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SwissOps

Artificial intelligence

Put AI to work in your business.

Artificial intelligence is no longer a lab experiment: it is an efficiency lever available today. We use it every day for our own products, and we help businesses bring it in where it saves time and money.

Discuss a use case

Why now

What AI changes, concretely

  1. 01

    Hours recovered

    Data entry, sorting, filing, summarising, answering recurring questions: the repetitive tasks that keep your teams busy can be handled automatically, with human validation where needed.

  2. 02

    Costs under control

    An automated process costs a fraction of its manual equivalent and does not stop at night. The investment pays back in weeks, not years.

  3. 03

    Consistent quality

    Models apply the same rules to every document, every request, every record. Fewer omissions, fewer copying errors.

  4. 04

    A lead that is hard to catch up

    The companies structuring their data and processes today are the ones that will benefit from the next generations of models.

Use cases

What we put in place

  1. Document processing

    Invoices, contracts, forms, emails: extraction of the useful information, classification and direct integration into your tools.

  2. Business assistants

    An assistant that knows your procedures, your catalogue or your documentation, and answers your teams or your clients with sources.

  3. Workflow automation

    Complete processing chains, from receiving a request to closing it, with AI at the steps that used to need a person.

  4. Content production

    Product sheets, descriptions, translations, summaries: generated at scale, in your tone, then published automatically. That is what LaminarFlow does.

  5. Data analysis and extraction

    Turn unstructured data (text, PDFs, web pages) into usable tables and indicators.

  6. Support and qualification

    Triage of incoming requests, answers to frequent questions, escalation to the right person with the context already prepared.

What you gain

Time saved first, then money

Three illustrative examples, quantified the way we do during a pilot: the time spent today, and the time left once the process is automated with human validation.

What you gain

  • Today
  • With SwissOps

  • Entering 400 supplier invoices per month

    85% less time

    40 h / month
    6 h / month

  • Answering recurring customer requests

    answers 12× faster

    24 h of delay
    2 h of delay

  • Writing 200 product sheets

    88% less time

    100 h
    12 h

Fictional examples given for illustration, not client cases. Your own figures are measured during the pilot, before anything goes to production.

Cumulative cost over 12 months

  • Manual processing
  • Automated with SwissOps
0102030kCHF036912monthsBreak-even in month 4

Fictional example for illustration: 40 h/month at CHF 80, CHF 9,000 pilot, CHF 500/month to run.

Privacy

Cloud model or local model?

The most capable models, such as Claude, GPT or Gemini, run in their providers’ data centres, usually in the United States. A local model, installed on your servers or with a Swiss host, keeps every piece of data with you, at the cost of weaker results on complex tasks and an infrastructure to run.

The right choice depends on what you hand the model, not on a technical preference. Before the pilot we classify your data with you, then pick for each process the model that meets your obligations (Swiss FADP, GDPR, professional secrecy) and your budget.

Cloud

Claude (Anthropic), GPT (OpenAI), Gemini (Google): the most capable models, used through an API.

  • The best results on complex tasks
  • No infrastructure, pay as you go
  • Business plans that do not train on your data
  • Data is processed outside your walls, often outside Switzerland

Local

An open model (Llama, Mistral, Qwen) installed on your server or with a Swiss host.

  • No data leaves your infrastructure
  • No dependence on a provider
  • A fixed cost, whatever the volume
  • Weaker on complex tasks
  • A GPU server to pay for and run

Both

What we recommend most often: each piece of data goes to the model it allows.

  • The cloud for public or anonymised content
  • Local for personal and confidential data
  • Written rules, applied automatically and auditable

Our approach

From pilot to production

  1. 01

    Identify

    Together we look for the processes where AI brings a real, measurable gain, and set the others aside.

  2. 02

    Pilot

    A prototype on your real data within weeks, evaluated on criteria defined together: accuracy, time saved, cost.

  3. 03

    Industrialise

    Integration into your tools, human validation where it matters, monitoring, logging, and a known operating cost.

From pilot to production, you follow progress, hours and invoices live in your client area.

Discover the client area

Principles

What we hold to

  1. The best models, your data protected

    We use the most capable models available, wherever they are. Sensitive data is not sent to them: it is anonymised, filtered, or processed by a local model installed on your premises when the project requires it.

  2. People decide

    AI proposes, classifies, prepares; the decisions that commit your company remain validated by your teams.

  3. Measure before scaling

    Every project starts with a pilot and clear indicators. What cannot be measured does not get industrialised.

  4. The best tools, no dogma

    We work with the most advanced models on the market and switch when a better one appears. Your solution is tied to no single vendor.

Proof by use

We apply it to ourselves first

LaminarFlow, our content-automation software, orchestrates AI workflows for thousands of publications. Our own development is accelerated by the latest AI tooling. We sell what we use.

Discover LaminarFlow

FAQ

Frequently asked questions

Where do we start if we have never used AI in the company?
With one precise, repetitive, time-consuming process: handling one type of document, one category of incoming requests, one weekly report. We scope it in a single meeting and deliver a pilot on your real data within weeks.
Is our data sent to foreign services?
The best models are not European, and we use them. What is sensitive is simply not sent to them: anonymisation, filtering, or a local model installed on your infrastructure for the data that requires it. We define these rules with you before the pilot.
How much does an AI integration project cost?
A pilot typically costs a few thousand francs and takes a few weeks. The operating cost depends on the volume processed; we measure it during the pilot so you can decide on the next step with full knowledge.
Will AI replace our staff?
In the projects we run, it replaces tasks, not people: it absorbs the repetitive part so your teams can focus on what requires judgement and relationships.

A process to automate? Let’s test it.

Tell us about the task that costs you the most time. We will tell you honestly whether AI can improve it.

Propose a use case