The manufacturing landscape has long suffered from a siloed approach where design drawings created in CAD systems and the operational data generated on the shop floor reside in separate ecosystems. This disconnect forces engineers to re‑enter object information, bill of materials, and geometric details repeatedly, inflating labor hours and introducing transcription errors that can cascade into costly rework. As AI‑driven manufacturing expands, the ability to preserve design intent while feeding it directly into production analytics becomes a decisive competitive factor. Companies that can close this loop gain faster iteration cycles, higher first‑pass yield, and clearer traceability from concept to shipment. The pressure to integrate these domains is no longer a nice‑to‑have; it is a prerequisite for achieving the speed and flexibility demanded by modern mass customization and just‑in‑time delivery models.
IMTS 2026, held in Chicago’s McCormick Place North building, served as the perfect stage for showcasing technologies that address this exact pain point. Wizcore seized the opportunity to present an integrated AI‑powered workflow that marries its CAD division with its AI Factory division under a single demonstrative umbrella. The booth, positioned in the automation section, attracted a steady stream of global buyers, plant managers, and digital transformation leaders eager to see how design data can flow seamlessly into manufacturing execution. By offering live product demos, one‑on‑one consultations, and partnership discussions, Wizcore created an interactive environment where visitors could evaluate the practical implications of linking CAD intelligence with factory‑floor analytics in real time.
At the heart of the CAD showcase lies CADian, a home‑grown platform that has evolved over three decades of hands‑on design experience. Built on a foundation of 500+ automated LISP routines, CADian delivers smart dimensioning, intelligent detail‑drawing generation, and a suite of productivity‑boosting shortcuts that reduce manual drafting effort. Its deep knowledge base captures the nuances of Korean manufacturing practices while remaining adaptable to international standards, making it a versatile tool for engineers who need both speed and precision. The platform’s stability and extensive library of macros enable teams to enforce drafting standards across large projects, thereby minimizing variability and improving collaboration between design and manufacturing groups.
Complementing CADian is AI‑CAD, a generative‑enhanced layer that transforms visual inputs into structured engineering data. Using advanced image recognition, AI‑CAD interprets sketches, scanned blueprints, or even photographs of parts, extracting geometric features, dimensions, and material specifications. The system then converts this information into editable CAD drawings and accurate bills of materials without manual re‑entry. Visitors to the booth witnessed a live prompt‑based design generation feature where a simple textual description—such as “create a flange with four M8 holes spaced 50 mm apart”—instantly produced a compliant 3D model and associated BOM. This capability dramatically shortens the concept‑to‑design phase, especially for legacy documentation that exists only in paper or image format.
The live demonstrations highlighted tangible benefits for manufacturers facing pressure to accelerate product development. By pointing a tablet at a hand‑drawn schematic, the AI‑CAD engine recognized line types, identified hole patterns, and suggested appropriate tolerances based on the part’s intended function. Engineers could then tweak parameters via natural language prompts, watching the model update in real time. This interactive loop reduces the need for iterative drawing reviews, cuts down on email chains, and ensures that the digital model remains faithful to the designer’s original vision. For companies that manage large volumes of legacy drawings, the technology offers a viable path to digitize archives and make them searchable, editable, and ready for downstream manufacturing processes.
Moving to the AI Factory side of the showcase, Wizcore unveiled NEXPOM, a manufacturing integration platform designed to aggregate both structured and unstructured data from disparate shop‑floor sources. NEXPOM collects machine logs, sensor streams, quality inspection reports, maintenance tickets, and even operator notes, placing them into a unified data lake where they can be queried, visualized, and analyzed. By breaking down data silos, the platform enables cross‑functional teams to spot correlations—for example, linking a subtle vibration pattern on a CNC spindle to a rise in surface‑finish defects—without waiting for manual report consolidation. The architecture supports edge computing for low‑latency processing while also offering cloud‑scale analytics for long‑term trend analysis.
Widdy, the specialized AI agent that sits atop NEXPOM, acts as an intelligent intermediary that turns raw data into actionable insight. Leveraging natural language understanding, Widdy responds to queries such as “show me the top three causes of tool wear on line B this week” or “predict the likelihood of a downstream assembly bottleneck given current OEE trends.” Beyond retrieval, Widdy can initiate automated workflows: triggering a work order when a vibration threshold is exceeded, adjusting feed rates based on predicted tool life, or notifying quality engineers when a statistical process control chart signals a shift. This closed‑loop capability transforms passive monitoring into proactive control, reducing unplanned downtime and improving overall equipment effectiveness.
When NEXPOM and Widdy operate together, the result is a dynamic feedback network that continuously refines manufacturing performance. Real‑time dashboards display KPIs such as cycle time, yield, and energy consumption, while predictive models forecast maintenance needs weeks in advance. The system’s ability to correlate design‑stage tolerances with actual part measurements enables early detection of drift—if a batch of machined shafts consistently runs outside the tolerance band specified in the CAD model, Widdy can flag the issue, suggest a tool change, and automatically update the machining program. This tight coupling between design intent and production feedback minimizes scrap, reduces warranty risk, and accelerates continuous improvement initiatives.
The integration narrative comes full circle when considering how AI‑CAD’s structured output feeds directly into NEXPOM’s data lake. Once a design is finalized, the generated CAD model and BOM are automatically ingested, providing a authoritative reference for downstream operations. Any engineering change order triggers an automatic update in the digital twin maintained by NEXPOM, ensuring that simulation, scheduling, and quality checks always reference the latest version. This eliminates the traditional lag between design revision and shop‑floor implementation, cutting lead time by up to 30 % in pilot implementations and drastically reducing the incidence of mismatched parts that cause assembly line stoppages.
Throughout the exhibition, Wizcore hosted two daily deep‑dive sessions to educate attendees on the strategic value of their combined offering. The CAD session, titled “Design Intelligence for Manufacturing,” explored how design data transcends mere geometry to become a carrier of material specifications, tolerance intent, and manufacturability feedback. Participants learned how AI‑CAD’s generative capabilities can be leveraged to explore alternative design concepts quickly, evaluate their production impact, and select optimal configurations before committing to tooling. The AI Factory session, “From Data Collection to Manufacturing AX,” walked through the end‑to‑end process of harvesting shop‑floor signals, applying AI‑driven analytics, and closing the loop with automated control actions, illustrated by real‑world case studies from automotive and heavy‑equipment manufacturers.
Looking at broader market trends, the push toward AI‑enabled manufacturing is reflected in rising investments in industrial IoT, digital twins, and generative design tools. Analysts predict that manufacturers who successfully integrate design and production data will see average OEE improvements of 10‑15 % and a reduction in engineering change‑related downtime by as much as 40 %. However, challenges remain, including data governance, legacy system compatibility, and the need for cross‑functional skill sets. Wizcore’s approach addresses these hurdles by offering a plug‑and‑play platform that respects existing CAD investments while layering AI capabilities on top, thus protecting prior investments and reducing the barrier to entry for mid‑size firms seeking to modernize.
For manufacturers evaluating a similar transformation, the first step is to conduct a data‑maturity assessment: identify where design artifacts reside, how BOMs are maintained, and what shop‑floor signals are currently captured. Next, prioritize a pilot use case—such as automating the conversion of legacy drawings into CAD models or implementing predictive maintenance on a critical machine—and define clear success metrics (e.g., time saved, error rate reduction, OEE gain). Engage with a vendor like Wizcore that can provide end‑to‑end support, from data ingestion and model training to change‑management training for operators and engineers. Finally, establish a governance framework that ensures data quality, version control, and security, enabling the AI‑driven workflow to scale from a single line to the entire enterprise.