AI Automation Switzerland
AI systems that reduce manual admin work and speed up answers
We build AI workflows for Swiss SMEs so teams can copy less, respond faster and make better use of existing company knowledge.
RAG, automation, permissions and Swiss-first data-flow review remain visible as technical proof — but the first priority is whether the workflow helps in daily operations.
BUSINESS FIRST
No AI theatre. Real workflow support.
AI must support real processes. Chatbots alone are not a strategy. Company knowledge needs structure before AI can answer, route or automate usefully.
Recurring work is prepared, routed or automated.
Teams find answers from approved knowledge faster.
Usage, escalations and answer quality become visible.
METHODOLOGY
The GlasBox AI Operating System
A structured approach to secure AI automation: clarify data, structure knowledge, build RAG, automate processes and measure usage.
Knowledge Architecture
Company knowledge mapping, document sources, FAQ, SOP, policy and offer knowledge.
Business result:People find answers faster.
Secure RAG Layer
Retrieval from approved content, source-aware answers and reduced hallucination risk.
Business result:AI answers become more reliable and easier to check.
Process Automation
Lead qualification, internal routing, CRM/API handover and recurring admin flows.
Business result:Less copy-paste and fewer manual handovers.
Measurement & Governance
Usage tracking, answer quality checks, escalation rules and privacy-by-design thinking.
Business result:Management can see impact, adoption and risk.
OUTPUT
What we actually build
Internal AI Assistant
Answers questions from approved internal knowledge.
RAG Knowledge Base
Turns documents, FAQs, SOPs and service knowledge into searchable context.
Lead Qualification Assistant
Qualifies website enquiries and prepares structured handover.
AI Workflow Automation
Automates repetitive steps between website, CRM, email, forms and APIs.
AI Website / Chat Layer
Helps visitors understand services and route themselves faster.
Integration Layer
Connects AI output with CRM, API, dashboards or internal tools.
RAG
RAG explained simply
RAG means the AI does not rely only on general model knowledge. It first retrieves approved information from your company context. That makes answers more precise, more traceable and easier to control.
Only content you have released is used as knowledge.
The system finds the relevant passages for each question.
Rules define what the AI must not answer on its own.
Users get a traceable answer — or a human escalation.
Finds relevant knowledge even when users ask differently.
Users can see where an answer comes from.
The AI knows what it should not answer or where to escalate.
Management can check whether the system is useful and safe.
PRIVACY
Built for Swiss privacy expectations
Only necessary information is processed or stored.
Public and internal knowledge are considered separately.
Swiss/EU hosting and tool choices are discussed based on risk.
Sensitive cases receive fallbacks and human approval paths.
Not legal advice. We build technical systems so that privacy, data minimisation and traceability are considered from the start.
PROCESS
How an AI project with GlasBox works
AI Readiness Check
Clarify goals, risks, knowledge sources and process candidates.
Knowledge & Data Architecture
Define what may be used, what stays private and what needs structure.
Prototype / Controlled PoC
Build a small working system with a real use case.
RAG / Automation Build
Implement retrieval layer, prompts, APIs, UI and escalation logic.
Testing & Governance
Check answer quality, privacy assumptions, logs and fallback paths.
Launch & Improvement
Training, monitoring, iteration and monthly optimisation.
Proof of work
Reference — local service SME in the canton of Lucerne
- Type
- Website for a local service SME in the canton of Lucerne
- Starting point
- Local service business needed a trustworthy web presence with guided enquiries.
- Scope
- Service website, local visibility groundwork, live chat, enquiry guidance.
- Stack
- Custom code (no page builder), live-chat layer, structured local content.
- Result
- Live since 2026 — production live-chat milestone. Public review profile can be inspected externally; no ROI or conversion metric is claimed here.
- Hosting / privacy
- Swiss-first hosting strategy, nDSG/FADP-aware delivery.
OFFERS
Entry offers for AI and RAG
Start small with a workshop, test a real RAG use case or plan a production AI system with integrations.
AI Readiness Workshop
- Use-case discovery
- Data and risk check
- Process shortlist
- Feasibility view and roadmap
RAG Prototype
- One defined use case
- Limited knowledge base
- Retrieval setup and basic interface
- Testing report with next steps
Production AI System
- Secure architecture
- Integrations and governance
- Logs, launch support and improvement
- Optional monthly operation
BUSINESS / ENGINEERING
Technology with business meaning
Business View
- Fewer manual steps
- Faster knowledge access
- Better lead handover
- Clearer escalation
- Measurable adoption
Engineering View
Users do not need exact keywords; the system finds company knowledge by meaning.
API HandoverQualified information moves to CRM or internal tools without copy-paste.
Fallback LogicUnclear or sensitive cases are not answered blindly; they are escalated.
PROOF OF WORK
Proof of Work — what you actually receive
Which AI cases are realistic.
Which sources are usable, private or risky.
Which content is suitable for RAG.
Real questions for quality checks.
When AI should not answer.
What follows after prototype or go-live.
Until verified results are available, we show proof of work, delivery scope and technical implementation transparently. No invented numbers.
MEASUREMENT
Measurable, not magical
FAQ
AI Decision Check
What is the difference between AI automation and a chatbot?
A chatbot usually answers individual questions. AI automation connects knowledge, processes and systems so enquiries, internal tasks or workflows can be handled in a structured way.
What is RAG?
RAG means the AI first retrieves approved company information and uses it to form an answer. That makes answers more precise, more traceable and easier to control.
Is this safe for Swiss SMEs?
Safety depends on the setup. We plan data minimisation, access, separation of internal and public knowledge, logging and escalation from the start. This is not legal advice.
Do we need clean data first?
You do not need a perfect data programme, but you do need usable knowledge sources. The Readiness Check clarifies which documents, FAQs, SOPs or process data are suitable and what needs structure first.
Can the system use our own documents?
Yes, if the documents are approved and structured sensibly. Typical sources are PDFs, FAQs, SOPs, offer knowledge, internal policies and website content.
Can AI connect to CRM, APIs or internal tools?
Yes. API and workflow integrations can pass qualified information to CRM, email, forms, dashboards or internal tools.
What does a prototype cost?
A clearly limited RAG Prototype starts visibly from CHF 4'900. The exact scope depends on the use case, knowledge sources, interface and integrations.
How long does implementation take?
A Readiness Workshop can happen quickly. A controlled prototype often takes a few weeks; production systems with integrations and governance are built in phases.
What should not be automated?
High-risk decisions, legally sensitive individual cases and processes without clear responsibility should not be automated blindly. They need escalation or human approval.
How is success measured?
We measure usage, answer quality, manual steps, escalation rate, lead quality, CRM handovers and recurring error or fallback patterns.
NEXT STEP
Want to know which AI use case is realistic?
Start with a Readiness Workshop or plan a limited RAG prototype.