From process engineers building bespoke “Process Agents” to seamlessly bridging customer demand with manufacturing execution, Quick AI is emerging as UnionTech’s core platform for driving industrial AI applications.
SHANGHAI, Oct. 10, 2026 /PRNewswire/ — When a 3D model of a complex part arrives on a process engineer’s desk, the print job does not begin immediately.
Is the material suitable? How should the part be oriented? Where are supports required? Which areas are prone to deformation? How should print parameters be adjusted? Traditionally, addressing these questions relies on the seasoned judgment of senior engineers, followed by iterative trial-and-error to arrive at a viable approach.
At UnionTech, this process previously took anywhere from several hours to multiple days.
Today, for new projects supported by historical precedent and comparable case data, engineers can leverage self-built Process Agents to generate an initial process plan in just minutes. Engineering teams then review the plan and schedule targeted validations for critical risks.
This paradigm shift is unfolding on UnionTech’s proprietary Enterprise Agent OS—Quick AI.
Allen Yang, Vice General Manager & Head of Digital Center and AI at UnionTech, believes industrial 3D printing is entering a new stage of competition. While hardware is capable of fabricating increasingly complex parts, customers are now demanding consistent, controlled, and continuous small-batch production.
“Hardware determines what we can build, but whether a complex project can be formulated quickly and delivered reliably hinges on engineering expertise and workflow coordination,” said Yang. “Our AI initiatives are designed to better organize, institutionalize, and scale these capabilities.”
Process Experts: Transforming Tribal Knowledge into Process Agents
In industrial manufacturing, crucial domain knowledge often remains locked in personal experience.
The exact same material may require entirely different processing strategies depending on varying wall thicknesses, geometries, and end-use applications. Why a particular parameter adjustment succeeded—or why a specific support strategy failed—is often understood only by the engineers directly involved in the project.
Over the years, this accumulated know-how has formed the core technical barrier of the enterprise. Yet whenever a new project arrived, teams still had to hunt down the engineers with relevant expertise, sift through past records, and re-evaluate from scratch.
Quick AI provides a modern vehicle for this tacit knowledge.
On the platform, process engineers can build their own Process Agents to continuously log process workflows, experimental results, and empirical parameters. Every detail—the selected materials and parameters, challenges encountered, subsequent adjustments, and the final outcome—becomes valuable institutional reference data for future projects.
“In the past, once a project was completed, usually only the final recipe was retained. But what truly matters is the decision-making process along the way,” explained Yang. “Why the part was oriented a certain way, why parameters were tweaked, which methods were attempted and failed—this is the real intellectual property worth preserving.”
When a new project is onboarded, the Process Agent leverages existing logs and similar historical cases to rapidly structure a proposed process plan, helping engineers pinpoint the most promising paths for priority validation.
For projects backed by historical precedent, the preparation phase—which once involved lengthy searches, discussions, and trial runs—is now compressed into minutes. Engineers can focus their energy where it matters most: validating novel structures, qualifying new materials, and mitigating critical risks.
Here, “a few minutes” refers to generating the initial proposal; it does not imply that full process validation is complete. Complex components still demand engineering reviews and necessary physical trials. The transformation lies in giving the team a data-backed starting point from day one, drastically cutting down repetitive trial and error.
“Our goal is to ensure that every project completed by our engineers empowers the team to execute the next project better, faster, and smarter,” said Yang.
Integrating Quick AI into Workflows: Powered by an End-to-End Data Backbone
Process Agents represent only the entry point. Enabling AI to deeply participate in manufacturing requires overcoming a fundamental challenge: bridging siloed business systems.
Customer requirements might be buried in emails, quotation benchmarks are stored in separate business systems, production schedules are tracked on factory shop floors, and quality inspection records are handled by different personnel. Even if AI can parse each individual piece of information, it must accurately map that data to the specific order, part, and design revision.
UnionTech’s decade-long investment in digital transformation laid the groundwork for this integration:
Unionfab Cloud orchestrates business collaboration across customer engagement, RFQs, quoting, order management, and supply chain logistics.Unionfab ONE streamlines digital operations across 3D model preparation, process configuration, and machine control.The Auto Product Suite continually drives specialized algorithmic development and intelligent industrial applications.
Quick AI’s role is to act as the collaborative orchestrator across this infrastructure—connecting intelligent agents with enterprise knowledge bases and operational tools, allowing personnel to effortlessly tap into institutional capabilities.
While Cloud and ONE continue to drive business and manufacturing execution, Quick AI serves as the intelligent interface for task interpretation, knowledge retrieval, and tool orchestration. As APIs and workflow automations mature, the operational scope accessible to these agents will expand even further.
“Over the years, UnionTech has built not only a robust suite of software tools, but also an extensive repository of manufacturing expertise and process data. Our systems have accumulated nearly 5 million industrial part models along with their corresponding process parameter datasets,” said Yang. “Quick AI is designed to make these assets intuitive and actionable for front-line teams, ensuring AI directly interfaces with real-world production.”
Automated quotation serves as an active, real-world starting point.
Unionfab has already deployed automated quoting for standardized requirements, pairing 3D model geometry, materials, process rules, and cost algorithms to substantially reduce customer turnaround times. Highly specialized requirements and complex projects are automatically routed to manual engineering review.
These digital foundations empower future Process Agents to assist with requirement interpretation, material selection, and quote preparation. High-stakes decisions involving pricing commitments, critical tolerances, and stringent quality specs continue to be governed by established rules and human sign-off.
AI deployment at UnionTech follows a pragmatic, value-driven trajectory: solve a tangible operational problem, validate it within live workflows, and systematically scale its application.
Unionfab: Exposing AI to Real-World Orders Every Single Day
UnionTech benefits from a vital real-world proving ground: the Unionfab global on-demand manufacturing platform.
Here, customer journeys begin with online engagement and RFQs, move through automated or engineer-assisted quotation, order processing, and supply chain orchestration, and culminate in manufacturing, QA inspection, and global logistics. Incoming platform orders are fulfilled through a hybrid network: partly manufactured by UnionTech’s own fleet of industrial-grade 3D printers, and partly distributed across a partner network of hundreds of 3D printing service providers managed via the Unionfab Cloud ecosystem.
Every stage in this pipeline surfaces concrete operational challenges:
Why was a particular quote declined?Which geometries repeatedly trigger manual engineering revisions?Did a specific quality tolerance get accurately transmitted to the shop floor?What root cause in an anomaly compromised a delivery window?
These live operational friction points define technical R&D priorities and provide an objective benchmark for whether AI tools deliver actual value.
If a new feature is deployed but engineers still have to conduct extensive manual cross-checks, it requires refinement. If an automated pre-processing step saves digital clicks but fails to enhance physical build stability, it must be re-evaluated.
“Customers don’t lower their standards for quality or lead times just because we use AI,” Yang emphasized. “What matters to them is whether the solution works, whether our response is faster, and whether the parts are delivered strictly to specification.”
Unionfab ensures UnionTech remains in continuous contact with authentic market demands, giving AI R&D immediate, high-fidelity feedback. Recommendations generated by Process Agents are tested against real-world shop floor constraints, cost models, and quality metrics. The fresh insights generated by these projects are then fed back to enrich the enterprise knowledge base.
In targeted production programs, such as tire mold manufacturing, UnionTech has already deployed integrated solutions that combine equipment, advanced materials, proprietary software, and tailored process recipes. These proven manufacturing practices provide empirical benchmarks for further intelligence.
Operating a proprietary digital service platform in tandem with physical production creates a continuous iteration loop: business operations define the challenges, technology provides the tools, and physical manufacturing validates the results.
Manufacturing Outcomes: Giving Experience the Medium to Scale
Industrial AI must answer one fundamental question: How do you verify that the model’s judgment is actually effective?
A 3D model successfully processed in software does not guarantee stable, repeatable physical fabrication. Ultimately, final part quality, total unit cost, and on-time delivery are the only true metrics of whether a process decision was sound.
UnionTech is actively correlating data across geometric features, material and process parameters, real-time machine telemetry, and final quality and delivery outcomes. The objective is to help engineering teams systematically understand why a manufacturing decision succeeded or failed, and which parameters are ripe for standardized reuse.
Governed by strict authorization boundaries and enterprise data security protocols, these historical datasets serve as the fuel for knowledge institutionalization, rule refinement, and algorithm optimization.
The long-term enterprise value of Quick AI is anchored in this closed-loop feedback. The broader the library of reference projects and the more rigorous the validated data, the more effectively Process Agents can guide upcoming manufacturing challenges.
“What we strive to retain above all else is actionable experience that informs the next decision,” said Yang. “Every time a project finishes, our organizational capability must take a step forward.”
This means senior engineers’ tribal knowledge is steadily transformed from individual intuition into shared, accessible organizational capability. Onboarding personnel can ramp up rapidly using historical intelligence, while mature, validated process parameters can be scaled and reproduced across more machines, production facilities, and client applications.
UnionTech’s AI Roadmap: Scaling Reproducible Manufacturing Capabilities
UnionTech has consistently positioned the development of AI capabilities as the core engine of its long-term competitive advantage.
Hardware and materials deliver the physical foundation; software structures process knowledge into executable digital workflows; Unionfab channels a steady stream of customer demand and fulfillment feedback; and Quick AI introduces a unified interface to orchestrate engineering intelligence and business tools.
Whether these synergies unlock real business value is measured by tangible operating outcomes: faster customer adoption, higher engineering capacity per team, superior quality consistency, reliable delivery timelines, and the seamless replication of mature manufacturing recipes across diverse client bases.
UnionTech is executing firmly along this strategic roadmap.
As Quick AI becomes deeply integrated with its mature digital ecosystem, UnionTech aims to create an uninterrupted loop connecting customer demand, process engineering, machine execution, and physical performance feedback—driving the continuous accumulation and scalable replication of industrial manufacturing intelligence.
Competition in industrial additive manufacturing is expanding far beyond standalone hardware specifications. How rapidly an organization interprets requirements, how consistently it delivers parts, and how effectively it reuses accumulated expertise will increasingly determine the quality of long-term business growth.
By embedding AI directly into the manufacturing workflow, UnionTech is explicitly addressing these very challenges.
About UnionTech
UnionTech is a global provider of industrial-grade 3D printing equipment, advanced 3D printing materials, and on-demand digital manufacturing services. Its Quick AI platform serves as an enterprise Agent OS enabling the creation and deployment of specialized Process Agents; Unionfab Cloud drives end-to-end digital collaboration across customer acquisition, order management, and supply chain operations; and Unionfab ONE digitalizes CAD pre-processing, process configuration, and machine-level control. Together, these platforms form a unified digital backbone powering the future of intelligent, automated additive manufacturing.
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SOURCE UnionTech
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