Smart AI use for IT architects: delegate without losing control

What can be delegated to AI

AI can already support a sizable portion of architectural and design work — without removing the architect from the process. Three areas stand out:

• Understanding and organizing requirements

• Designing system architecture

• Evaluating technologies and developing PoCs

Let’s break down how each of these works, and which tools offer practical value.

Making sense of project requirements

A key part of an architect’s role is navigating through business, user, technical, and sometimes legal requirements. These often arrive in fragments: emails, notes, PDF documents, and decks. The task is not just to extract details, but to interpret intent, resolve conflicts, and identify what’s missing.

At this stage, large language models, especially when paired with retrieval-augmented generation (RAG), prove particularly effective. RAG enables the model to base its output on actual project documentation instead of relying on general knowledge. First, relevant fragments are retrieved from the data set, then used to generate accurate, context-aware results.

With the help of OCR, LLMs can process scanned documents as well. Natural language processing (NLP) techniques enable them to extract key requirements, categorize them (by function, performance, security, etc.), and sort them by priority.

Models like DeepSeek and Gemma can also detect duplicates, highlight inconsistencies, and help make sense of scattered or conflicting information. The result is a clear and defensible map of project requirements — one that the architect can use to build on.

This speeds up early-stage analysis and improves clarity, especially when multiple stakeholders are involved and the input is fragmented or high-volume.

Getting to a working architecture faster

Once the requirements are clear, the next step is system design: defining the structure, components, and interfaces. This includes mapping dependencies, identifying integration points, and thinking through scalability and resilience.

AI can support this phase in several ways. Modern LLMs can:

• Generate C4 diagrams and data models from descriptions

• Suggest system components and how they interact

• Identify potential performance or security concerns

In practice, the architect outlines the system goals and structure, and a model like DeepSeek translates that into a draft diagram in formats like Mermaid, PlantUML, or C4. That diagram becomes a starting point, which the architect then adjusts to fit the project context.

This isn’t a substitute for architectural thinking, it’s a tool that helps you get to a first version quickly. Instead of spending hours creating diagrams from scratch, you begin with a baseline that’s already aligned with your goals.

Choosing technologies and building PoCs

Selecting the tech stack (databases, frameworks, integrations) is a time-consuming process that requires both technical knowledge and strategic context. Performance, scalability, compatibility, licensing — all have to be considered.

Here, AI can act as a research and comparison engine. DeepSeek and Claude can analyze different technologies based on criteria you define, explain trade-offs, and structure that analysis in a way that’s easy to review: tables, feature comparisons, short summaries.

Once the stack is selected, the next step is to validate the core hypotheses. In combination with Cursor.ai, DeepSeek and Claude can help build a working prototype or Proof of Concept. The model can generate a basic project structure, outline component responsibilities, and produce starter code. This is especially helpful when time is tight and early feedback is needed.

LLMs can also assist in documenting the system and formulating tasks for development teams. Based on the system architecture and business goals, they can generate a preliminary set of tasks for backend, frontend, and DevOps teams — with implementation notes or suggestions. It’s not a full replacement for a tech lead, but it does accelerate the planning-to-execution handoff.

Importantly, every AI-generated result still needs expert validation. The model proposes, and the architect decides. That balance is what makes the process efficient without compromising quality.

Where AI has limits (and why it matters)

Even with all their strengths, AI is not a magic wound. Understanding where and how they fall short helps avoid costly mistakes.

Privacy and security constraints

When a project involves sensitive data or restricted information, using public models becomes risky. You won’t be able to provide the necessary details in your prompts without exposing something confidential.

That’s where on-premise solutions become essential. Platforms like ValueXIlet you deploy models inside your organization’s infrastructure. Requests are handled within your own data center — giving you full control and helping meet legal and security requirements. It’s more effort upfront, but essential for high-stakes environments.

Inaccuracies and hallucinations

Like any generative model, LLMs can sometimes get it wrong. DeepSeek, for example, has occasionally returned garbled text or non-relevant output when the prompt is ambiguous.

The solution is straightforward: keep a human in the loop. One reliable approach is to generate a draft using a primary model, then verify the results using a second, more conservative model focused on checking logic and consistency. With the right guardrails, the risks can be kept low — even in high-complexity use cases.

In real-world projects

In our experience, combining DeepSeek and Cursor.ai has proven effective across projects of varying size and complexity. Two examples:

• Project management platform for outdoor advertising campaigns

  AI helped automate system design, select technologies, and produce a prototype — saving time during early-stage coordination and clarifying the technical foundation before development began.

• Partner program for an online learning platform

  The solution included a Telegram bot and a mini-app for partner onboarding and referral tracking. DeepSeek generated the product requirements document (PRD), analyzed business needs, created component diagrams and data models, and drafted backend and frontend tasks. Cursor.ai was then used to further develop and refine these outputs.

In both cases, AI reduced time spent on routine work and allowed the team to focus on architecture-level decisions.

Looking ahead: AI as part of the architecture team

Today, AI plays the role of an assistant. But we’re moving toward a future where it becomes a fully integrated team member with clearly defined responsibilities, transparent scope, and manageable risks.

Architecture tools are already becoming smarter by design. They’ll not only visualize systems — they’ll understand intent, offer proven patterns based on similar implementations, and flag design bottlenecks early on. As AI becomes more embedded in day-to-day workflows, we’ll see models track requirement changes, keep documentation up to date, and help align architecture with implementation.

As a result, the barrier to entering implementation drops, while the quality of the technical foundation rises — thanks to stronger scalability, security, and performance.

And still, the architect remains at the center. They evaluate trade-offs and make the final calls. But AI will handle more of the preparation, giving professionals space to focus on what requires experience, judgment, and creativity.

This shift isn’t temporary, it’s a natural step in the evolution of software architecture.