How We Integrated an AI Operating System With Langdock

– And Why Fast-Growing Companies Need More Than Just Tools

When the CEO first invited me for a conversation, it happened at a social event. He leaned over and said:

“We want to become an AI-first company… but honestly, we’re not even sure what that means in practice.”

A few weeks later, I walked into their workshop room and immediately saw the same picture I see in many organisations today:

A team full of ideas. 

A whiteboard full of sticky notes. 

And absolutely no clarity on how AI should fit into daily work.

Everyone wanted AI. Nobody knew where to begin. And the processes in place were never designed for AI at all.

This is the story of how we turned that chaos into a structured AI operating system — complete with assistants, agents, governance, a unified prompt library, and, most importantly, people who now feel confident and safe using AI every day.

Maybe it helps you see what your organisation needs too.

1. The Company: Fast-Growing, International, and Ready for an AI-First Strategy

The client (anonymised) is an international, fast-growing company with 22 employees across different locations. Rapid growth brought increasing complexity — and with it, pressure to standardise workflows.

Their vision was strong:

“We want to introduce an AI-first policy to optimise processes, speed up decisions, and reduce manual work.”

But reality looked different:

• Half the team used ChatGPT secretly, without permission.

• The other half avoided AI because they did not trust it.

• Data protection was unclear.

• Every team had a different understanding of AI.

• Tools were used inconsistently and without guidance.

• No one knew what was allowed — or which tools were safe.

This is where many companies are today. They do not fail because technology is missing. They struggle because structure is missing.

And this is exactly the gap I help close.

2. Step One: Understanding the Business Requirements (Before Touching Any Tool)

Before selecting a single technology, we analysed the business from the ground up.

We evaluated:

• the main AI usage scenarios (current and future)

• the needs and pain points of each department

• the data protection workflow and regulatory constraints

• existing manual processes that slow teams down

• internal knowledge sources that must remain confidential

• success criteria and operational risks

• the company’s readiness for an AI-first policy

One guiding principle shaped everything:

AI must reduce complexity — not add new chaos.

This analysis helped us design a system that fits the business, not the other way around.

3. Why We Chose an AI Operating System (and Why Langdock)

After analysing the requirements, it became clear:

They didn’t just need random tools. They needed an architecture.

A central system that defines:

• which AI models are used

• how data flows

• who is allowed to do what

• how assistants behave

• how prompts are managed

• how processes stay compliant

• how to keep control over tokens, logs, and costs

In short: an AI operating system.

Based in Germany, Langdock fulfilled several key requirements:

• a strong focus on data protection

• support for multiple LLMs (ChatGPT, Claude, Gemini, etc.)

• the ability to build company-specific assistants and agents

• clear governance and permissionsa 

• stable environment for text based use cases

• a user interface that non-technical employees can actually use

• stable APIs for workflows

We deliberately chose a modular ecosystem:

• Langdock for text-based workflows

• A specialised tool for image generation

• A separate tool for slide creation

• A meeting-notes tool optimised for transcripts

• Internal databases connected to selected assistants

AI is not one tool. It is a designed infrastructure.

4. Step Three: Identifying High-Value Use Cases (Not the Shiny Ones)

The initial wish list? Endless.

We filtered everything down based on:

• frequency

• measurable business value

• existing workflow integration

• data protection constraints

• how easily AI could improve quality or speed

• the potential for future automation

We removed use cases that sounded exciting but would not change everyday work. This is where many AI projects derail: the tool works, but the process does not.

5. Step Four: Creating a Scalable Prompt Library

A strong system is not just technology — it is also standards.

We designed a centralised, structured prompt library:

• grouped by department

• named clearly

• easy to maintain

• connected to the right assistants

• aligned with company language

• with guidance for safe and correct usage

A good prompt library is like a playbook. Without it, every person has to reinvent the wheel — and quality becomes a lottery.

6. Step Five: Building the Assistants and Agents

Once the structure was in place, we started building.

Assistants included:

• communication assistant for customer interactions

• assistant for first drafts of social media posts

• onboarding assistant for new employees

• assistant that simplifies explanations of internal systems

• internal knowledge search assistant

• assistant for rewriting internal guidelines

Agents included:

• invoice agent for pre-checking and classifying invoices

• inbox agent for sorting incoming e-mails

• meeting agent that extracts to-dos and decisions from transcripts

• admin automation agent for recurring internal tasks

This is where organisations start to feel the real power of AI:

Tasks that took hours now run in the background — guided, transparent, auditable.

7. Step Six: AI Literacy — The Real Turning Point

The moment the assistants were launched, one thing became obvious:

The system alone is not enough. People need skills.

We trained the team on:

• how AI works (in simple terms)

• how to write effective prompts

• how to evaluate outputs

• how to handle data responsibly

• where the risks and limits are

• how to use assistants as partners, not decision makers

Something shifted.

AI stopped being scary. It became a normal part of work.

People became confident, independent, and curious — the ideal base for an AI-first culture.

8. The Real Challenges Nobody Talks About

1. Integrating AI into existing processes

You cannot simply “put AI on top”. Sometimes processes need to be redesigned. Sometimes teams need to let go of habits that no longer make sense.

2. Predicting costs based on token usage

Usage-based pricing can be difficult to forecast, especially for agents that run many times per day. In my experience, for small teams, Langdock is a very strong and cost-efficient choice. For larger organisations, platforms with flat-rate models for application programming interfaces (APIs) and agents can make budgeting easier.

3. Keeping the knowledge base clean

AI is only as good as the information it receives. Part of the work is to structure knowledge in a way that systems – and humans – can work with.

4. Change management and emotions

AI changes how people work and how they see their role. That requires communication, transparency and a lot of listening.

9. What Changed for the Client

After the implementation:

• repetitive tasks decreased significantly

• onboarding became faster and more consistent

• communication quality improved

• employees became more independent

• knowledge was centralised instead of scattered

• roles and responsibilities became clearer

• productivity increased without increasing workload

• teams trusted the system and themselves

We didn’t “introduce a tool.” We built an AI operating system for the entire company.

10. Why This Matters for Your Organisation

AI does not replace people. But people who work with AI will outperform those who don’t.

This transformation needs:

• a clear strategy

• the right operating system

• governance and standards

• well-designed assistants

• and a strong AI literacy foundation

This is the work I do with my clients — especially small and medium-sized organisations that want to move from:

“We should use AI” to “We know exactly how AI supports our work every day.”

11. If You Want to Build Your Own AI Operating System

If you’re asking yourself:

• Where do we even start?

• Which tools make sense for us?

• How do we integrate AI into our processes without chaos?

• How do we ensure employees feel safe and confident?

…then it might be time to talk.

I support organisations in:

• analysing business requirements

• designing a tailor-made AI operating system

• building assistants and agents

• structuring knowledge

• training employees in AI literacy

If this resonates with you, reach out — on LinkedIn or via my website.

The sooner you build your AI foundation, the faster your people can grow with it.