What Founders Get Wrong About Building with AI

These days, everyone wants to “add AI” to their product. Chatbots here, auto-suggestions there, some GPT magic sprinkled around, and boom, they call it innovation.

But the truth is that, most of the time, AI gets added like a bell or a whistle. It looks nice. Feels modern. But doesn’t actually do much.

At Allevio Soft, we’ve seen this happen again and again. And in this article, we want to show you how we helped one of our clients rethink AI as a real solution to a real-world problem.

The Problem

Our client is a fast-growing recruitment consultancy in Sydney. Great team, solid culture, cool clients. But hiring was a total chaos.

Every job post brought in hundreds of resumes. Their small team couldn’t keep up. Good candidates were slipping through the cracks. Recruiters were spending hours scanning CVs just to find a few decent profiles worth shortlisting.

So, naturally, the owner came to us with an idea:

Can we use AI to rank the resumes and show us the best 20?

We said: Sure, that sounds like a good idea. But… let’s pause for a second. Just building a model that scores resumes based on keywords? That’s not solving the problem. That’s just moving the problem into a black box.

Here’s what we told them:

• What if the AI starts favouring candidates from fancy schools just because that’s what it “learned”?

• What if someone took a career break or switched domains, and the model flags them as low-quality?

• What if the AI keeps making the same mistakes and no one knows why?

You end up with bias, no visibility, and a lot of guesswork. That’s not what AI should do.

Our Approach

We sat down with their recruiters and said:

Let’s build a screening assistant, not a robot judge.

Here’s what we built:

• AI that reads in context. It doesn’t just scan for keywords. It looks at projects, work history, transitions, even soft skills. We prompted and trained it to spot real potential, not just buzzwords.

• Every time a recruiter accepted or rejected a suggestion, the model learned. Over time, it started to understand the hiring manager’s style and what “good fit” actually meant.

• No black box nonsense. For every recommended profile, the AI showed why it surfaced it. Things like "Matched with previous hires," or "Strong overlap with required tech stack."

• It flagged interesting edge cases. You know those folks with a wild, non-linear career path? Or someone who freelanced but never had job titles that stood out? The AI flagged them for manual review, not rejection.

The Results

• The HR team spent 60% less time shortlisting candidates.

• They spotted candidates they would’ve otherwise missed.

• The AI became a trusted teammate, not a weird guess machine.

• And recruiters felt in control.

It wasn’t a gimmick. It was useful.

And what we understood

AI isn’t here to replace your team. It’s here to help them. But only if you build it right.

We don’t just “add AI” to things. We sit down, look at the real pain points, and ask:

If AI had existed when this problem started, how would we solve it differently?

That’s the difference between adding AI as a cool feature vs. building something that actually makes life easier.

If you’re a founder, product owner, or hiring manager thinking about using AI - don’t ask where to stick it. Ask what you can take off your team’s plate with it. And then build from there.

Want help rethinking AI in your product? Drop us a message at Allevio Soft. We’d love to chat.