Why Backend Systems Still Don’t Have Schemas?
And How We’re Changing That
When we try to understand the architecture of a software system, we start from the most information-dense sources. We look for schemas that reveal structure at a glance. We love high-level architecture diagrams, API specifications that explain contracts, and database schemas that show entities and relationships. But when it comes to backend codebases, something is missing: there is no such thing as a backend schema.
The Missing Layer Between API and Database
You can see the API endpoints. You can, if you are lucky, see the database schema (however - for document databases, you often need a running instance to get that). But between the two lies a large unknown space. That space is where domain logic, modules, and process orchestration live. It is also where most system complexity hides. And yet, there is no consistent, automatically generated schema for it.
Why Existing Tools Fall Short
A few approaches have tried to fill this gap:
Spring Modulith shows how your application is divided into modules but does not let you browse dependencies.
C4 model diagrams define components inside containers, but those components can mean anything: a class, a package, or a module. The structure becomes flat and unclear.
AI-based reverse engineering tools can sometimes extract fragments of structure, but using them feels like generating a database schema from scratch each time. It is unpredictable and inconsistent.
None of these solutions provide a repeatable, code-based schema for what actually happens between the API and the database.
Our Approach: A Real Schema for the Backend
At Noesis we decided to change that. We built a solution that automatically maps backend systems into a consistent, structured graph based on the P3 model, a generic ontology for describing software architecture.
The P3 model represents systems through three perspectives:
Domain – modules, objects, and behaviors that form the business logic
Technology – deployable units, APIs, and runtime containers
People – development teams and business units responsible for each part
With these elements, any system can be represented clearly and systematically, much like a database schema but for code.
How It Works
Noesis scans your codebase and creates a P3 model using static analysis. It comes with default conventions that already match most real-world architectural styles. You only need to tweak them to reflect your specific naming or module structure.
The result is a deterministic model:
Key architectural elements such as application entry points, modules, and building blocks are recognized and visualized.
Less important code parts are still analyzed, but used only to detect dependencies, not shown on the diagram.
Everything happens locally, safely, and without AI or internet access.
No hallucinations. No random outputs. No data leaving your infrastructure.
Why Architectural Patterns Matter
Architectural patterns are the compass that keeps the mapping meaningful.
By recognizing well-known structures such as entities, services, repositories, aggregate roots, or command handlers the model can show just the right level of detail.
Even more importantly, the language of patterns allows us to filter the schema to see only the parts that are relevant at a given moment. You can focus on process orchestration when designing changes, switch to module dependencies during refactoring, or examine persistence boundaries while debugging data issues.
It works a bit like tomography or MR imaging — the same system can be observed at different depth levels, revealing only what is necessary without losing the overall structure.
The result is a diagram that is dense with knowledge but never overwhelming. It gives your backend the same kind of clarity that an Entity Relationship diagram gives to your database.
The Bigger Vision
Visualization is only the beginning. We are building a platform that will allow teams to:
Generate element descriptions with AI (already available)
Export described elements organized according to the P3 model to documentation tools like Confluence (already available)
Provide an MCP server interface, allowing AI agents to access reliable, structured backend knowledge so they can design, document, and generate code consistent with the real system architecture
Design new features directly on top of the P3 schema
Generate automated architecture tests to verify that implementation matches design
Compare codebase versions and highlight architectural changes
Assess the impact of backend modifications across integrations e.g. showing which endpoints should be tested after a change
Produce change reports for architects and managers
All powered by the same predictable, code-based model.
Join as an Early Adopter
If this sounds relevant to your daily work, we are inviting early adopters to join our closed beta. If you are an analyst, architect, or engineer working in the .NET / C# ecosystem, you can already scan your system with Noesis and see its structure mapped into a P3 schema.
You do not need to be a deeply technical person. We will help you run the first scans, and as an early tester you will get full access free for at least three months, with a tailored offer afterward.
👉 Request early access (link to your signup page)
AI makes code generation faster than ever. But as code multiplies, understanding it becomes the real bottleneck. Let’s make backend systems as easy to explore as a database schema and finally see how they really work.