Forward-deployed engineers and AI agents for a multinational EU client's PLM version migration across integrated CAD providers. Validating compatibility across AutoCAD, Catia, and Solidworks. Seven-day consultant back-and-forth reduced to instantaneous.
PLM version upgrades across integrated CAD environments are not trivial. When a PLM system connects to Autodesk Inventor, Vault, AutoCAD, Catia, and Solidworks, the upgrade path requires validating schema compatibility, custom field mappings, API contracts, stored procedures, ETL pipelines, and provider-specific integration patterns. Client-facing compatibility assessment and discovery: 18 to 24 business days.
The real cost isn't just time. It's the 7-day back-and-forth between consultant question, data pull, compatibility analysis, and response. Manual search across GitHub, SQL exports, API specifications, SharePoint documentation, and integration notes. Review quality dependent on individual consultant effort. Every provider question: another cycle.
Forward-deployed engineers embedded directly into a multinational EU client's PLM version upgrade workflow. Not as external consultants building a tool and leaving—as embedded engineers living inside the operation, owning the compatibility outcomes. AutoCAD, Catia, Solidworks integrations. Different CAD providers, same PLM upgrade challenge.
The team built custom GitHub MCP connectors to pull API contracts and integration code. Custom CAD provider API specifications, GraphQL queries, Power Fx expressions to enable multi-provider compatibility checks. SQL schema extraction jobs that pull stored procedures, field mappings, ETL logic, and metadata into structured JSON on SharePoint.
Not a pre-packaged integration. Forward-deployed engineering: custom connector code for each CAD provider, response normalization logic, compatibility matrix generation, SharePoint JSON staging, Parse JSON flows, Copilot topic orchestration, Word document generation—all built for each client's exact integration stack. That 7-day cycle? Instantaneous.
AI Enablement Engineers made sure the agents actually got used. They optimized Copilot topics for PLM Architect and Integration Consultant workflows. They added business-specific system prompts to keep responses focused on PLM version compatibility, CAD provider integration validation, and upgrade risk assessment. They built confidence routing so high-quality results auto-applied and low-confidence outputs went to human review.
The result: PLM Architects can ask for a comprehensive upgrade compatibility summary, then follow up with questions about specific API contracts, field mapping compatibility, ETL pipeline changes, or provider-specific risks. Integration teams can inspect API specifications, trigger points, and integration logic using responses grounded in provider documentation and extracted compatibility matrices.
The agent can package output into a Word document for architect review, deployment handoff, upgrade planning, or technical risk assessment. Generated documents are saved to OneDrive or SharePoint and followed by email notification—creating reusable project artifacts, not disposable chat sessions.
Data Sources: GitHub repositories (integration code, API specifications, provider SDKs), PLM database (stored procedures, field mappings, ETL logic), CAD provider API documentation (AutoCAD, Catia, Solidworks contracts), SharePoint (staged compatibility matrices), OneDrive (generated assessment documents).
Core Components: Custom GitHub MCP connector for provider SDK analysis, multi-provider API query layer using GraphQL and Power Fx, PLM schema extraction jobs, compatibility matrix generation, SharePoint JSON staging and Parse JSON flows, Copilot Studio topic orchestration, Word document generation via Work IQ MCP.
Scale: Vector search layer using Azure AI Search. Custom vectorization models for API contract similarity. Containerized deployment. Supports 50+ integration repositories, 25,000+ field/API mappings, 5 GB+ indexed provider documentation per upgrade project.
Integration: Linked to Jira. Auto-reads tickets, comments, provider integration issues, project metadata. Real-time context from active integration work feeds directly into upgrade compatibility assessments.
The core outcome: 7-day back-and-forth eliminated. Question to answer, instantaneous. Across all integrated CAD provider compatibility checks.
Pilot measurements across 12 PLM upgrade assessments showed task-time reduction ranging from 34% to 78% based on environment size, provider integration complexity, field mapping volume, and API contract changes. Largest gains in compatibility search, integration risk review, cross-provider impact analysis, and document preparation. Standard topics, controlled prompts, structured connector logic reduced variation between upgrade projects.
Forward-deployed ownership: Engineers didn't build a tool and leave. They embedded in the PLM upgrade workflow, owned the provider integration connector logic, managed the SharePoint compatibility matrices, monitored the Copilot topics, and iterated based on architect feedback.
AI enablement for adoption: AI Enablement Engineers optimized the agent for how PLM Architects and Integration teams actually work. They refined system prompts for compatibility assessment, improved topic routing for cross-provider queries, added confidence thresholds for breaking changes, and ensured generated documents matched technical handoff requirements.
Outcomes-based iteration: The team measured architect task-time reduction, compatibility validation success rate, context overflow rate, human rework required, and team satisfaction. They iterated on the components that moved those numbers.
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