A New Potential Is Emerging in AI with MountainTech+

Artificial intelligence is no longer the preserve of large corporations; it is equally the field of focused, inventive teams. MountainTech+ was founded in 2021 on a single observation: there was a wide gap between what was being said about AI and the systems that could actually be put into service on the ground — and until that gap closed, every number on the slide stayed on the slide.

This article is not a product pitch. It sets out how we work, which jobs we take, and which ones we deliberately turn down. Those are the things worth knowing when choosing a technology partner.

Where we came from#

The company was founded when engineers who had spent years inside production and quality processes in the automotive industry sat down at the same table as a team coming from software. That combination was not an accident; it was the founding choice.

A software team that does not know the shop floor can build a technically flawless system that is of no use to anyone. A field team that does not know software can describe the right problem but cannot build the solution. Deployable systems come out of the intersection of the two. Our office is in Nilüfer, Bursa, and we work across industry, education, human resources and web.

Why Bursa?#

Where we are based is not a logistical footnote for us, it is part of how we work. A team that does its process analysis on site should not be a flight away from the site.

Bursa is one of the densest regions in Turkey for automotive and metalworking. That means the areas where AI finds its most concrete application — quality inspection, process selection, production data — all sit in the same city. Seeing a production line in person produces a different project than hearing about the same line over a meeting screen.

Alongside industry we work in education and human resources today, but the habit of watching the process on site stays the same across all of them.

How we work#

Every project runs through the same four steps, and the order matters.

1. Process analysis. We watch how the work runs on site and agree together on the step genuinely worth automating. Skip this and the project ends up automating something nobody was complaining about.
2. Feasibility and prototype. We build a small-scale prototype on your own data and measure the target metric before anything goes live. Learning in three weeks that an idea will not work is cheaper than learning it in six months.
3. Deployment. The system is integrated into your existing infrastructure and handed over with training and documentation for your team.
4. Monitoring and improvement. Performance is tracked after go-live, and the model and rules are updated from the feedback that comes back.

The fourth step is the one most often skipped. When processes change the data changes, and when the data changes the pattern the model learned starts to age. An AI system without maintenance quietly goes blunt.

What we believe#

Four headings, and all four decide day-to-day calls.

Technology without the hype. If a problem is solved by plain software, we do not propose AI. The right tool is not the most impressive one. That position costs work in the short term and is the only sustainable one in the long term.

Measurability. At the start of every project we write down, as a number, what we are trying to improve; at the end we look at the same number. "Efficiency went up" is an impression, not a result.

Data responsibility. Customer data belongs to the customer. Retention, access and deletion rules are agreed in writing at the start of the project. Where it is needed, an on-premise installation is made.

Long-term partnership. We do not deliver a project and walk away; your team taking ownership of the system is part of the handover. A system nobody can use is a system that was never built.

What we have built so far#

A vision statement with nothing concrete under it is worth little. The work running in the field today falls under roughly four headings.

• Quality and decision support in industry. Quality inspection systems running on image processing on the production line, and assistants that carry technical selection processes. The KromAsistan project is one example.
• Measurable study time in education. A question-solving and study platform that turns study periods into data; the project we run with İspat College sits on that infrastructure.
• Screening in human resources. HireUp360, our recruitment platform, where an AI runs the first interview and produces a reasoned score for every candidate.
• Web and customer communication. Digital assistants that talk to visitors, point them to the right service and leave behind a qualified enquiry.

Some of these are client projects and some are our own products. The second kind earns us one more thing: we use the systems we build. We publish our open roles through HireUp360, and the postings in the careers section of this website come straight from it.

Which jobs we turn down#

The fastest way to understand a company is to look at what it refuses.

• Work with an undefined problem. "We need AI too" is not a requirement. If how long the work takes and what is supposed to improve are not written down, we write that first.
• Work that does not need AI. Building a model for a process with clear rules and few exceptions is extra complexity. In that case we recommend classical software.
• Work with no data and no data plan. If the data a model needs does not exist, the project starts with collecting it. Saying so up front beats discovering it halfway through.

The new balance: small team, narrow scope, real delivery#

The most important shift in AI in recent years is that powerful models became available to everyone. The consequence is this: the competition is no longer about who trained the model, it is about who described the problem correctly.

For focused teams like ours that is a genuine change in the balance. Understanding a problem, keeping the scope tight and handing over a working system — these are done through proximity, not scale. We see concentrating on a small number of projects as a choice rather than a constraint.

We set out to be a team that builds the future of AI rather than one that only talks about it. If you would like to discuss where to start in your own processes, get in touch — or first take a look at how we work.