Development of artificial intelligence solutions

Artificial intelligence solutions integrated into your ecosystem.

Our 5 areas of AI development expertise

Five complementary disciplines covering enterprise AI use cases in production. Select an area to see concrete use cases.

Generative AI & intelligent agents

Generative AI and intelligent agents — powered by LLMs, business-specific fine-tuning, and RAG (Retrieval-Augmented Generation) architectures — let you leverage your internal data and carry out complex business tasks while interacting with your existing tools.

Use cases
A semantic search engine able to query thousands of internal documents and business records to instantly retrieve the relevant information.
An autonomous procurement agent that analyzes needs, compares suppliers, and automatically generates calls for tenders.
A financial agent that handles invoice filing, payment reconciliation, and the pre-entry of accounting records submitted for validation.
A sales copilot connected to your CRM that analyzes customer history, recommends priority opportunities, and optimizes field visits through real-time scoring.

4 steps to secure your AI project

A well-run artificial intelligence project is never a black box. Here's how we structure every engagement, from initial scoping to long-term maintenance.

Feasibility study

We analyze your needs, the quality of your data, and your technical environment to identify priority use cases, business constraints, and security stakes. This phase validates the project's feasibility, estimates the ROI, and anticipates the main risks.

1–2 weeks
A clear view of the opportunities, constraints, and value potential before any commitment

Prototype (POC)

We build a functional prototype on a limited but representative scope of your business. This lets you concretely validate the added value, performance, and fit with your business processes before any larger investment in scaling up.

2–4 weeks
Concrete validation of the value, with measurable results on a real use case

Development & integration

We turn the prototype into a robust, scalable, and secure solution, then integrate it into your existing ecosystem (ERP, CRM, databases, business APIs). The goal is to ensure smooth adoption and day-to-day operational use, without disrupting your current tools and processes.

1–4 months
A reliable solution, integrated with your tools and ready for daily use by your teams

Deployment & evolution

We ensure a gradual production rollout, support for your teams, and documentation to guarantee a transfer of skills. We also handle monitoring, retraining, and the continuous evolution of the models so they stay aligned with your business needs.

Ongoing
A solution that stays operational over time, with support, monitoring, and continuous improvement

Who our artificial intelligence solutions are for

We support organizations that want to leverage their data and integrate AI operationally, with a concrete impact on their performance.

SMEs

For companies in every sector seeking a competitive edge through intelligent automation. We design custom solutions tailored to your processes and business reality to optimize your operations, reduce costs, and free up your teams' time for higher-value work.

Regulated sectors

For environments where confidentiality, traceability, and compliance (nFADP, GDPR, ISO standards) are non-negotiable requirements. We design secure solutions, deployable on controlled infrastructure, to guarantee data protection and regulatory compliance, particularly in the Swiss and European context.

Business departments

For operational teams (sales, HR, finance, production, operations) that want to gain productivity without adding complexity to their tools. We build solutions integrated into your work environment to automate processes, make operations more reliable, and improve day-to-day decision-making.

IT managers and CTOs

For technical teams that want to integrate AI in a structured and sustainable way. We act as a partner, with an advisory approach focused on skills transfer, so you can progressively master these technologies in-house, without excessive dependence on an external provider.

Every organization has automation and optimization levers that often go underused. A conversation quickly surfaces concrete options tailored to your context.

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Frequently asked questions

Here are answers to the most common questions to better understand our approach to artificial intelligence and how we support our clients.

Yes, and it's actually what makes an AI solution truly relevant to your business. Your internal data — whether documents, customer history, production data, or business databases — is what sets a generic solution apart from a tool genuinely suited to your reality.

That said, raw data can't be used as-is. Depending on its state, it must be cleaned, structured, formatted, or vectorized to be usable by a model. This is an essential step we carry out ahead of development. The higher the quality of your data, the more reliable and accurate the results.

Yes, an AI model can make mistakes, and it's essential to design it with that reality in mind rather than ignoring it.

We systematically build in human-oversight mechanisms for critical decisions. Concretely, this means the AI can propose, suggest, or prepare an action, but an employee validates it before any execution on sensitive matters. We can also configure confidence thresholds below which a task is automatically handed back to a human.

Moreover, every human intervention can be used to improve the model. The AI learns from your corrections and progressively refines itself to your real business context. This is what's called the feedback loop, and it's one of the most powerful levers for improving reliability over time.

Data security is the number-one concern we hear from Swiss companies, and rightly so. We design every solution starting from your confidentiality requirements, not by adding them at the end of the project.

Several deployment levels are possible depending on your context. Proprietary models like OpenAI or Anthropic hold recognized certifications (SOC 2, ISO 27001) and offer contractual guarantees via the API or an enterprise contract. It's also possible to deploy an open-source model on a shared Swiss cloud with no dependency on foreign providers, on a dedicated cloud in Switzerland where you are the only user of the infrastructure, or directly on-premise on your own machines, with no data leaving your environment.

We systematically apply encryption of data in transit and can design the data flows so that sensitive information is never sent to the model.

Yes, it's our standard approach. We connect our solutions to your existing systems via APIs, native connectors, or custom integrations. The goal is for your teams to keep working in their usual tools, with AI integrated directly into their daily workflow.

A chatbot follows predefined scripts and responds to keywords. An AI agent understands context, reasons, makes decisions, and acts autonomously by interacting with your existing tools such as your CRM, ERP, or databases. It's fundamentally different in terms of capability and operational value.

Not necessarily. Depending on the use case, we can rely on pre-trained models and adapt them to your context with a limited volume of data. For RAG architectures, for example, your existing documents are enough, with no additional training. We assess what is actually needed for your situation as early as the scoping phase.

RAG stands for Retrieval-Augmented Generation. It's an architecture that lets a language model query your internal documents and databases to produce precise, contextualized answers. It's particularly well suited when you want to leverage your proprietary, historical, or document data without exposing it to an external model. We use it notably for semantic search engines and document assistants.

Fine-tuning consists of specializing a pre-trained model on your own data so it understands your business vocabulary, your processes, and your specific use cases. It's what turns a general-purpose model into a tool genuinely suited to your sector.

NLP, or natural language processing, encompasses all the techniques that let a machine understand, analyze, and generate text or speech. It's the foundation of voice assistants, sentiment analysis, transcription, and automatic summarization.

Computer vision lets a machine analyze images and video to identify, classify, or detect visual elements. It's used for quality control, automated inventory, data extraction from scanned documents, and anomaly detection on equipment.

Edge AI refers to deploying models directly on physical equipment, sensors, or connected devices, with no dependency on a remote server. This enables real-time processing even without connectivity, with minimal latency and maximum confidentiality.