Artificial intelligence agency in Geneva

AI brings value to companies of all sizes. We mainly work with organizations of 10 to 100 people, but size is not the deciding factor — what really defines the potential is the presence of repetitive tasks and processes that can be optimized.

In a small organization, AI automates low-value tasks so teams can focus on the core business and accelerate growth. In a larger organization, you generally start with targeted use cases on repetitive processes, and economies of scale then quickly amplify the return on investment.

In every case, we assess the expected ROI and productivity gains upfront, before any commitment.

The best way to start is not to start with technology. Before choosing a tool or a model, you need to understand your processes and identify where the friction, the repetitive tasks, and the real optimization opportunities are within your organization.

That's exactly what we do during our strategic audit. We analyze your business, your available data, and your technical environment to identify the most relevant and most quickly actionable use cases. The goal is to build a concrete, prioritized, and costed roadmap, so you know exactly where to start, in what order to proceed, and what each step will bring you.

Starting with an audit helps avoid the classic mistakes — investing in a poorly scoped solution, underestimating data preparation, or aiming too big from the outset. It's the starting point we recommend to every company, whatever their maturity on the subject.

Yes, and it's actually our systematic approach. We never recommend jumping straight to a full solution without first validating the idea in the field.

We build functional prototypes, called POCs, on a limited but representative scope of your real business. In 2 to 4 weeks, you get a first working version that lets you concretely test the value of the use case, measure performance, and verify that the solution integrates well with your processes before any larger investment.

It's a key step that considerably reduces risk. It confirms that the idea works in real conditions, identifies the adjustments needed, and lets you make an informed decision about what comes next, based on concrete data rather than assumptions.

Qubitech is based in Geneva, at 71 avenue Louis-Casaï in Meyrin (1216). We work mainly in Switzerland as well as internationally, notably in France and Germany. Our team is available Monday to Friday from 9 a.m. to 6 p.m., by phone at +41 22 558 25 80 or by email at info@qubitech.ch.

It depends on the complexity of the project. A functional prototype can be delivered in 2 to 4 weeks, while a complete solution with integration and production rollout generally takes several months.

We always recommend starting small and identifying the quick wins — the rapidly actionable tasks that generate concrete, measurable benefits within a few weeks. This validates the approach in the field before scaling up.

To get off to a good start, we recommend beginning with a strategic audit that prioritizes the most relevant use cases and defines a realistic roadmap before any development.

The cost varies depending on several factors, which is why we always recommend starting with an audit to precisely estimate the budget and the potential return on investment.

Even a seemingly simple solution like email automation depends on the number of message types, the existing tools to connect, and the level of integration required. Connectors to mainstream solutions are generally available and speed up development, whereas integration into a more specific system can require more custom work.

The quality and volume of data also play an important role. Well-structured data such as OCR-processed PDFs is easier to exploit than poor-quality scanned documents. A project handling millions of data points will be more complex than one with a few thousand entries, notably because the models will need to rely on larger vector databases to remain reliable.

Finally, the volume of available data directly determines the reliability of the model. Less data means more training, custom tuning, or alternative approaches to guarantee robust results.

AI always relies on quality data in sufficient quantity. This can be customer data, activity history, internal documents, production data, or any other business data specific to your organization. Quality takes precedence over quantity — well-structured data that is representative of your real business yields better results than a large volume of incomplete data.

The most common situation we encounter is data scattered across several tools, formats, or departments. This is not a blocker. During the audit, we analyze the actual state of your data, its origin, and its usability, then we define a suitable strategy to structure, clean, and prepare it.

Depending on the use case, this can involve reworking the raw data, structuring it into a usable format, or vectorizing it so the models can access it efficiently.

If you don't yet have enough data, it's possible to rely on pre-trained models, train a custom model with a limited volume, or set up a data-collection strategy ahead of the project.

Yes. Security is built in from the design stage of every project, and we adapt the architecture to your confidentiality and compliance requirements (FADP / GDPR).

Several levels of deployment are possible depending on your context:

- Proprietary models (OpenAI, Anthropic) with recognized certifications (SOC 2, ISO 27001) and contractual guarantees that your data will not be used for training, via the API or an enterprise contract.

- Open-source model on a shared Swiss cloud server, with no dependency on major foreign providers. Data is not shared between clients, but the physical infrastructure is.

- Open-source model on a dedicated cloud server in Switzerland, where you are the only user of the infrastructure.

- Model deployed locally on your own machines, with no data ever leaving your environment.

Beyond the choice of infrastructure, we systematically apply encryption of data in transit. We can also design the data flows so that sensitive information — such as customer data or internal references — is never sent to the model and stays within your controlled environment.

In our view, the main challenge is to properly structure the project upfront. This requires a rigorous analysis of existing processes, the definition of a clear action plan, and starting with a prototype on a limited scope.

Testing in real conditions before scaling up is the key to the success of an AI project, because it's in the field that you identify the adjustments needed for the solution to be truly suited to your business reality.

The recurring challenges we observe are data quality and accessibility, compliance and security depending on your sector and legislation, and profitability — that is, finding the right use cases relative to the budget invested.

On the risk side, the first is bias in the models. A model trained on data that is not representative of your business reality can produce results unsuited to your context. The second is interpretation errors.

An AI model is not infallible and can make mistakes outside its training scope, which is why we systematically design human-oversight mechanisms for critical decisions.

Finally, there is the risk of technological lock-in, which we limit by favoring modular and documented architectures so you keep control of your solution over time.

Yes, and it's actually a step we require before any development. We estimate the potential ROI as early as the audit phase to make sure the project is worth the investment before starting.

Concretely, the return on investment is measured through productivity gains, reduced operational costs, improved performance, and fewer human errors on repetitive tasks.

But beyond the direct figures, there is an often-underestimated benefit: the time freed up. When AI takes over low-value tasks, your teams can focus on what really matters — the core business, customer relationships, innovation, and so on.

We also identify less visible gains, such as the savings generated between departments or with external parties in the chain, where inefficiencies are often the most costly and the least monitored.

We define clear indicators upfront for every project so the ROI is measurable and tracked over time, not just estimated on paper.

Yes, and it's actually one of our core principles. Our goal is not to replace your existing ecosystem but to integrate AI into it smoothly, so your teams keep working in their usual tools without disrupting their work habits.

This is a point often underestimated in AI projects. Forcing a new interface or tool on employees who have their bearings in an existing ERP or CRM is one of the main causes of adoption failure. The best compromise is very often to integrate artificial intelligence directly into the tools your teams already use every day, so the benefit is immediate and adoption is natural.

Concretely, we connect our solutions to your existing systems — whether an ERP, a CRM, internal databases, or specific business APIs — to ensure a deployment without operational disruption.