CAD automation implementation for engineering teams.

High-stakes modernization, CAD-hosted workflow tooling, desktop application renewal, Autodesk, MicroStation, SolidWorks, and integration work where architecture and implementation need to stay in the same hands.

Yes. Most engagements start inside an existing codebase. I work with your branching strategy, CI pipeline, and review process — not around them.

Absolutely. Incremental modernization is the default posture. The goal is bounded wins that keep production running, not a high-risk rip-and-replace.

Yes. Most projects begin with a scoped discovery or bounded first sprint. Each phase has its own deliverables and acceptance criteria. You are never locked into the next phase.

That is the core value proposition. Architecture-led delivery means the person designing the system is the same person building it — no hand-off gap.

Weekly check-ins, a shared project tracker, and demo cadence keep stakeholders aligned. At close-out, you receive documentation, clean boundaries, and a handoff package your team can extend.

Yes. AutoCAD, Inventor, and Vault are core strengths, and SolidWorks and MicroStation are also in scope when the engagement is software automation, plugin modernization, data extraction, BOM/model workflow, PDM readiness, host migration, or .NET integration rather than drafting-only support.

CAD Guardian scopes security and privacy before implementation: least-privilege access, MFA/RBAC where available, no secrets in code, sanitized proof, dependency awareness, input validation, audit logs, rollback planning, and clear rules for whether AI tools may touch client material.

Yes. Public case studies and proof blocks use sanitized patterns only. Raw CAD files, drawings, title blocks, customer records, order data, part numbers, server paths, connection strings, screenshots, and proprietary logic stay out of public content and are handled only inside the agreed engagement boundary.

AI is an accelerator, not the owner. CAD Guardian uses best-fit cloud and local model workflows where they help with research, code scaffolding, test generation, documentation, media or artifact preparation, and workflow reasoning. Architecture, data access, validation, acceptance criteria, and handoff remain human-led.

Useful intelligence needs clean source data, retrieval context, permissions, tool access, logs, evaluation checks, and outputs people can inspect. The model is only one part of the system.

Remote-first by default. All delivery is managed through shared project tooling with documented cadence. On-site visits can be arranged for kickoffs or critical milestones when justified.

That is normal. The paid discovery call exists specifically to turn a rough idea into a clear go/no-go recommendation with enough structure to move forward or walk away confidently.