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Software Development

Custom AI that ships to production, not to a slide deck.

Overview

We design and build AI agents, fine-tuned models and LLM integrations wired into your tools and data — with a senior team in Geneva, from first prototype to a system your business relies on.

AI development is the engineering work of building artificial intelligence into real products: AI agents, fine-tuned models, machine-learning pipelines and integrations of LLMs such as Claude, ChatGPT, Gemini, Mistral or Llama. Unlike off-the-shelf chatbots, custom AI is wired into your data and workflows — and maintained like any serious software.

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Target

For Swiss SMEs and growing companies that have moved past experimenting with ChatGPT and want AI that actually works in their operations — services firms, industry, healthcare, real estate, logistics — with a senior engineering team nearby in Geneva.

Details

Most companies have tried ChatGPT. Few have AI that runs their operations. The gap between the two is engineering: connecting models to your data, handling edge cases, measuring quality and keeping costs predictable.

We build custom AI in Python and TypeScript — agents, fine-tuned models, ML pipelines and LLM integrations with Claude, ChatGPT, Gemini, Mistral or Llama — and take it from first prototype to a system your team relies on every day.

From prototype to production — where most AI projects fail

Getting an impressive demo out of Claude or ChatGPT takes an afternoon. Getting a system that handles real customer data, fails gracefully, stays accurate over time and keeps its costs predictable takes engineering. That second part is where most AI projects stall — and it is exactly what we do.

We treat AI as software. Every system we build ships with an evaluation suite that measures answer quality before and after each change, monitoring that catches drift and errors early, and cost dashboards so token spend never becomes a surprise on the invoice.

What we build

Custom AI takes different shapes depending on the problem. Sometimes the answer is an agent that processes incoming requests end to end; sometimes it is a model fine-tuned on ten years of your documents; sometimes it is a focused LLM integration inside the software your team already uses.

  • AI agents that read, decide and act across your tools — quoting, triage, follow-ups
  • LLM integrations inside your CRM, ERP or business applications
  • Fine-tuned models when prompting alone can't reach the required quality or format
  • ML pipelines: data preparation, training, evaluation and automated deployment
  • RAG systems that answer from your internal documents, with sources — see our enterprise RAG offer
  • Local AI deployments when confidentiality rules out the cloud

The right model for the job

We are provider-neutral by principle. Claude, ChatGPT, Gemini, Mistral and Llama each have strengths — reasoning, cost, speed, multilingual quality — and the right architecture often combines several. We benchmark on your actual tasks before committing, not on marketing pages.

When your data can't travel — professional secrecy, patient records, nLPD constraints — we deploy open-source models on your infrastructure or in a Swiss cloud. Confidentiality becomes an architecture decision, not a leap of faith.

How we work

We start with a short scoping phase: your use case, your data, a realistic estimate of what AI can and cannot do for it. Then we build in Python or TypeScript, in short iterations you can test from week one.

You keep a single senior contact throughout. No account managers relaying messages, no junior teams learning on your budget — the people who scope your system are the people who build it.

What it includes
  • •AI agents that execute multi-step business tasks
  • •LLM integration: Claude, ChatGPT, Gemini, Mistral, Llama
  • •Model fine-tuning on your domain data
  • •ML pipelines: data preparation, training, evaluation, deployment
  • •Python and Node.js/TypeScript engineering
  • •Evaluation, monitoring and cost control in production
  • •Custom MCP servers to connect AI to your tools
Deliverables
  • •A working AI system integrated into your tools, running in production
  • •Source code, documentation and deployment pipeline — you own everything
  • •An evaluation suite that measures quality before and after every change
  • •Monitoring and cost dashboards for tokens and infrastructure
  • •A direct senior contact for maintenance and evolutions
Takeaways

  • Custom AI means agents, fine-tuned models and LLM integrations wired into your tools.

  • We work with Claude, ChatGPT, Gemini, Mistral and Llama — the model fits the task.

  • The hard part is production: evaluation, monitoring and cost control are included.

  • AI is software: Python, TypeScript, clean engineering, code you own.

  • Senior team in Geneva, measured results, no slide decks.

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Frequent questions

How much does custom AI development cost in Switzerland?

It depends on scope. A focused LLM integration inside an existing tool starts at a few thousand francs; a production agent or fine-tuned model with evaluation and monitoring is a larger project. We always begin with a scoping phase that prices your specific case before any commitment.

Do we need to fine-tune a model, or is prompting enough?

In most cases, careful prompting combined with RAG on your documents gets you there faster and cheaper. Fine-tuning earns its cost when you need a very specific style or format, lower latency, or a smaller model for local deployment. We test the simple option first and only escalate when the measurements say so.

Which LLM should we choose — Claude, ChatGPT, Gemini or an open-source model?

There is no universal winner. The right choice depends on the task, the languages involved, latency and budget — and it changes as models evolve. We benchmark candidates on your real data and often combine several: a strong model where quality matters, a fast cheap one for high-volume steps.

How long does it take to build an AI agent?

A first working prototype typically takes two to four weeks. Hardening it for production — edge cases, evaluation, monitoring, integration into your tools — usually takes another one to two months depending on complexity. You test real versions throughout, not mockups.

What about our data and nLPD compliance?

We map your data flows before writing any code: what may go to a cloud model under contract, what must stay on Swiss or European infrastructure, what should never leave your network. When needed, we deploy open-source models locally so sensitive data simply doesn't travel.

Can you integrate AI into our existing software?

Yes — that is the most common case. We connect models to your CRM, ERP or custom applications through their APIs, so AI shows up inside the tools your team already uses rather than in yet another window. We built exactly this kind of integration for the Mellender real-estate CRM.

Do you build MCP servers?

Yes. MCP (Model Context Protocol) has become the standard for wiring Claude, ChatGPT and other models into business tools. We design custom MCP servers — data access, actions in your systems, controlled permissions — and use several daily in our own operations.
Next steps

Let's build AI that actually ships.

Talk about your projectSee our AI agents