Service

Factory data validation

Validate files, fields, dates, warranty codes, serial ranges and enrichment dependencies before the data reaches downstream systems.

What we solve

Industrial-strength checks before bad data reaches teams.

01 · we handle

Factory-specific validation profiles

result

Fewer support escalations

02 · we handle

Audit trails for exceptions and corrections

result

Higher trust in PIM data

03 · we handle

Data-quality reports for operational owners

result

Clearer ownership of errors

Deliverables

  • Validation matrix
  • Exception dashboard
  • Automated notifications
  • Data-quality reporting

Outcomes

  • Fewer support escalations
  • Higher trust in PIM data
  • Clearer ownership of errors

Common integrations

Factory filesCSV · XLSX · XML
PIM APIsproduct · warranty · media
ERP dataorders · serials · production
Service workflowsclaims · lookup · notifications

What you get

Four things, in one engagement.

01

Validation matrix

Every field, format, range and dependency written down per factory and product line.

02

Exception routing

Failures route to named owners with row-level context, not to a generic shared inbox.

03

Audit trail

Each correction and override is logged, so quality issues stay traceable to their source.

04

Quality reporting

Recurring data-quality summaries show which factories improve and which need attention.

Runs onRule engineCSV / XLSX parsersAzure FunctionsEmail reportsAudit logDashboards

Where it sits

From factory export to service-ready record.

Every engagement plugs into the same governed pipeline. This service strengthens its part of the flow without breaking the rest.

Factory filesCSV · XLSX · API
Validaterules per factory
Enrichparts · manuals
PublishPIM · ERP API
Service-readylookup · alerts

Onix x Duscholux

Duscholux warranty data automation

Automated factory warranty and serial number data from four European factories into a PIM-ready operational pipeline.

4factory flows
0manual import steps
14days after production
40%estimated time reduction

Very precise and well documented. Documents were ready to be turned into code. AI support in development is the part that matters. An in-depth process design is key to achieve it.

Client-side project review, Duscholux
Azure Logic AppsAzure App ServiceNext.js admin panelPIM APIEmail notificationsSharePoint / OneDrive

How we deliver

A delivery process designed for messy operational data.

Diagnosis first, scoped delivery second, measurable operation third. That order keeps automation precise instead of decorative.

01

Process triage

Identify systems, owners, file flows, exceptions and the business cost of manual work.

02

Operating spec

Define data contracts, validation rules, enrichment logic, roles and acceptance criteria.

03

Automation build

Implement the workflow layer, integrations, admin panel and notifications.

04

Real-data validation

Test with actual factory exports, edge cases and stakeholder reviews.

05

Operate and expand

Monitor results, improve quality and scale the pattern to more factories and processes.

Service FAQ

Asked on the first call.

More general questions live in the full FAQ.

Yes, that is the default. Factories have different systems and constraints; the matrix captures each one, and the downstream model stays identical for everyone.

The factory or owner that produced it, guided by a row-level error report. The layer never silently corrects records: silent fixes hide problems instead of solving them.

Project triage

Bring the messy process. We will map the path to automation.

Share the workflow, systems and data sources. We turn it into a clear next step: rescue, integration, admin panel, or a fuller automation build.

Reply within 1 business dayNDA-friendly reviewFixed scope before code

Typical first audit

free · senior-led
  • System and file-flow map with named owners
  • Automation opportunity score for each manual step
  • Delivery plan, risk list and a fixed-scope next step
Start project triage