One engineering thread, from concept to installed base
Engineering value is created once, at design, then eroded at every handoff: design to product lifecycle management (PLM), PLM to enterprise resource planning (ERP) and manufacturing execution systems (MES), plan to shop floor, shop floor to service. Late changes, downtime, and missed launches can often trace back to these breaks. We help engineer the thread that can close them, so R&D, manufacturing, and aftermarket can work as one.
We pair operating-model rigor with hands-on engineering, working inside the same platforms and governed data your own teams use.
PLM that keeps requirements, design models, and engineering change connected, so design decisions stay legible in service.
MES that turns the engineering bill of materials into routings and work instructions, helping engineering changes reach the line consistently.
Converge information and operational technology, so instructions flow down and as-built data flows back on one record.
Legacy PLM, computer-aided design, and engineering data moved to the target platform, with an approach designed to preserve history and revisions.
AI copilots, human-governed agents, and digital twins inside the engineer's toolset, built on governed data.
Service parts, maintenance and repair, dealer, diagnostic, and warranty data fed back into design, helping support new service revenue opportunities.
Start with the operating model, not the catalog
Many engineering programs get into trouble long before go-live, in the assumptions nobody wrote down. So we start with the operating model. We map the process and the people, assess what data the machine and application estate can realistically expose, and pressure-test integration against the ERP and PLM systems already in place. You get an evidence-based fit-gap picture and a data foundation to build on. Product selection comes later, and by then, the requirements are yours.
Roll out from one template, not one site at a time
Rollout is template-led, not reinvented at every location. One golden solution covers core requirements across sites, with lean, governed per-site configuration and a fit-gap discipline designed to keep deviations priced, visible, and deliberate. The same principle holds for a PLM backbone across engineering centers or an execution layer across plants: standardize what should be common, and make every exception earn its place.
Match AI to the job, then govern it
We work across the full enterprise-to-plant stack, including leading off-the-shelf and custom PLM, ERP, and MES platforms. Where AI adds value, we choose the right layer: AI built into design tools, analytics that turn plant and field data into decisions, generative AI that drafts specifications and documentation, and agents that run routine engineering and maintenance workflows under human governance. Engineers stay accountable, and the data foundation comes first.
Plan for adoption, not just deployment
The adoption curve, not the software license, decides whether the value lands. Engineers who don't trust a new system will quietly keep the side spreadsheet, and you end up with two versions of the truth. So change management runs alongside the build: role-based training, engineering and shop-floor champions, and a sustenance model designed to keep the estate supported long after the program team moves on.
Our delivery approach
Assess: Map the process, assess the machine and application estate, and pressure-test integration. Run a product-agnostic evaluation and set the fit-gap baseline
Design and prove: Build the golden template, define integration between PLM, ERP, and the plant, and stand up the data foundation the thread depends on
Roll out and run: Deploy site by site, sustain the estate, and add governed AI once the data is trustworthy. Track progress against delivery, launch, and quality goals, not just go-live
We pair operating-model rigor with hands-on engineering for manufacturers in industrial equipment, automotive, aerospace and defense, electronics and high tech, chemicals and advanced materials, energy, and mining and metals, working inside the platforms and governed data your teams already use.