Why dbsof
Logic in the data layer
We're recreating how data and logic coexist. The backend-database separation becomes redundant. The infrastructure makes simple runtimes.
Automatic ontology
Schema introspection identifies patterns and builds ontology from migrated data. The system understands relationships without being told.
Learned migrations
No more scripted, one-off migrations. The system learns from prior migrations and handles schema drift automatically.
What does building this actually require?
The problem
Companies usually run 20–50 separate systems with different data structures. They need manual work to connect them, and this makes AI hard to use effectively.
What dbsof is
dbsof is one system that includes the database, runtime, migration engine, and logic layer. Its main goal is to normalise data automatically and make integration smarter.
The architecture
A database engine builds migrations and an ontology by understanding the schema. It also has a runtime, pattern detection, code generation from logic graphs, and a way to self-host.
The core innovation
The system automatically creates data flows (DAG/CFG), Schema → ontology → logic → runtime. Ontologies are built from the data itself instead of being manually designed.
What it enables
Automatic migration and code generation directly into production-ready AI applications. The marginal cost of software creation is trending to zero.
What building it requires
It needs knowledge of distributed systems, database internals, ETL methods, schema analysis, category theory, and runtime design. It also requires experience with large-scale Linux and backend infrastructure.
The problem
Companies usually run 20–50 separate systems with different data structures. They need manual work to connect them, and this makes AI hard to use effectively.
What dbsof is
dbsof is one system that includes the database, runtime, migration engine, and logic layer. Its main goal is to normalise data automatically and make integration smarter.
The architecture
A database engine builds migrations and an ontology by understanding the schema. It also has a runtime, pattern detection, code generation from logic graphs, and a way to self-host.
The core innovation
The system automatically creates data flows (DAG/CFG), Schema → ontology → logic → runtime. Ontologies are built from the data itself instead of being manually designed.
What it enables
Automatic migration and code generation directly into production-ready AI applications. The marginal cost of software creation is trending to zero.
What building it requires
It needs knowledge of distributed systems, database internals, ETL methods, schema analysis, category theory, and runtime design. It also requires experience with large-scale Linux and backend infrastructure.