Raft's AI KOMP-AS Framework: How to Choose the Right Architecture for an AI Solution

Most enterprise AI initiatives fail before anyone writes the first line of code. The cause isn't technical limitations or a shortage of data. It's picking the wrong direction at the very start. Section "A" of AI KOMP-AS, a framework developed by the Raft team, targets exactly this systemic problem. It is a method for the architecture and product design of AI services that helps teams set the right direction and calculate the return on investment before development begins.

AI KOMP-AS is a methodological framework for evaluating and designing custom AI solutions in an enterprise setting. The acronym, which comes from Russian, spells out its structure: Architecture and product design, Component cost, Scalability, Performance, Data architecture, and Use-case scenarios. Each block is a separate method with specific questions and decision criteria.

The framework is aimed at teams that plan to build custom AI products from scratch or on top of open-source models, rather than adopt off-the-shelf SaaS tools. For these teams, an architectural mistake discovered late in the process can cost more than the entire original budget.

Section "A" covers the phase that comes before any technical development. It is a toolkit for answering four key questions that most teams either ignore or settle on instinct, and this is where future problems take root.

The first question is problem definition. Most AI initiatives start with a vague brief: "we want a smart chatbot," "we need to automate document processing." The framework offers a structured path from business goal to functional requirements to a specific AI task with measurable success criteria. This step often exposes a gap between what the business wants and what is technically feasible within a reasonable timeframe.

The second question is the choice of solution type. Not every task calls for custom development. KOMP-AS provides a matrix: when integrating a third-party provider's API is enough, when you need to fine-tune an existing model on corporate data, and when building an architecture from scratch is justified. This choice directly determines the budget, timeline, infrastructure requirements, and future maintenance costs.

The third question is estimating ROI before development starts. One of the core principles of KOMP-AS is a controlled return on investment. The authors show how to build a baseline economic model as early as the design stage: define key performance metrics, calculate break-even points, and model failure scenarios. This gives teams a tool to stop a project in time, before the resources have already been spent.

The fourth question is top-level architecture decisions. RAG versus fine-tuning, cloud versus on-premise, centralized versus distributed architecture: all of these are settled at the start. Architectural decisions made early set the constraints for all subsequent development. Reworking fundamental decisions at later stages costs many times more than getting them right at the beginning.

Industry analysts estimate that most enterprise AI pilots never reach production. The main reason isn't technical limitations but the lack of a structured approach to design. In companies where several teams run AI initiatives in parallel, the absence of a shared methodology leads to incompatible architectural decisions and duplicated effort. Frameworks like AI KOMP-AS fill exactly this gap by giving teams a common language and clear decision criteria.

The Raft team's series will continue with breakdowns of the remaining sections: component cost, scalability, performance, data architecture, and use-case scenarios. For practitioners currently evaluating AI initiatives or preparing to defend a budget before leadership, section "A" is a useful entry point: it asks the right questions before architectural decisions become irreversible.