No fake client logos. No inflated numbers. These stories focus on real challenges Tigient can solve in CAD, PLM, AI, data and workflow environments.
Challenge: Manual CAD reviews slowed product development and created inconsistent feedback.
Approach: Designed an AI-assisted review workflow using feature extraction, engineering rules and human approval.
Outcome: Faster review turnaround, clearer decisions and reduced repetitive engineering effort.
Challenge: Revisions, comments and decisions were spread across disconnected systems.
Approach: Created a unified workflow connecting CAD data, PLM metadata and review history.
Outcome: Better traceability, improved collaboration and faster design release conversations.
Challenge: Teams repeatedly solved similar design issues without reusing previous decisions.
Approach: Captured approvals, rejections and comments into a reusable knowledge layer.
Outcome: Better design consistency, faster onboarding and reduced knowledge loss.
Challenge: AI recommendations alone were not reliable enough for engineering release decisions.
Approach: Added approve/reject feedback, reviewer comments and decision history into the workflow.
Outcome: AI became more useful because every decision improved future context.
Challenge: Engineering rules were difficult to manage because exceptions depended on part type and use case.
Approach: Structured rules as decision trees connected to feature context and review outcomes.
Outcome: Rules became easier to audit, explain and evolve over time.
Challenge: Packaging designs had to be recreated whenever product dimensions changed.
Approach: Created an on-demand packaging workflow driven by product dimensions and constraints.
Outcome: Faster packaging iterations and less manual redesign work.