See the Architecture. →
A containerized API server that executes business logic at commit.
It owns transaction commits and enforces business logic on every write — regardless of whether the request comes from an API, UI, MCP client, or AI agent.
Logic executes at commit time, guaranteeing that all outcomes are validated before data is written.
Clarifications:
Not an external rules layer
Business logic executes inside the runtime — not as callbacks, interceptors, or policy add-ons.
Not AI code generation
AI does not emit procedural backend code. Logic executes in a governed runtime; AI participates only where explicitly declared.
Not a workflow or event engine
Logic executes transactionally, with full correctness guarantees.
This is infrastructure: a runtime you deploy and operate as the commit control point.
See it - See the 8-minute creation demo →
Accept NL, per path
(Place Order, Pay Order, Change Item…)
Generate path-specific code
Same business rule restated N times
Correctness depends on covering every path
Design one path at a time.
Hope you didn’t miss one.
Accept NL for some use case
Generate Rules DSL: path-independent end-state invariants
(what must be true at commit)
Logic engine executes rules once, at commit
All paths governed — including agentic proposals
Why Procedural GenAI Fails at Business Logic
AI can generate procedural code, using pattern matching
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)
By translating intent into typed declarative rules, the engine can derive dependencies deterministically — including transitive dependencies that procedural control flow cannot reliably encode.
AI helps create the DSL.
The DSL is the corporate asset.
The engine guarantees correct behavior at commit time.
For more information, click through the slides at right.
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
Transactional systems have always required a single invariant control point: commit time governance through rules.
Regardless of how logic is produced — handwritten, generated, or AI-proposed — correctness can only be guaranteed when the final state is validated before data is written.
Logic executes on commit.
So, logic declared for Order Add governs Item Update (shown at right).
All paths obey the rules,
ensured by infrastructure.
Agentic systems amplify this reality.
You cannot govern thousands of execution paths.
You can govern the commit.
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 predictable, WebGenAI can generate UI, API, data, and logic — the whole stack — 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.
A governed agent runtime where deterministic business rules and optional probabilistic reasoning enable safe, agentic behavior over enterprise systems.
Natural-language intent is translated into declarative DSL and executed by a deterministic engine — allowing AI to act while remaining correct, explainable, and auditable.
Standard IDE. Standard deployment. Extensible with standard Python.
WebGenAI - Complete System From Browser Prompt
No Screen Painting, No Database Design
With Business Logic
Here is an example of agentic business logic — not prompts or scripts.
Use case: Check Credit:
1. The Customer's balance is less than the credit limit
2. The Customer's balance is the sum of the Order amount_total where date_shipped is null
3. The Order's amount_total is the sum of the Item amount
4. The Item amount is the quantity * unit_price
5. The Product count suppliers is the sum of the Product Suppliers
6. Use AI to Set Item field unit_price by finding the optimal Product Supplier based on cost, lead time, and world conditions
Use case: App Integration
1. Send the Order to Kafka topic 'order_shipping' if the date_shipped is not None.
GenAI-Logic produces a governed, executable backend:
• JSON:API for B2B and web apps
• MCP-discoverable endpoints
• Messaging for application integration
• Deterministic rules reused over all transaction sources
• Container-ready deployment
A logic appliance means infrastructure that runs your logic — not scripts, not frameworks — with deterministic guarantees.
Business Logic is Nearly Half the Effort
✅ Good UI Support
See video (2:28 in the video below)
Or use your favorite tools
Buggy: Key Cases Missed
FrankenCode: Maintenance Challenge
Architecture: often created on client (!)
Nearly Half the Effort Remains...
Rules are 40X More Concise Than Procedural Franken-Code
Unique to GenAI-Logic
Transparent to Business Users
Fully Extensible with Python
Governable, No Hallucinations
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.”*
