AI Agents and Assistants
We build AI agents that take on clearly defined tasks and relieve teams in document-heavy workflows.
- Internal assistants for teams
- Preparatory analysis and summaries
- Human approval at critical steps
AI Automation in Zug
AI Automation in Zug.
Less manual process work, more measurable impact.
Memago helps businesses in Zug implement AI automation, AI agents, and LLM integration for processes with significant manual effort.
Services
AI automation for businesses with real workflows, not isolated demos
We automate document-heavy, cross-system, and knowledge-heavy workflows where repeated manual coordination slows teams down.
LLM Integration into Workflows
We connect AI to existing systems, data sources, and workflow steps so it creates value inside the process.
- LLM integration into existing systems
- Automated handoffs between tools
- APIs, data flows, and system integration
Delivery with Governance
We design automation with quality, ownership, and data handling from the start.
- Data categories and usage boundaries
- Review and approval processes
- Rollout with clear ownership
Typical Use Cases for AI Automation
Especially relevant where documents, reporting, coordination, and knowledge work create repeated overhead.
Document Processing
Classify documents, extract relevant content, and prepare information for downstream processes.
Reporting and Coordination
Prepare reports, status updates, and draft work faster for finance, controlling, and operations teams.
Internal Knowledge with RAG
Make internal documents, policies, and knowledge bases accessible through focused RAG systems.
Review and Approval Flows
Prepare drafts, responses, or recommendations and feed them into existing approval steps.
Internal Assistants for Teams
Build assistants for research, summarization, prioritization, and day-to-day knowledge work.
LLM Integration into Workflows
Connect existing tools, data sources, and APIs so AI works inside the process.
Build Governance, Quality, and Data Handling into the Solution
In business settings, AI automation needs data handling, human control, and traceability planned together.
Data Categories and Boundaries
We define which data may be processed and what protections each workflow needs.
Human in the Loop
For sensitive decisions, review, approval, and accountability stay clearly with people.
Access and Traceability
Roles, permissions, and logging are set up so usage stays safe and traceable.
How the Collaboration Works
Pragmatic and grounded in real processes instead of generic AI demos.
Understand Processes and Bottlenecks
We identify where manual work, handoffs, or repeated knowledge tasks consume time.
Prioritize the Right Use Cases
We prioritize the use cases with realistic value and acceptable risk.
Build a Pilot or Prototype
We build an initial workflow and define the review and quality mechanisms it needs.
Secure Rollout and Operations
We turn the pilot into a durable process with ownership and monitoring.
Frequently Asked Questions
Where do you start when there are many AI ideas?
Start where recurring tasks, documents, or internal coordination already create visible overhead.
Is this only for financial companies?
No, the offer applies across industries wherever automation demand is real.
Do we need a large AI project immediately?
No, a focused pilot or prototype is often the better first step.
How is data protection handled?
With data categories, technical boundaries, review processes, and an architecture that fits the risk profile.
Can existing systems be integrated?
Yes, much of the value comes from connecting AI to existing tools, data sources, APIs, and workflows.
Do you also work on-site in Zug?
Yes, collaboration can happen on-site in Zug or remotely.
Founder
Patrick Lemke
Your contact for AI automation, AI agents, and workflow integration in Zug.
Request a Discovery Call for AI Automation
Briefly describe your processes, goals, and whether you want to evaluate opportunities, build a pilot, or extend existing systems.
Or by email: info@memago.ch