Technical documents, practical experience and master data underpin many business decisions. AI needs clearly defined access to this information, with context, quality and accountable ownership.
Do your people find the right information — or simply a large number of documents?
USE CASE EXAMPLES
How AI could help in everyday work.
Illustrative scenarios. Requirements and value are assessed for your specific process.
01
ENTERPRISE SEARCH & AI
Find technical knowledge across repositories
THE STARTING POINT
Relevant knowledge is spread across document management systems, wikis and project folders. Similar terms and inconsistent naming make searching harder.
A POSSIBLE APPROACH
Enterprise search and AI-assisted answers help users find approved information in the context of a question. Source references make the answer verifiable.
WHAT YOU NEED
Agreed sources, usable documents, metadata, up-to-date access rights and a process for maintaining the knowledge base.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
Enterprise search · RAG · Embeddings
What does the AI actually do?
AI interprets a domain question, retrieves relevant material across repositories and drafts a source-linked answer.
How the solution works
01
Index sources
Index passages with source, revision and permissions.
02
Search by meaning
Embeddings represent content as numeric vectors; keyword and vector retrieval identify candidate passages.
03
Compose an answer
An LLM uses retrieved evidence and links sources; unsupported questions remain unresolved.
What value can it deliver?
Less cross-system searching and easier access to distributed technical knowledge.
A concrete example
A question about a seal for an older variant finds a valid technical description linked to that variant.
Technology, setup & operation+
RAG supplies knowledge at query time. A new knowledge base does not inherently require model training; source maintenance, permission filters and real-question evaluation remain essential.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Connect one user group with selected knowledge sources and a test set drawn from real questions.
HOW TO ASSESS VALUE
Evaluate search time, coverage of typical questions, source quality and correct enforcement of access rights in the pilot.
Search must respect existing access rights. Unsupported answers and gaps in knowledge are clearly identified.
02
DOCUMENT INTELLIGENCE
Classify documents and extract information
THE STARTING POINT
Technical documents and forms contain valuable information that must be transferred manually or located across different document types.
A POSSIBLE APPROACH
AI-based methods can sort documents and suggest relevant information for review. Ambiguous results are routed to a defined exception-handling process.
WHAT YOU NEED
Representative document samples, defined target fields, agreed data formats and examples reviewed by domain specialists.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
Document intelligence · OCR · Field extraction
What does the AI actually do?
AI classifies documents and maps content from varying layouts to defined target fields.
How the solution works
01
Read the document
OCR and layout analysis extract text, tables and relationships.
02
Identify type and fields
Classification and extraction models propose the document class and required information.
03
Validate results
Check data types, required fields and references; route uncertain fields to a correction interface.
What value can it deliver?
Less rekeying and faster availability of reusable document data for recurring processes.
A concrete example
A material certificate yields proposed material, batch and test-date fields linked to their original locations.
Technology, setup & operation+
Pretrained models can start a pilot. Specialised classes or layouts may need training examples. Field-level accuracy and correction effort determine viability.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Begin with one well-defined document type and a small number of business-relevant fields.
HOW TO ASSESS VALUE
Measure field-level accuracy, the proportion requiring correction and processing time for each document type.
Business-critical information is confirmed using agreed review rules. Difficult cases remain traceable.
03
DATA QUALITY
Highlight master data issues
THE STARTING POINT
Inconsistent naming, missing attributes and potential duplicates make procurement, reporting and automated workflows more difficult.
A POSSIBLE APPROACH
Rule-based checks and supporting AI methods flag unusual records and suggest possible matches. Data owners decide which corrections to make.
WHAT YOU NEED
A defined master data set, business quality rules, identified systems of record and named data owners.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
Entity matching · Similarity model · Quality rules
What does the AI actually do?
AI identifies potentially duplicate parts or suppliers despite different naming and prioritises record pairs for review.
How the solution works
01
Normalise fields
Standardise units and naming with fixed quality rules.
02
Compare candidates
Text similarity, embeddings or a trained pair model score potential matches.
03
Prepare review
Show matching and conflicting attributes for a data-owner decision.
What value can it deliver?
Smaller review queues and more consistent master data can reduce downstream purchasing and reporting errors.
A concrete example
A stainless-steel screw and an abbreviated description become a review pair; conflicting material grades remain visible.
Technology, setup & operation+
Text similarity does not prove identity. Use expert-labelled record pairs and measure both incorrect merges and missed duplicates.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Review one material group or supplier master data set against a few agreed quality criteria.
HOW TO ASSESS VALUE
Compare completeness, suspected duplicates and manual review effort before and after the exercise.
A suggested merge is reviewed by a data owner before any change is made to the system of record.
MY CONTRIBUTION
Work out together what is worth pursuing.
I help you turn an interesting use case into a clear, assessable business task.
Structure needs and objectives with business functions and IT
Clarify value, requirements and open questions
Prepare a basis for decisions and define a possible pilot scope
Support alignment with suitable implementation partners
FREQUENTLY ASKED QUESTIONS
Before taking the first step.
Do all data need to be perfect first? +
A limited pilot can start with a curated, suitable data set. Gaps that affect its value should be visible from the beginning. This provides a basis for a focused improvement plan.
Is AI search the same as a new database? +
No. Search can make existing sources accessible. What matters is which information can be accessed, how it stays current and whether permissions and versions are reliably respected.
FROM A USE CASE TO THE NEXT STEP
Which idea would you like to make concrete?
Together, we clarify the starting point, possible use cases and expected value, creating a sound basis for your next decisions.