Field reports
What we trialled. And what follows from it.
Every report starts with a question from practice. It shows what was technically feasible, what it means for business architecture and which lessons we take away. Customer names, people and cost figures have been removed.
AI governance
Who may know what, who approves, and how to prove it.
- In trialAI governance
Who may know what: need-to-know enforced in code, not by discipline
How an AI function sees only the data that role and tenant allow, even when a developer forgets a check.
- Business Architecture
- AI
- BuiltAI governance
Knowledge with clearance: content an AI may only use with permission
How to control which AI system may use which piece of company information, separately from who may read it.
- Data Architecture
- Business Data Strategy
- In trialAI governance
Better too careful: classifying data before every AI call
How we decide, before calling a language model, whether an input is confidential and may leave the organisation.
- AI
- Data Architecture
- In trialAI governance
Provable, not claimed: a tamper-evident log for AI agents
How to prove after the fact what an autonomous AI agent did, without anyone being able to change the log unnoticed.
- Infrastructure
- AI
- BuiltAI governance
Microsoft 365 and AI: the value lies in control, not in access
How AI assistants get access to mail, calendar and documents without an all-or-nothing permission.
- Business Architecture
- AI
- BuiltAI governance
Four eyes on every post: a shared governance foundation
How AI-assisted social media posts never go out without human approval, across several channels.
- Business Architecture
- Implementation
Data sources and registers
Connecting external and public data so that provenance and gaps stay visible.
- BuiltData sources and registers
Making public registers machine-readable: annual accounts as data
How published annual accounts of German companies become comparable figures that can be analysed reliably.
- Data Architecture
- Business Data Strategy
- BuiltData sources and registers
Finding public tenders: when portals draw the line
How public tenders from many portals add up to one picture that matches your own profile.
- Business Data Strategy
- Implementation
- BuiltData sources and registers
Trademarks in an agent workflow: connecting register APIs cleanly
How trademark and design data from a European register flows straight into a workflow with AI agents.
- Data Architecture
- Implementation
Identity and security
Sign-in, keys and shared capabilities for people and machines.
- In trialIdentity and security
Signing in without passwords, for machines too
How static passwords disappear from the mail infrastructure, including for automations such as the ERP system.
- Infrastructure
- Business Architecture
- BuiltIdentity and security
One mail route instead of many: reuse beats a new integration
Why new applications send their notifications through one shared, hardened mail route instead of their own providers.
- Business Architecture
- Infrastructure
- From practiceIdentity and security
Secrets do not belong in the process list
How to automate the handling of keys and credentials without exposing them along the way.
- Infrastructure
- Implementation
Operations and observability
Measure, don't estimate: monitoring, cost and quality in daily operation.
- From practiceOperations and observability
When the dashboard goes quiet: observability needs cross-checks
How to tell that monitoring actually monitors, and why “green” is not proof.
- Infrastructure
- Enablement
- In trialOperations and observability
Fewer tokens without losing the line that matters
How to cut the cost of tool output in AI agents without handing customer data to external services.
- AI
- Data Architecture
- From practiceOperations and observability
Measure accessibility, don't estimate it: an audit across 32 pages
How usable a website really is, for every user, in both colour modes and on every screen size.
- Implementation
- Enablement
AI on your own infrastructure
What works locally, what it takes and when a hybrid architecture holds up.
- AssessedAI on your own infrastructure
Local AI on existing hardware: an honest feasibility check
Can an AI assistant run entirely on your own server? A feasibility check for a firm with around 20 workstations.
- Infrastructure
- AI
Matching consulting service
From the Lab into consulting.
What we trial in the Lab, SIMO GmbH puts into practice as consultants: Business Data Strategy & Architecture for AI, following the Zero Friction Data Flow principle. The services behind it:
45 minutes, free of charge, with the SIMO GmbH consultants. The form is on simo-online.com.