How We Use AI at Dalbe in Q1 2026 – Real Projects, Not Presentations

How We Use AI at Dalbe in Real Life – Not in Presentations

Everyone is talking about AI. Few show you exactly what they do with it.

At Dalbe, we've been using AI seriously for almost two years. We didn't discover ChatGPT six months ago and we don't write about it just to seem trendy. We use it daily, in real projects, for real money, with real clients. And in our everyday lives, not just at the office.

We've also been hosting AI workshops for local businesses for two years, which means we've already seen all the mistakes people make when they start using it. We made them too in the beginning.

This article isn't a tutorial. It's an honest look at what our AI usage looks like right now, in Q1 2026, after two+ years of constant testing, failing, and refining.

What tools we use day to day

At Dalbe, we aren't loyal to a single tool. We use Claude, ChatGPT, and Gemini, all on pro accounts. We also have access to the coding components of each platform: GitHub Copilot, Claude for coding via VSCode, Gemini in AI Studio (just today we were watching a tutorial on Antigravity). For images, we use dedicated generators depending on the project, our favorite being Nano Banana, version 2.0 with Gemini 3.1 PRO.

Each tool has its strengths. Claude is better for reasoning and complex code. ChatGPT is fast for drafts and brainstorming. Gemini has advantages when working in the Google ecosystem, but lately, it has become a Dalbe team favorite for general tasks.

A year ago, we used much fewer tools and in a much simpler way. In Q1 2026, the difference is clear: we no longer talk about "using AI", but about how we choose the right tool for each task.

Comparisons that lead to real decisions

One of the most underestimated use cases is analysis before a decision. Not just in business, but in personal life too.

Concrete example: I was looking for a bike for my son. New and second-hand, multiple options, big price differences. Instead of searching "best kids bike", I provided real context: his age, where he'll use it, the risks of a second-hand option, what is worth paying extra for.

We arrived at a clear comparison, with real pros and trade-offs. The decision was no longer based on instinct or what looked best in the pictures.

This is one of the biggest differences: AI doesn't automatically tell you what to do, but it can help you see more clearly what truly matters.

We applied the same process when choosing a laptop for a new team member. We compared options with images, specs, and real feedback, based on what they needed for work.

It became clear which processor made sense, which configuration was worth it, and where there was no point paying extra for branding. Without this kind of analysis, it's very easy to pick something that looks good but won't help you in the long run.

I'll repeat myself, this is where AI helps the most. It doesn't give you the final answer, but it makes your thought process much clearer.

Shopify and debugging: where it's felt the fastest

On Shopify, AI is part of the daily workflow. And this is where the evolution of the last few months is seen most clearly. What a year ago was "nice to have" is now part of the normal way of working.

Liquid has specific logic, limitations, and sometimes hard-to-track behaviors. When an issue arises, instead of wasting time searching forums, we provide the context and ask for options.

We don't use it to write all the code for us. We use it to understand the problem faster and see the possible options.

Often, just the fact that it explains the problem from another angle is enough for you to figure out where the mistake is.

The same goes for Flutter and API integrations. Structure matters, and AI helps you not miss obvious things when you're in the middle of a complex task.

Technical SEO without wasting time

During technical SEO audits, AI helps structure what's a priority and what isn't much faster. When you have a Shopify site with hundreds of pages and performance, canonical, or metadata issues, you don't have time to manually analyze every detail.

We use it to identify patterns, prioritize fixes, and formulate recommendations clearly for the client. It helps us most in generating META details for pages by analyzing the context very quickly.

It doesn't replace expertise, but it heavily accelerates work on repetitive tasks. This means more time is left for the things that actually matter.

Generated images: useful if you know what to ask for

Image generators are part of our toolkit for mockups, presentations, or quick concepts.

They work well when you know exactly what you want. If you go in vague, you come out with something generic. With experience, you learn to be precise in your requests and the results change completely.

It's a learned skill, not a magic button.

This week we also got a little reality check. We thought we already knew how everything worked, but a seemingly simple image took us over 3 hours.

The problem wasn't the tool. The problem was that we wanted to do it quickly, without a well-structured prompt. We kept iterating, back and forth, until we got the desired result.

It was a good reminder. If you don't invest time in the input, you pay for it in the output.

Marketing: structure yes, empty texts no

This is the area where most people make mistakes. They ask for a text, receive something that sounds good but says nothing, and publish it.

We use it for structure, ideas, and validation. We provide real context: audience, goal, possible objections.

Then we rewrite everything in our own style. Otherwise, you publish content that sounds correct but helps no one.

P.S. AI is great for brainstorming and marketing ideas. But without input from someone with experience, you risk quite a bit.

Generally, this is how it works. AI is a tool. A tool that helps professionals be more efficient, not a shortcut to become good overnight in a field you don't master.

From practice to courses and beyond

All this experience doesn't just stay internal. We structure it and take it further in our AI courses.

For us, it's a normal process. You learn, apply, test, see what works, and then pass it on.

This is exactly the idea behind how we work in BNI. Givers gain. The more you help and share your experience, the more it comes back over time.

What's left after this period

After two months of using it constantly, things are pretty clear to us.

It hasn't changed our way of working compared to 2025. But it has helped us optimize our time and work more efficiently. We reach good options faster and waste less time on useless things.

You feel it especially when you have decisions to make or when you're trying to understand why something isn't working.

If you use it without context, it doesn't help much. But if you know what you're looking for and use it to structure your thinking, it starts to make sense.

That's the state of things now, at the beginning of 2026. And the pace at which it's evolving is probably the most important thing to understand.

If you want to get there too with your team, we have workshops for exactly that.