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AI IN PRACTICE / COMPLIANCE & REPORTS

Compliance & reports

Connect evidence. Make gaps visible.

Draft inspection and acceptance reports from verified evidence Prepare a compliance report with a traceable evidence chain

Illustrative process scenarios — not customer references. Feasibility, benefits and approvals need to be established for the specific organisation.

05

ILLUSTRATIVE SCENARIO

Draft inspection and acceptance reports from verified evidence

Measurements, test records and outstanding items are mapped to the agreed acceptance scope and assembled into a reviewable draft.

UNDERSTAND THE AI

What happens behind the scenes.

TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN

Document AI · RAG · Structured report generation

What does the AI actually do?

AI maps differently worded evidence to acceptance-plan items and drafts readable report text. Measurements are taken from original records and assessed with validated rules.

How the solution works

  1. 01
    Read the evidence

    Document models extract test item, value, unit, serial number and revision from protocols and tables, preserving page and table references.

  2. 02
    Match requirements

    Semantic retrieval proposes evidence for each item. Fixed checks validate identity, completeness and approved limits; missing or contradictory results remain open.

  3. 03
    Assemble the report

    An LLM writes within an approved report template. Tables and test results come from validated data, accompanied by sources and outstanding items.

What value can it deliver?

Less searching, matching and formatting effort; earlier visibility of missing evidence; faster resolution of review questions through source links. Reduced revision cycles are a measurable objective.

A concrete example

Illustration: a pressure-test record matches a FAT item, but the serial number differs. The item remains unresolved and is not reported as passed.

Technology, setup & operation

A pretrained document model, search index and template engine can work together. Limit checks remain reproducible software logic. Evaluate extraction, evidence matching and faithfulness of generated text separately.

Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.

Starting situation

A machine builder is preparing a factory acceptance test. Measurement records, calibration evidence and functional tests sit in different folders. The customer expects a report linking each agreed acceptance item to its evidence.

How the workflow could look

  1. Structure the scope

    The assistant breaks the approved acceptance plan into individual requirements. Project management and quality confirm this baseline.

  2. Map evidence

    It links each item to test records, values and document revisions. Missing signatures, mismatched serial numbers and unmet criteria are flagged.

  3. Prepare the report

    The draft contains evidenced results, source references and a punch list. Unchecked or missing results remain clearly identified.

Illustrative work product

An inspection or acceptance report in the agreed format, plus an evidence matrix and outstanding-item list for FAT or SAT.

06

ILLUSTRATIVE SCENARIO

Prepare a compliance report with a traceable evidence chain

An approved requirements catalogue is matched with product, material and supplier evidence. Missing or mismatched records are routed for clarification.

UNDERSTAND THE AI

What happens behind the scenes.

TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN

Semantic matching · RAG · Evidence matrix

What does the AI actually do?

AI breaks an expert-approved catalogue into reviewable statements and proposes supporting evidence, even when requirements and records use different terminology.

How the solution works

  1. 01
    Structure requirements

    An LLM proposes individual requirements, scope and evidence types. The responsible function confirms the structure as the working baseline.

  2. 02
    Find candidate evidence

    A search index represents document passages by meaning. Retrieval proposes candidates; metadata filters check product revision, date and release status.

  3. 03
    Show coverage and gaps

    Each requirement gains a source-linked match and rationale. Expert review determines coverage; missing evidence is recorded as a gap.

What value can it deliver?

Less search and compilation effort for recurring evidence tasks. A shared matrix exposes gaps and outdated records earlier and supports review preparation.

A concrete example

Illustration: a requirement asks for material evidence. AI finds a related certificate but flags it because the component revision differs.

Technology, setup & operation

Vector representations, called embeddings, support semantic retrieval. Similarity alone does not establish compliance. Validity, authoritative interpretation and approval remain expert responsibilities; the language model does not certify conformity.

Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.

Starting situation

An industrial supplier must provide a customer-specific compliance report for an assembly. Material declarations, supplier certificates and technical records refer to different part revisions. Having documents on file does not establish that the right evidence is available.

How the workflow could look

  1. Confirm requirements

    Compliance owners define the applicable catalogue, scope and evidence criteria. This baseline is version-controlled.

  2. Check relevance

    The assistant compares part references, revisions, issuers and validity. A document is proposed as evidence only when its relationship is traceable.

  3. Consolidate gaps and reporting

    A draft shows the evidence and review status for each requirement. Missing records are grouped with an owner and clarification request.

Illustrative work product

A versioned compliance matrix and report draft with evidenced, open or expert-review-required status.

CONTINUE EXPLORING

More processes.
More perspectives.

ProductionQuality managementComplaints managementLogisticsSupply chainService & self-service
All ten scenarios

FROM SCENARIO TO A PRACTICAL NEXT STEP

Which process deserves
a closer look?

Together, we can clarify the current process, available evidence and a useful pilot. My contribution: structure, priorities and alignment between business teams, IT and implementation partners.

Discuss a use case