Artificial intelligence consulting
Independent AI consulting, from discovery workshop to production. Together we choose the use cases worth pursuing and the right model, including open-source models hosted in Switzerland.
Most AI projects do not fail because of the model, but because the use case was poorly chosen or nobody had set the rules. Our work therefore starts with your processes: where time goes, which decisions repeat, what data is involved. The choice of model comes afterwards, and it is not tied to any vendor.
Why independent consulting
An OpenAI model on Azure, a model from Anthropic or Mistral, or an open-source model such as Llama, Qwen or Gemma each have different strengths, different costs and different consequences for your data. An integrator tied to a single vendor will offer you its platform. We compare the options on your case, with your documents, and we also tell you when AI is not the right answer.
Sorting files and requests
A model reads each incoming file and sorts it according to your rules: those that can follow a simple route and those, fewer in number, that need a human eye. Low-value claims, supplier invoices, customer requests, compliance alerts: the principle is the same, and the rules are yours.
Document extraction and checking
Invoices, contracts, forms, identity documents, reports: the model extracts the useful information, checks that a file is complete and flags inconsistencies. Your teams review the discrepancies instead of re-entering everything.
Internal assistants on your documents
An assistant that answers from your procedures, contracts and files, citing its sources. It can run in Microsoft 365, in your business tools or on infrastructure hosted in Switzerland, depending on how sensitive the content is.
Integration into your tools
Useful AI lives where your teams work: email, ERP, business software, document management. We handle the integration, access rights, logging and monitoring, and we document everything for your IT team.
Our method, from workshop to production
Six steps, each with a deliverable and a clear decision: continue, adjust or stop.
1. Discovery workshop
A working session with your business and IT teams. We review your processes, volumes and constraints: professional secrecy, data protection, tools already in place.
2. Use case selection
We rank the ideas by value, feasibility and risk, and we keep one or two. Cases where an error would cost too much, or where the rule cannot be written down, are set aside.
3. Choice of model and hosting
We test several models, proprietary and open source, on a sample of your documents, and we compare quality, cost and consequences for your data. You receive a reasoned recommendation, with its limits.
4. Measured pilot
The pilot runs on a limited scope, often alongside the existing process. The indicators are set before the start and measured on your own figures.
5. Going into production
Integration into your tools, access rights, logging, supervision, team training and a manual fallback procedure in case of an outage.
6. Governance
Who decides the rules, who changes them, who checks the errors, how often the model is reassessed. We document these points so that the system stays under control over time.
The technologies we work with
Proprietary models: OpenAI (including through Azure OpenAI), Anthropic, Mistral. Open-source models: Llama, Mistral, Qwen, Gemma, deployed on your infrastructure or in a Swiss cloud. Microsoft ecosystem: Microsoft 365, Copilot, Power Platform and Azure, when your teams already work there. We have no exclusivity with any vendor: the list changes as models change, and the choice is always made on your case.
Benefits for your business
Productivity gains
Automate repetitive tasks and allow your teams to focus on high-value activities.
Cost reduction
Lower your operational costs by optimizing your processes and reducing manual errors.
Better decision making
Base your decisions on in-depth data analysis rather than intuition alone.
Competitive advantage
Adopt the technologies that will make the difference against your competitors in the years to come.
Models and hosting: how we choose
There is no best model in general. There is a model suited to a case, to certain data and to a budget. Here is how we reason.
When to choose what
A managed service from a major vendor suits varied, low-sensitivity tasks, small volumes and teams without operations skills. An open-source model hosted in Switzerland becomes interesting when the data is covered by professional or medical secrecy, when the task is repetitive and well defined, when volumes make pay-per-use billing expensive, or when you do not want to depend on a single supplier.
Open-source models hosted in Switzerland
Open models such as Llama, Mistral, Qwen or Gemma can be installed on your own servers or in a data centre in Switzerland. For targeted tasks such as sorting, extraction or summarising, they often give sufficient results, provided they are tested on your documents. Their licence varies from one model to another: we check it before any use.
What "hosted in Switzerland" means in practice
For each project we set four points in writing. Where inference runs: on machines located in Switzerland, either yours or those of a Swiss host. Where the data is: documents, indexes and results stay on that same infrastructure. Who has access: a named list of people, with limited rights. What is logged: decisions and accesses, without keeping more content than necessary. Hosting in Switzerland does not replace your own legal analysis; it simplifies it.
Microsoft, OpenAI, Anthropic or Mistral, when it is the right choice
If your teams live in Microsoft 365 and the content is already there, staying in that environment is often the simplest option. For complex drafting or reasoning tasks, the large proprietary models keep an edge. We then review with you the processing region, the contractual terms and data retention.
What we do not promise
A model sometimes makes mistakes. We do not sell certification, turnkey compliance or full automation. We design systems where the rules are yours, where doubtful cases go to a person and where every decision can be explained after the fact.
Examples and use cases
Client cases and illustrative scenarios published in our resources. The illustrative scenarios describe representative projects, with no named client and no quantified result.
Minor liability claims in a medical institution
Illustrative scenario. An open-source model hosted in Switzerland sorts a medical institution's minor liability claims: automatic settlement for files that meet the institution's rules, human review for all the others.
Read articleOpen source hosted in Switzerland or a US subscription
Decision guide. Data, cost, quality, maintenance, skills: the five criteria for choosing between an open-source model hosted in Switzerland and a US AI service, with a comparison table and the limits of each option.
Read articleSmall insurance claims
Illustrative scenario. AI sorting separates the claims that can be settled directly from those that need a claims handler. The thresholds remain the insurer's and no refusal is automated.
Read articleInvoices, expense reports and VAT at a fiduciary firm
Illustrative scenario. Each document receives a proposed entry or a reasoned anomaly flag. The accountant validates conforming documents in batches and concentrates on the discrepancies.
Read articleKYC files and AMLA alerts
Illustrative scenario. The model checks that files are complete and ranks alerts by priority with a sourced summary. Closing alerts and any compliance decision remain human.
Read articleMail and email automation
A Geneva law firm reduced incoming mail and email processing time by 83% thanks to an AI system automatically classifying each document and message by nature. The system extracts key information, routes to the right files, and alerts on urgencies. Lawyers instantly find any communication through semantic search.
Read articleAutomatic deadline detection
A notary office never misses any deadline thanks to a SharePoint application automatically analyzing documents to identify all legal and contractual deadlines. The system creates calendar events in Teams with progressive alerts, completely eliminating the risk of oversight that could have serious consequences on professional liability.
Read articleIntelligent invoice payment reminders
A financial consulting firm reduced its average payment time by 40% through automated and personalized reminders generated by AI. The system adapts tone and content based on delay duration and relationship history, generating courteous and effective emails. Time spent on reminders dropped by 85%.
Read articleAutomatic legal document summarization
A wealth management company now processes 10 times more reports and contracts thanks to AI document synthesis. The system generates two levels of structured summaries allowing managers to quickly decide if in-depth reading is necessary. Time spent on document review decreased by 70%.
Read articleContract drafting assistant
An international trading company divided its commercial contract drafting time by three thanks to an AI-powered Word add-in. The assistant automatically selects appropriate clauses from a validated base, generates the personalized contract, and checks consistency. Legal professionals now focus on customization and negotiation rather than mechanical drafting.
Read articlePredictive sales and inventory analysis
A pharmaceutical distributor reduced stockouts by 70% and expiry losses by 65% through AI predictive models analyzing five years of history enriched with contextual data. Predictions accurately anticipate seasonal peaks and optimize orders, freeing 120,000 CHF in working capital.
Read articleKnowledge base with semantic search
A software publisher reduced support ticket resolution times by 55% through an AI-augmented knowledge base. Semantic search understands the intent behind questions, and a GPT-4 model generates synthetic answers citing sources. New technicians become operational twice as fast.
Read articleMeeting transcription and action extraction
An audit firm eliminated manual note-taking and saves 12 hours per week through AI transcription of client meetings. The system automatically generates structured minutes, creates Planner tasks for each agreed action, and detects client sentiment. Minutes are sent within the hour following the meeting.
Read article