How to double project throughput without expanding your team: the software house + GENESIS-AI collaboration model

Software houses have been living with the same paradox for years: in order to grow, you need to deliver more projects, and in order to deliver more projects, you need to hire more developers. Margins are eaten up by recruitment costs, turnover, and wage pressure, and management is constantly faced with the same dilemma: "take on another project or risk overloading the team?"

The GENESIS-AI cooperation model allows you to break this pattern. Instead of the classic "more people = more projects," a software house can build a hybrid: people + AI software factory, where the platform takes on repetitive, low-level work, and the team focuses on what actually generates margin – analytics, architecture, integrations, UX, and maintenance.

Problem: growth that stifles margins

A typical scenario seen in many service companies:

• The sales pipeline is full, but delivery capabilities are limited by the number of available developers.

• The customer expects shorter time-to-market and a predictable budget, and projects are becoming increasingly complex in terms of architecture (microservices, integrations, security, compliance).

• Even a well-organized team spends a significant amount of time on repetitive tasks: generating boilerplate, configuring layers, repetitive CRUDs, APIs, forms, lists, validations.

As a result, the company grows "across the board": more people, more projects, but the unit margin does not increase, and the operational risk does.

The role of GENESIS-AI: a full AI software factory in SDLC

GENESIS-AI is a platform that generates complete, multi-layered, production-ready web applications based on precisely described business requirements – from the data model, through the backend and frontend, to quality tests, security tests, and deployment packages (e.g., Docker images ready to run in Kubernetes). From a software house perspective, it is not about replacing the team, but about integrating GENESIS-AI into the existing SDLC as a factory that automates the entire process of producing applications in accordance with specifications, while people focus on what really generates margin: analysis, architecture, integrations, UX, and further development.

GENESIS-AI:

• accepts a structured requirements specification (GENESIS-DOCU standard),

• designs architecture and components,

• generates complete application code and automated tests,

• prepares containerized packages ready for deployment on the customer's target infrastructure.

GENESIS-DOCU – fuel for the factory

The key to full automation is the GENESIS-DOCU standard – a unified way of describing requirements that GENESIS-AI interprets without ambiguity. GENESIS-DOCU combines business process descriptions, diagrams (e.g., BPMN, UML), role and permission definitions, data models, and mockups/prototypes of key screens into a single coherent set of artifacts, which is a formal "contract" between the analysis and architecture team and the software factory.

Thanks to GENESIS-DOCU:

• analysts and architects work with tools and notations they are familiar with,

• the platform receives input in a precise, machine-processable format,

• any change in requirements can be quickly reflected in DOCU and the complete application can be regenerated, instead of manually rewriting large blocks of code.

Which SDLC elements to delegate to the platform

1. Requirements analysis → GENESIS-BIZSTORY → GENESIS-DOCU → input to GENESIS-AI

The partner team continues to conduct workshops with the customer, map processes, and design solutions at the business level—but instead of locking it down in classic "Word + Excel," it uses the GENESIS-BIZSTORY mechanism.

BIZSTORY conducts conversations in natural language (moderated by agents specializing in UI, integrations, data models, and testing), asks probing questions, ensures consistency, and validates the quality of requirements on an ongoing basis.

The result of this work is automatically generated documentation in the GENESIS-DOCU standard – a structured description of requirements that combines: business process descriptions, diagrams (BPMN, UML), role and permission definitions, data models, and mockups of key screens.

Such a DOCU package becomes the input to the GENESIS-AI factory: it is precise enough for the platform to autonomously design the architecture and generate a complete application , and at the same time clear enough for business, analysts, and architects to work on it normally and, if necessary, iteratively refine it.

In this way:

• there is no longer any manual "preparation in DOCU,"

• BIZSTORY is naturally integrated as the first step,

• DOCU is shown as an automatic but editable output that feeds the application factory.

2. Architecture and pattern design

The architect on the software house side selects the target architecture style, technologies, integrations, and non-functional constraints. GENESIS-AI treats this as a "bus" – based on these assumptions, it designs and generates:

• the structure of modules and services,

• API contracts,

• communication layers,

• integrations with databases and external systems.

The partner continues to sell architectural expertise – only the speed of transition from the model to a working application compliant with the adopted architecture and ready for further development increases.

3. Application code generation

The greatest leverage effect occurs when generating:

• screens and forms (including validation and UI status),

• CRUDs and business operations,

• process flows (workflow),

• reports, lists, filters, notifications.

Based on this, GENESIS-AI creates a consistent, complete web application compatible with GENESIS-DOCU – with a data layer, backend, frontend, security mechanisms, and tests – and the development team takes it over for further customization and integration, instead of writing everything from scratch.

4. Quality and security testing

The platform automatically:

• generates some of the tests (e.g., unit tests, E2E scenarios for critical paths),

• performs static code analysis,

• checks compliance with selected security guidelines.

The QA team does not disappear – it shifts towards exploratory testing, non-functional testing, edge scenarios, and business testing.

How does a software house continue to make money?

In the hybrid model, the software house's revenue is no longer based on simple developer billing, but rather on the value delivered around the GENESIS-AI factory and repeatable product packages.

The key revenue streams are:

• Business and product analysis – workshops, discovery, scope clarification, function prioritization; this is where decisions are made about what will actually go into GENESIS-DOCU and what solution variants are worth generating.

• Architecture and technical design – selection of architecture style, patterns, integration, cloud, security; the software house sells its expertise in designing solutions, which GENESIS-AI then quickly materializes in the form of a working application.

• Integrations and edge cases – connecting existing systems, non-standard components, domain-specific logic that does not make sense to generate; this is an area where deep technical and domain knowledge is still needed.

• UX/UI and product design – designing user experience, micro-interactions, and product advantages that distinguish the solution from a "typical" template-generated application.

• Maintenance, development, and ops – DevOps, SRE, monitoring, development roadmap, change requests; thanks to GENESIS-AI, further functionality additions can be planned as iterations at the GENESIS-DOCU level, rather than just manually adding code.

• Productized services and IP – as similar projects are implemented, the software house can build its own DOCU libraries (process patterns, data models, UI) and sell them as semi-finished products or accelerated implementation packages for selected industries.

The result is that the share of revenue from high-margin expert services, productized services, and maintenance is growing, while the share of low-margin "code writing" is decreasing. The same number of programmers handles a larger number of projects because GENESIS-AI does the heavy, repetitive work, and people focus on what the customer is really willing to pay more for.

Cooperation model: step by step

The following steps describe how to move from customer discussions, through GENESIS-BIZSTORY and GENESIS-DOCU, to a complete application generated in GENESIS-AI.

Step 1: identification of "factory" project types

GENESIS-AI has the greatest business impact in projects dominated by business processes and data operations – there, the platform can generate virtually the entire application, and the team focuses on integrations, UX, and elements that distinguish the product from the competition.

Step 2: Standardization of input artifacts

The software house adopts GENESIS-DOCU as a common standard for describing requirements for projects implemented using the GENESIS-AI factory. This means consistent rules for how we describe business processes, how we model data, how we define roles and permissions, and how we prepare screen prototypes – all in a single, unified format that serves as a contract between the team and the platform.

Such standardization:

• eliminates documentation chaos between projects,

• speeds up the start of each subsequent implementation, because analysts work according to a repeatable template,

• enables GENESIS-AI to generate complete, consistent applications without the need to manually "translate" requirements into technical tasks.

Step 3: Integrating the pipeline with GENESIS-AI

In practice, this means:

• implementing a workflow in which, after the analysis/architecture stage, the project goes to the "factory" as a set of artifacts,

• integration with the code repository and CI/CD process,

• determining which modules are generated and which are always written manually.

The team receives the generated application directly to the repository – with full code, tests, and configuration, ready for further customization, integration, and development.

Step 4: Tuning and development

Developers focus on:

• adding custom logic,

• optimizing key fragments,

• integrations that require domain knowledge,

• refining UX and edge cases.

In subsequent iterations, some changes can be reverted back to models and regenerated—instead of rewriting larger blocks of code.

Business effect: how to "double throughput"

A software house that has implemented such a model can:

• Maintain the current size of the team, yet increase the number of parallel projects (some of the work is taken over by the factory).

• Shorten project start-up time: it takes weeks, not months, to go from requirements to a working first version of the MVP.

• Improve cost predictability – it is easier to estimate the scope of manual work when a large part of the application is created according to a repeatable pattern.

• Improve the quality and consistency of solutions, because the architecture and patterns are generated according to a uniform standard.

By combining the standardized GENESIS-DOCU specification with the full automation of application generation by GENESIS-AI, the same number of people can handle a larger number of projects with a similar profile – with shorter time-to-market and higher quality repeatability. This is not a promise of "magical" overnight growth, but a predictable model: each project starts from the same structured input (DOCU) and ends with a generated, production-ready application, and the team focuses on what the customer is willing to pay the most for – domain expertise, integrations, UX, and further development of the solution.

This means that a software house can realistically double its project throughput in selected types of orders – without linearly adding more FTEs.

Who is this model for?

• For software houses that have a steady influx of similar projects (e.g., in a specific industry or domain) and want to build their own "productized services" model.

• For companies that feel their teams are too expensive to waste their time generating boilerplate, yet do not want to relinquish control over architecture and quality.

For organizations that want to build a competitive advantage: instead of competing only on price and number of FTEs, they offer shorter time-to-market while maintaining enterprise quality.