About
EAM is my deepest specialist domain, but it does not define the limits of my work. Interface engineering and applied AI/ML are separate capabilities, delivered with the same practical focus.
My background
We focused on standard EAM implementations, especially Start Centres with inboxes, KPIs and FlexSQL. I spent much of my time clarifying processes and speaking with customers; my staff carried out the implementation and also handled smaller project topics directly. EF played a smaller part in our work at the time.
I clarify the customer’s process, implement the agreed improvement and take responsibility for technical review, testing and documentation. AI supports suitable, clearly specified routine work; I direct its use and check the result.
The boutique builds on experience of leading an EAM delivery team. The model changes; process understanding, product knowledge and responsibility for a usable result remain central.
ERP & interface engineering
Practical support for Infor COM ERP, IGF/Varial and EDI, from screens and repository logic to SQL, reports and administration with IMC. A defined ERP or interface issue can be commissioned independently of EAM.
Expertise
Within EAM, I bring deep last-mile expertise around HxGN EAM / Octave Attune EAM, SQL, FlexSQL, reports and work-order logic. Independently of EAM, I build and stabilise interfaces and develop focused AI/ML and automation solutions around workflows, documents, sensor readings and images.
Deep product and maintenance-process expertise around HxGN EAM / Octave Attune EAM, work-order logic, SQL, FlexSQL, JavaScript, reports, custom rules and last-mile improvements.
New integrations and reliable data movement across ERP, CRM, MES, EAM, SaaS, files, APIs and reporting systems, with clear ownership. EAM can be involved, but it is not a prerequisite.
Computer vision, sensor and time-series ML, knowledge retrieval and document intelligence—built around defined use cases, representative data, measurable evaluation and human guardrails.
Working approach
My delivery standard comes from environments where teams depend on systems that already exist, data has operational meaning and a technically successful result can still fail in daily use.
The work can start inside EAM, between unrelated business systems or with an AI/ML use case. In every case, I stay close to the real records, documents, decisions, exceptions and owners.
That is why the focus is practical: define the useful outcome, test representative and difficult cases, document limitations and ownership, and make the next delivery decision explicit.
Experience and examples
These examples show working methods and project scope. Demonstrations and self-initiated prototypes are identified as such; they are not presented as client results.
Published EAM demonstration
A recorded example of AI carrying out EAM configuration through the browser under expert direction. Customer access, review and release controls are agreed for each engagement.
Watch the EAM inbox demoSelf-initiated ML projects
Sensor time-series and computer-vision prototypes, both still active in adapted forms. The project descriptions explain the data, model tasks and limits of the results.
Explore the two ML prototypesPublished workflow demonstration
An Excel task-pane agent that works directly in the workbook. The demo shows the workflow; ongoing development explores how it can support practical spreadsheet work.
Watch the Excel agent demoDelivery structure
Lukas Paul Streiff is the personal specialist brand. Broader delivery or implementation arrangements can be handled through Tiravera SL.
A useful first conversation can begin with an EAM bottleneck, an interface record, an AI/ML opportunity, a data sample, a document set or a manual workflow.