Where does AI actually pay off in our company?

Where a lot of knowledge, many documents and recurring alignment work meet limited resources – typically in engineering, testing, project management, sales, purchasing and knowledge management.

Which of our processes can we already automate today?

That depends on your processes. Tasks with many documents and recurring alignment work are often a good fit, such as meeting minutes, status reports or transferring data between systems. We find out which ones apply to you in the free initial call.

How can AI agents take load off our engineers and project managers?

By maintaining technical documentation, deriving test cases directly from the requirements and creating meeting minutes and status reports: engineers get back to developing instead of maintaining documents, and project managers steer instead of chasing people.

How do we integrate AI into existing systems and processes?

Step by step: first we identify tasks where AI delivers concrete business value, then workflows are restructured so that AI can be integrated meaningfully – not as an add-on tool, but rethought from the ground up.

Which tasks can AI agents handle on their own?

Defined tasks that they handle largely on their own: analyzing information, creating documents, transferring data between systems, preparing decisions and triggering follow-up actions.

How can we start with a small pilot project instead of a large AI program?

The start can be small: one specific process, one team or a single AI agent.

What could our company look like if AI takes over a large part of the operational work in the future?

Several specialized AI agents take over complete process chains. People step in where decisions, responsibility or expertise are needed and increasingly take on a steering and supervising role.

What does the initial call cost?

Nothing. The initial call is free, takes 30 minutes and is held online via Microsoft Teams.

Do you consult on site or remotely?

Both: on site in the Munich area, remotely throughout Germany, Austria and Switzerland.

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AI consulting for technical companies

Integrate AI into your processes – where it really pays off

I help technical SMEs integrate AI systematically into their existing processes and hand over tasks to AI agents step by step. Because there is a big gap between a few prompts in Copilot or ChatGPT and a company where AI productively runs processes.

Where can AI actually take over work in your company?

Identify AI potential
Redesign processes with AI
Put AI agents to productive use

See examples

About me

Dr. Hai Van Le Jorks

Founder · Electrical Engineer · Consultant for AI Transformation

Dr. Hai Van Le Jorks holds a PhD in electrical engineering, worked for around 10 years as a development engineer in the automotive industry and founded Osos AI GmbH, which develops OSOS/Omega, a SaaS platform with an AI agent for requirements engineering.

PhD in Electrical Engineering
Around 10 years as a development engineer in the automotive industry – power electronics and high-voltage battery systems
Founder of Osos AI GmbH
Building OSOS/Omega, a SaaS platform with an AI agent for requirements engineering

“I don’t just advise on AI agents – I work with them in my own company every day.”

My approach: I run an AI-native company myself

In my own company, a large share of operational tasks is already supported or handled by AI agents. So I know not only the theoretical potential of agentic AI, but also the practical questions:

Which tasks can really be automated?
Where do AI agents need clear processes and control mechanisms?
How do you organize collaboration between people and AI?
How do you prevent many AI tools from turning into an unmanageable system?
How does a company change when AI is no longer just an assistant but an operational actor?

Engineering expertise meets AI expertise

As a development engineer in the automotive industry, I experienced complex development organizations, technical interfaces and processes first-hand: V-model and systems engineering, requirements engineering, development and release processes, technical documentation, testing and validation, and the collaboration between different departments.

That is why I don’t look at AI only from an IT or management perspective – I understand the reality of technical development processes.

You can start small.

One specific process, one team or a single AI agent.

Typical questions companies bring to me

“Where does AI actually pay off in our company?”
Where a lot of knowledge, many documents and recurring alignment work meet limited resources – typically in engineering, testing, project management, sales, purchasing and knowledge management.
“Which of our processes can we already automate today?”
That depends on your processes. Tasks with many documents and recurring alignment work are often a good fit, such as meeting minutes, status reports or transferring data between systems. We find out which ones apply to you in the free initial call.
“How can AI agents take load off our engineers and project managers?”
By maintaining technical documentation, deriving test cases directly from the requirements and creating meeting minutes and status reports: engineers get back to developing instead of maintaining documents, and project managers steer instead of chasing people.
“How do we integrate AI into existing systems and processes?”
Step by step: first we identify tasks where AI delivers concrete business value, then workflows are restructured so that AI can be integrated meaningfully – not as an add-on tool, but rethought from the ground up.
“Which tasks can AI agents handle on their own?”
Defined tasks that they handle largely on their own: analyzing information, creating documents, transferring data between systems, preparing decisions and triggering follow-up actions.
“How can we start with a small pilot project instead of a large AI program?”
The start can be small: one specific process, one team or a single AI agent.
“What could our company look like if AI takes over a large part of the operational work in the future?”
Several specialized AI agents take over complete process chains. People step in where decisions, responsibility or expertise are needed and increasingly take on a steering and supervising role.
“What does the initial call cost?”
Nothing. The initial call is free, takes 30 minutes and is held online via Microsoft Teams.
“Do you consult on site or remotely?”
Both: on site in the Munich area, remotely throughout Germany, Austria and Switzerland.

Use cases

Which tasks can AI agents take over in technical companies?

Above all in six areas: requirements and technical documentation, testing and validation, project management, sales and quotation, purchasing and data transfer between systems, and knowledge management. That is where a lot of knowledge, many documents and recurring alignment work meet limited resources for automation.

Engineering: requirements & technical documentation

Engineers get back to developing instead of maintaining documents.

Today: Requirements, specifications and release documents are written, cross-checked and updated by hand – often spread across Word, Excel and ALM tools.
With AI: AI agents draft requirements and documents, check them for gaps and contradictions, and keep the links between requirement, design and test up to date.
Value: Less routine work for engineers, more consistent documentation and faster development and release cycles.

Testing & validation

Test cases and evaluations come straight from the requirements.

Today: Test specifications are derived from requirements by hand, and test results are painstakingly collected and assessed.
With AI: AI derives test cases from requirements, summarizes test results and flags deviations that a person should review.
Value: Higher test coverage for the same effort and faster feedback to development.

Project management & coordination

Project managers steer instead of chasing people.

Today: Minutes, status reports and task lists cost project managers many hours every week – and follow-up actions still get lost.
With AI: AI agents write minutes and status reports, derive tasks, remind owners and trigger follow-up actions on their own.
Value: More time for decisions and customers, less friction between departments.

Sales & quotation

Faster, better-founded quotes for technical inquiries.

Today: Technical customer inquiries have to be read, matched against past projects and turned into a quote – often by scarce engineering resources.
With AI: AI analyzes inquiries and specifications, finds comparable projects and prepares draft quotes including open questions.
Value: Shorter response times, less load on engineers and a higher quote rate.

Purchasing & data transfer between systems

No more copy & paste between datasheets, ERP and Excel.

Today: Datasheets and supplier quotes are compared manually, and data is transferred between systems by hand.
With AI: AI extracts technical data from datasheets and quotes, prepares comparisons and transfers data between systems in a structured way.
Value: Fewer transfer errors, faster purchasing decisions and relieved departments.

Knowledge management & internal administration

Technical knowledge becomes usable for everyone – not just experts.

Today: Knowledge sits in people’s heads, emails and thousands of documents. New employees take a long time to find their way around.
With AI: An AI assistant answers questions based on your own documents – with sources – and takes over recurring administrative tasks.
Value: Faster onboarding, fewer questions to key people, and knowledge that stays in the company.

How do you introduce AI agents in a company?

In five stages: identify AI potential, redesign processes with AI, use AI agents for specific tasks, build agentic workflows and hand over areas of work to AI agents. I support you from wherever you are today.

1
Identify AI potential
We analyze your existing processes and identify tasks where AI delivers concrete business value – e.g. in engineering, project management, documentation, sales, purchasing or administration.
2
Redesign processes with AI
Existing workflows are restructured so that AI can be integrated meaningfully – not as an add-on tool, but rethought from the ground up.
3
Use AI agents for specific tasks
AI agents handle defined tasks largely on their own: analyzing information, creating documents, transferring data between systems, preparing decisions, triggering follow-up actions.
4
Build agentic workflows
Several specialized AI agents work together and take over complete process chains. People step in where decisions, responsibility or expertise are needed.
5
Hand over areas of work to AI agents
Entire areas of work are run by AI agents – people increasingly take on a steering and supervising role.

Who is this for?

Technical SMEs with complex products and processes.

Mechanical engineering and technical product development
Automotive and supplier industry
Electrical engineering and electronics development
Energy technology
Electrical installation and technical planning
Photovoltaics and solar technology
Battery and energy storage systems
Engineering and development services

My goal

Not to introduce as many AI tools as possible.

But to create an organization where people and AI work together meaningfully – and AI takes over operational work wherever it creates real value.

From AI-assisted processes to AI agents in day-to-day operations

Explore OSOS/Omega

Integrate AI into your processes – where it really pays off

Dr. Hai Van Le Jorks

My approach: I run an AI-native company myself

Engineering expertise meets AI expertise

You can start small.

Typical questions companies bring to me

“Where does AI actually pay off in our company?”

“Which of our processes can we already automate today?”

“How can AI agents take load off our engineers and project managers?”

“How do we integrate AI into existing systems and processes?”

“Which tasks can AI agents handle on their own?”

“How can we start with a small pilot project instead of a large AI program?”

“What could our company look like if AI takes over a large part of the operational work in the future?”

“What does the initial call cost?”

“Do you consult on site or remotely?”