Today, AI is advancing at a breakneck pace—new models emerge faster than the previous ones become obsolete. Along with its evolution, the community’s perception of AI has shifted—from skepticism ("It’s just a fancy autocomplete!") to recognizing it as an integral part of not just the future, but the present. Fully embracing the idea that "Those who master AI will shape the future," we actively explore existing tools through the lens of three principles:

• Curiosity — We want to figure out for ourselves where the real value lies.
• Pragmatism — If a tool saves 20% of time on routine tasks, why not give it a try?
• Responsibility — Before recommending solutions to clients, we test them ourselves.

To systematically integrate AI into our workflows, we created Noveo Innovation Lab—a platform where specialists from different departments test AI at every stage of work, from analytics and development to testing and design.

After evaluating the efficiency of various tools—from niche to all-purpose—we’re ready to introduce to you our top picks!

🔍 All-Purpose Tools (Especially Effective for Analytics and Text Processing)

- ChatGPT (v4 / 4o)

Average rating: 4 / 5

Strengths:
✔ Generates high-quality documentation of various types.
✔ Excellent at interpreting technical documentation, bug reports, and code structure.
✔ Reliable in explaining logic, rephrasing, and editing text.

Weaknesses:
❌ Without a clear prompt, it may "hallucinate" logic.
❌ Sometimes oversimplifies critical details (e.g., indexing or security conditions).

Recommendations:
✅ An omni-purpose tool—suits almost any task.
⚠️ Requires well-structured prompts—the more precise the request, the better the result.

- Claude.ai

Average rating: 3.7 / 5

Strengths:
✔ Great at requirement analysis and logic explanation.
✔ Effective in dialogue—asks clarifying questions and suggests follow-ups.
✔ Particularly useful for technical interviews and test generation.

Weaknesses:
❌ Code generation is weaker than its text capabilities.
❌ Doesn’t always grasp the full context.

Recommendations:
✅ Best for requirement analysis, architectural decisions, and text-based tasks.
⚠️ Not ideal for generating complex code or diagrams.

(We’ve separately analyzed tools for automating meeting minutes—check out the detailed breakdown).

💻 Development & Testing

- GitHub Copilot

Average rating: 4.0 / 5

Strengths:
✔ Effective in generating unit tests, especially for Java/Maven projects.
✔ Works well within the IDE, considering file context.
✔ Helps with feature coverage and template generation.

Weaknesses:
❌ Sometimes oversimplifies complex logic or duplicates code.
❌ Without guidance, produces generic solutions.
❌ Requires refinement in automated tests (especially assertion logic).

Recommendations:
✅ A great assistant for routine IDE tasks.
⚠️ Needs clear instructions for non-standard projects.

- Cursor IDE + ChatGPT 4o

Average rating: 3.7 / 5

Strengths:
✔ Useful for code review, refactoring, and unit test generation.
✔ Helps analyze Pull Requests and extract logic into hooks.

Weaknesses:
❌ Can get "lost" in non-standard projects.
❌ Suggests template solutions without clear constraints.

Recommendations:
✅ Best for reviews, refactoring, and automated tests.
⚠️ Avoid for complex architecture or lack of templates.

- JetBrains AI Assistant

Average rating: 3.4 / 5

Strengths:
✔ Good for generating automated tests, migrations, and basic refactoring.
✔ Suitable for UI components and visual tasks.

Weaknesses:
❌ Doesn’t always take into account architectural constraints.
❌ Unreliable in generating complex business logic.

Recommendations:
✅ Useful for environment setup and basic tasks.
⚠️ Limited in high-level architecture.

🎨 Design & Content Generation

We tested Flux, Ideogram, Midjourney, and Google Imagen 3 for tasks like:
✔ Quick generation of images and icons (with manual selection afterward).
✔ Visual audits (color palette, composition).
✔ Idea generation for wireframes and mood boards.

Google Imagen 3 delivered the best results—its images were the most photorealistic in color accuracy, detail, and lighting.

Time saved:
🔹 Stock search: 5–30 minutes.
🔹 Generation + upscaling: 3–10 minutes.

Conclusion

When working with AI, remember:

• Input quality = output quality (the more precise the prompt, the better the result).
• AI is an assistant, not a replacement (complex architectural solutions require expert input).
• A hybrid approach (AI + manual review) yields the best results.

We’re continuing to experiment and share our findings—there are plenty more exciting discoveries ahead! Which AI tools have proven most effective in your experience?