Martenweave Core · local-first model governance

Turn one local data file into a governed next step.

Transformation knowledge is scattered across files, tickets, reports, and people. Martenweave profiles one local file, makes readiness findings and evidence inspectable, then routes a reviewable change for human approval.

Install from PyPI See the workflow

Apache 2.0 open source Core 0.9.0 on PyPI local-first workspace canonical files stay local

Abstract map of model relationships, lineage routes, and validation signals
A governed model makes relationships inspectable before change reaches delivery.
Source evidenceCanonical modelReviewed change

Canonical filesare the source of truth.

Derived indexesare rebuildable.

AI proposes.It does not silently mutate.

First value · local only

One file produces findings you can inspect before you change anything.

Use a local CSV, XLSX, XML, or JSON file. The generated workspace records its preflight, profile, readiness report, evidence, and any optional proposal without silently changing canonical model files.

$ python -m pip install martenweave-core
$ martenweave start ./customers.xlsx
format: xlsx
readiness: blocked
findings: unmapped columns, ownership gaps,
          transformation risks
report: generated/readiness-report.html
workbench: http://127.0.0.1:8000

Run the PyPI flow →Inspect release evidence →

Synthetic example · local static viewer

Start with a model you can inspect, not a dashboard you have to trust.

The checked-in Customer Business Partner example is indexed locally into a disposable, read-only viewer. The screen shows 89 indexed canonical objects from the checked-in synthetic example; the model files remain the source of truth.

Explore the example

Synthetic Customer Business Partner model overview showing 89 indexed canonical objects and a fresh local index
Generated from a synthetic Customer Business Partner model. No customer data is shown.

Product

One connected layer between delivery artefacts and controlled change.

01

Model the meaning

Represent business attributes, SAP contexts, field endpoints, mappings, value lists, issues, decisions, and ownership as traceable objects.

02

Verify before publishing

Validate object shape, references, and domain context deterministically before the index and reports are rebuilt.

03

Investigate relationships

Trace lineage, identify dataset gaps, and assess downstream impact from the same canonical model.

04

Control the change

Convert analysis into a reviewable proposal and an approved change request with an explicit audit trail.

Workflow

A visible path from file to approval.

Each stage answers a delivery question without pretending the workspace is a hosted platform or a chatbot.

  1. SelectStart with one local CSV, XLSX, XML, or JSON file.
  2. PreflightDetect the format and record a safe workspace manifest before analysis.
  3. ProfileExpose the shape and evidence of the source without treating it as canonical truth.
  4. ValidateReadiness findings make gaps, ownership, and transformation risks explicit.
  5. ReviewInspect evidence and the report locally; optional AI proposals still require human approval.

Use cases

Built for the moments when model knowledge becomes operational risk.

SAP migrationConnect source datasets, mappings, target fields, and readiness evidence before testing exposes the mismatch.MDM and MDGPreserve the implementation knowledge around governed operational master data.AMS and changeGive support teams a defensible starting point for investigating and reviewing a model change.

Pilot projects

Start with one representative model slice.

A focused pilot establishes the evidence, canonical structure, validation, and review path around a real migration, MDM, governance, or AMS question.

Explore pilot projects

Consulting

Use Core with an accountable delivery method.

Engagements focus on the model and evidence that need to survive a handover—not on inventing a parallel operating platform.

How consulting works

Where it fits

A supporting layer for the systems you already run.

Martenweave keeps model evidence inspectable around delivery systems. It does not replace their transactions, configuration, workflow, or source of record.

01SAP ECC & S/4HANA

Connect legacy columns, SAP field endpoints, contexts, mappings, and migration evidence before testing.

02SAP MDG & MDM

Keep field meaning, ownership, value rules, and implementation decisions reviewable alongside governed data.

03ERP, CRM & warehouse

Use shared business attributes and lineage to make cross-system definitions and downstream impact explicit.

04Jira, Confluence & delivery files

Turn tickets, documents, workbooks, and incident findings into durable evidence without replacing the tools.

AI workflow

Give AI structured context—then keep the decision under control.

Instead of asking an agent to reconstruct meaning from a chat or a spreadsheet, give it the canonical model, evidence, and boundaries it needs to prepare a focused next step.

  1. 01
    Ground the request

    Use canonical objects, references, source evidence, and known context as the working frame.

  2. 02
    Draft a proposal

    AI can prepare a reviewable PatchProposal from a note, finding, or design question.

  3. 03
    Validate deterministically

    Rules check object shape, references, and supported SAP context before anything is indexed.

  4. 04
    Approve with a human

    Reviewers decide whether a proposal becomes canonical truth and an auditable change.

Product FAQ

Questions teams ask before they put model knowledge under control.

Short answers for migration, MDM, governance, and AI-assisted delivery teams. For detailed product boundaries, see the full FAQ.

What is Martenweave?

Martenweave is an open-source model governance and evidence layer. It turns model knowledge from spreadsheets, datasets, tickets, and SAP context into canonical files that can be validated, traced, reviewed, and exported.

Which systems and projects does Martenweave support?

Martenweave is an open-source core with a tested SAP domain pack and synthetic example models for SAP ECC to S/4HANA migration, MDM/MDG delivery, data governance, and AMS scenarios. It ingests evidence from Jira and Confluence exports, mapping workbooks, and delivery files as inputs to review. It is not certified by SAP or affiliated with SAP, and it does not replace SAP MDG, Migration Cockpit, Syniti, SNP, Jira, or Confluence.

Does Martenweave replace SAP MDG, S/4HANA, Jira, or Confluence?

No. Martenweave is a supporting model-governance layer. It does not replace system transactions, configuration, workflow, source-of-record tools, or direct SAP write-back.

How does Martenweave help SAP migration and MDM teams?

Teams can connect business attributes, source columns, SAP field endpoints, mappings, value rules, ownership, lineage, validation findings, and change proposals in one reviewable model.

How does the AI workflow work?

AI works from canonical objects, references, and source evidence to draft a reviewable PatchProposal. Deterministic validators then check the proposal, and a human decides whether it becomes canonical truth.

Can AI change the model automatically?

No. AI can prepare proposals and explain structured context, but it does not silently mutate canonical model files. Human approval and an auditable change path remain required.

Capacity scenario

Make the cost of hidden rework visible.

Use your own team assumptions to estimate the annual capacity that clearer model evidence and controlled change could recover.

Illustrative annual capacity recovered

€109,4408 people × 3 hours × €95 × 48 working weeksAn editable planning scenario, not a savings guarantee.
Discuss a real baseline