Keep every change connected.

Technology in the context of your operation

Connect designs, parts, production and quality records so teams can see what a change affects. We connect the people, records and systems involved in the work, with a clear owner for every important decision.

• Discrete Manufacturing (General)
• Process Manufacturing (Chemicals / FMCG)
• Textile & Apparel Manufacturing
• Automotive Manufacturing (OEM & Parts)
• Aerospace & Defense Manufacturing
• Pharmaceutical Manufacturing
• Electronics Manufacturing
• Furniture & Home Goods Manufacturing
• Jewelry & Luxury Goods Manufacturing

Engineering changes reach purchasing after parts are ordered

An approved design revision does not propagate to bills of materials, purchase orders and work instructions.

The solution: A change-impact workspace linking approved revisions to affected material and production records.

Where intelligence helps: Deterministic revision rules identify conflicts; document comparison summarises differences for engineers.

Operating responsibility: AI never approves a replacement part or releases a revised instruction. Changes require the authorised reviewer.

What to measure: Obsolete-version usage, review time and missed affected orders.

A quality issue triggers too wide—or too narrow—a recall review

Teams cannot quickly identify which assemblies used a questionable batch.

The solution: Component genealogy and containment-review tooling linking receipts, batches, assemblies and shipments.

Where intelligence helps: Graph queries find known relationships; extraction helps recover missing legacy records for review.

Operating responsibility: Quality staff decide quarantine, release or recall. Missing traceability is displayed, not filled with an invented relationship.

What to measure: Trace completeness, time to identify affected stock and erroneous associations.

Build around a real workflow

Start with one operational journey and representative records. Establish how the work is handled today, where responsibility changes and what evidence is missing. A pilot should prove data quality, integration behavior and user adoption before expanding to additional teams or locations.

Common questions