Because all changes flow through this funnel, rules decide what persists—making governance inevitable by architecture, not discipline
GenAI-Logic is a commit-time logic appliance that sits between intent and persistent state and runs your logic — not scripts, not frameworks — with deterministic guarantees.
All writes pass through it. None bypass it - apps, services, messages.
Clarifications:
Complements existing infrastructure
(workflows, databases, Cloud, AI) — GenAI-Logic governs what may commit, not how systems are orchestrated or data is stored.
Not an external rules or policy layer or agent
business logic executes inside the commit runtime, not as callbacks, interceptors, or agent-side enforcement
This is infrastructure: a runtime you deploy and operate as the commit control point.
Logic as Infrastructure: Deploying the Business Logic Appliance makes
path-independent governance an unavoidable architectural consequence
See it - See the 8-minute creation demo →
Distilled Rules, Enforced on Commit
(Path-independent Invariants, Correct By Construction)
AI distills intent into reusable rules.
Because rules are declarative and data-bound, they apply to all sources and paths.
Dependencies and ordering are computed by the runtime — not guessed by pattern-based AI.
Intent is preserved: humans read and debug rules, not generated code.
Governance is inspectable: rules generate executable tests, with requirements traceable to rules in a Business Rules Report.
Consequences:
New paths inherit behavior automatically, without added complexity.
Design Once. Govern All Paths.
Correct By Construction.
Logic Executes on Commit -
All transaction sources converge on one deterministic control point, where Reuse and Ordering are unavoidable architectural consequences.
All paths obey the rules - enforced by infrastructure.
You can't govern paths.
You can govern the commit.
Why Procedural GenAI Fails at Business Logic
AI can generate procedural code, using pattern matching. Great for pages, etc.
But pattern matching breaks down on long dependency chains -- the core of business logic.
In our own A/B test:
Five business rules became ~200 lines of generated code
Two correctness bugs emerged
After the second bug, the AI explained why it failed:
“The issue arises from transitive dependencies that cannot be reliably inferred from procedural control flow.”
— AI self-analysis (read it here)
Create Backend directly from existing database
Run created system:
• JSON:API, MCP Discovery
• Admin UI
• ORM Configuration
Declare business logic in NL or Python
Enforced on commit
Governs all Paths, all Transaction sources
Test, Extend, Iterate -
More logic
Python logic events, custom endpoints
Vibe Custom Apps, etc
genai-logic create --project_name=basic_demo --db_url=sqlite:///samples/dbs/basic_demo.sqlite
cd basic_demo
sh run.sh
On Alice's first order, update the widget quantity to 100
GenAI-Logic does not introduce a proprietary language, IDE, or runtime.
It generates a standard Python project that runs in your existing development and deployment environment.
Edit logic in your IDE
Debug with standard tools
Deploy as a container, anywhere
Integrate using familiar APIs and messaging
Governance is built in — without changing how your team works.
Natural Language Simplicity
LogicGen to eliminate Franken-response
Declarative Engines for Execution
✅ 1. Automatic Backend - why bother with data mockups?
Create MCP API from existing database
Create database from prompt
✅ 2. Automatic App - why start from scratch?
Kickstart with a running, multi-page application
Customize with your favorite Vibe tools
✅ 3. Declarative Business Logic 🏆
Vibe for UI and logic -- half the effort
See how rules are More Concise than Code
Properly Factored out of UI
✅ 4. Standard IDE, Language, Deployment
Full Extensibility With Python
Human in the Loop Governance
✅ Free and Open Source - Simple Install, Tutorial with Vibe
With declarative business logic, creating a system can finally match the way we think about it.
Full-Stack Vibe becomes real. With backend logic governed and deterministic, WebGenAI can generate UI, API, data, and logic from natural language.
Business users prototype in WebGenAI.
IT receives a standard, extensible backend — not a low-code black box.
AI scaffolds full React applications that developers can refine using familiar tools, powered by a deterministic rules engine.
Declarative approaches off-load complexity from developers to the system,
offering significant advantage over procedural, whether hand-coded or AI-generated.
40X less code
Rules Apply to new Use Cases - automatically
No Hallucinations
Deterministic Dependencies
GenAI Logic allows us to further accelerate the development of innovative software solutions that respond to the growing need for digitalization and process automation. Ontimize Web, our low-code web application framework, allows developers to declaratively implement powerful user interfaces for complex business applications. By integrating Ontimize Web with GenAI Logic, we automatically obtain a robust rules-based backend that fully supports the needs of our frontend and a fully documented API.
Furthermore, the generative capabilities of GenAI Logic allow us to automatically generate 90% of the application from a prompt. That’s really impressive! Most importantly, it means that our developers no longer have to manually declare hundreds of user screens, with their corresponding CRUDs, business logic and API endpoints. They can now focus on understanding the business requirements and designing a user experience that customers will love.
Rowbot is a new breed of data management platform that enables a true Data Mesh architecture. It allows non-technical business users to integrate data from multiple disparate databases and create a unified view of activity across the organization.
We output a unified dataset and pass that to GenAI Logic. In 2 minutes we have a fully functional application, allowing an analyst to see customer activity across all participating systems.
Then we can incrementally introduce business rules. These rules can range from alerts based on the data to propensity flags for marketing. Users cannot believe how quickly a unified view of the data can be presented and then enriched by the GenAI Logic rules engine.
I can’t say enough about ApiLogicServer. I’ve been using ALS for a number of years now having successfully delivered several solutions that started as an ALS app. ALS provides us an advantage when not only starting new projects, but also when gathering requirements. Including ALS as a tool within the SDLC has been instrumental in several migration projects as well. This is our bread and butter, and we use ALS more than any other framework in our projects.
I am excited to see the evolution of ALS into GenAI Logic using AI as the nexus for new project implementations. Thank you Val, Tyler and Thomas for a promising and powerful solution. ALS's iterative capabilities are especially helpful; driving the requirements process live with business stake holders. Getting the requirements right is very important in todays competitive market by helping us keep the costs down and ensuring customers are delighted with the outcome.
*“E-Cometa is a platform designed for the front end, enabling you to generate and maintain your forms on the fly, as well as define and manage processes dynamically. For back-end operations, GenAI allows you to define databases using AI and seamlessly integrate them with ApiLogicServer (ALS). ALS then provides the capability to add simple rules, enabling the creation of complex systems that integrate with your data.
By combining both, you will get a very flexible and powerful solution that allows you to create any web and mobile app easily, making it simple to develop and maintain—even if you are not a technical expert.
We also want to thank ALS for making it possible to migrate from the obsolete Live API Creator (a back-end rules engine with RESTful services), ensuring we could continue innovating with a modern and reliable solution.”*
