We are currently in the advanced R&D phase with a planned early access program for strategic partners in Q2-Q3 2026. Commercial launch is targeted for 2027. Early partners will have priority access ahead of general availability.

GENESIS-AI generates applications using industry-standard technologies including Python, C#, SQL databases, and modern frontend frameworks (React, Angular, Blazor). Generated code follows best practices and can be maintained by standard development teams.

Yes. We are selectively engaging strategic partners who can contribute to platform evolution through pilot programs, feedback, and collaborative development. This includes technology partners, early adopters, and investment partners.

Strategic partnerships can take various forms including technology collaboration, co-development agreements, pilot programs, and investment partnerships. We tailor engagement models to align with mutual strategic objectives and create value for all parties.

All code generated by GENESIS-AI remains the complete property of the customer. There are no proprietary dependencies or licensing restrictions. You have full rights to modify, extend, audit, and deploy the code as you see fit.

GENESIS-DOCU is our structured methodology for specifying application requirements in a way that optimizes AI interpretation. It combines natural language descriptions, diagrams, process models, and prototypes to minimize ambiguity and ensure comprehensive coverage.

Contact partnerships@genesis-ai.app with details about your organization and areas of interest. We'll schedule a conversation to explore potential collaboration models.

Requirements specifications are necessary to generate applications. GENESIS-AI uses documentation created in accordance with the GENESIS-DOCU methodology. This methodology is based on documents containing structured descriptions in natural language, diagrams, screen examples, etc. For more advanced users, the Platform also provides the GENESIS-DSL language, which allows such specifications to be converted into "AI-ready" specifications, significantly increasing control over the final result, especially in complex systems.

Yes. GENESIS-AI provides complete, readable source code generated in popular technologies (Python/C#/SQL + frontend framework). The code is owned by the customer and can be further developed, refactored, and maintained by any development team – without vendor lock-in.

The platform operates in a controlled technology stack: the backend is developed in Python or C#, a relational SQL database, a modern web frontend (e.g., React or Angular), and standard containerization (Docker) with orchestration in Kubernetes. This narrowing of the environment ensures predictability, high quality, and easy maintenance of the generated enterprise-class applications.

The default delivery model for GENESIS-AI is a fully managed SaaS service in the cloud, which allows for the effective use of the high-performance GPU environments necessary for the platform to operate. However, for organizations requiring full control over the environment, an Enterprise variant is available with the option of on-premises deployment – in a private cloud or customer infrastructure. Such deployments are carried out as individual projects, following a detailed analysis of technical and architectural requirements and the maintenance model.

GENESIS-AI is based on the MLDevSecOps approach—an integrated combination of DevOps, MLOps, and DevSecOps—so that security is built into the entire application and model lifecycle, rather than being "stuck on" at the end. Source data, feature stores, training pipelines, models, and application code are all subject to the same regime: access control, versioning, auditing, and security policies. Each generated application and ML artifact undergoes automated testing, static analysis, vulnerability scanning (including containers), and validation against security best practices (e.g., OWASP classes). Access to data and models is restricted by roles, all operations are logged, and CI/CD and runtime environments are strictly separated and monitored. This allows GENESIS-AI to securely process business data while minimizing the risk of vulnerabilities in the code and ML layer. In practice, this means that security is taken into account at every stage – from working with data, through code generation, to application deployment and maintenance.

We are building a support ecosystem around GENESIS-AI to help users achieve good results faster, rather than just "putting out fires." It is based on two pillars: (1) GENESIS-AI Guide – an intelligent voice and text assistant, powered by knowledge about the platform, the GENESIS-DOCU methodology, and the GENESIS-DSL metalanguage, which users can simply talk to (by voice or text) to ask about specific problems, best practices, or how to develop requirements specifications – the system guides them step by step; (2) GENESIS-AI Club – a development and partnership program for customers who want more than just "support in solving current problems" – it is a space for joint refinement of concepts, ways of working with requirements, best practices for using the platform, and additional project support.

The Early Access version will be deliberately limited – we anticipate a limited number of participants, a limited number of simultaneously generated projects, and no full SLA guarantees (the priority is technology development and feedback collection). Detailed limits – e.g., the number of application generations per month or available technologies – will be described in the EA program rules and may change between subsequent phases of platform implementation.

es. GENESIS-AI generates standard source code and container images (e.g., Docker), ready for deployment in any environment compatible with these technologies – in another public or private cloud or in the customer's server room. No vendor lock-in means that the customer retains full ownership of the code and can freely migrate the solution.

Yes, one of the goals of GENESIS-AI is to generate basic documentation artifacts for each application, such as: database schema, set of available operations in the API interface, architecture information, and deployment artifacts (containers, scripts). Future development plans (especially for Enterprise customers) include the implementation of extensive technical and user documentation, similar to the complete documentation created for the platform itself.

No programming skills are required. The user must be able to clearly articulate what they need and describe it in natural language. To generate an application, it is necessary to provide specifications of the problem to be solved by the generated application, who will use it, and what features are needed. The more precisely the user describes their expectations, the better and more accurate the result will be – as in other generative systems. For teams that want to maximize the platform's potential, the GENESIS-DSL metalanguage is available, the use of which requires proficiency in modern programming environments.