making it.
Visual, automated mapping from origins to applications, dashboards, and reports.
See data move through ingestion, transformation, and consumption.
Identify tables, pipelines, and reports affected by a change at the source.
Use identified dependencies to coordinate fixes with the teams involved.
Monitor quality with measurable criteria and handle anomalies in context.
Find missing values and duplicates that undermine processes and analyses.
Validate patterns, relationships, and compliance with organizational rules.
Track validation results and quality incidents in one place.
Notify technical and business owners to investigate and resolve issues.
Locate personal and sensitive data, follow its flows, and link every asset to owners, permissions, and records. Give controllers and data protection officers context to assess risk, prepare impact reports, and account for decisions when needed.
Law No. 15,352/2026 established Brazil's National Data Protection Agency as a special autonomous agency with the independence to carry out its duties. The LGPD requires accountability; the ANPD may request information and impact reports under applicable law. 4dbAI helps keep the data, flows, and controls that support those responses organized.
Identify and tag personal and sensitive data in the catalog. Use lineage to see where it flows and which assets depend on it.
Bring together data categories, flows, owners, and controls to help the controller assess risks and prepare a Data Protection Impact Assessment (RIPD).
Assign Data Owners and Data Stewards to domains, link permissions to assets, and retain audit trails for relevant actions.
Review classification, lineage, and action history in context for internal assessments and to help respond to ANPD oversight requests.
Live demo with your actual model. We respond within one business day.