Unlock transformative efficiencies in factory automation with MCCoder's innovative approach to motion control.
Motion control code—once a manual, error-prone, and hardware-specific grind—is being transformed by generative AI.
With tools like MCCoder, Large Language Models (LLMs) are now generating, validating, and self-correcting motion instructions with up to 131.77% performance gains on complex automation tasks.
This isn’t a minor productivity boost. It’s a foundational shift in how physical systems are programmed, unlocking safer operations, faster time-to-deployment, and higher-precision manufacturing.
For industrial CEOs and CTOs, the question is no longer if you adopt LLM-driven motion control—it’s how fast you integrate it before your competitors do.
MCCoder uses structured prompting and hybrid retrieval to translate high-level programming goals (often in Python) into verified motion control code.
Unlike traditional tools, it doesn't just generate output—it runs internal simulations, compares against known-safe actions, and iteratively improves its own code. The result:
This is what it looks like when AI doesn’t just assist—it automates entire layers of factory logic.
🔩 Graphcore (Semiconductors)
Uses LLMs in motion modules to accelerate chip fabrication workflows—delivering tighter hardware-software integration and faster production lines.
🏥 Alcon (Medical Devices)
Deploys MCCoder in surgical robotics to improve motion precision and drastically reduce coding errors in high-risk environments. Outcome: better patient results and faster deployment of robotic systems.
🚁 AeroVironment (Defense Drones)
Utilizes generative motion code to optimize drone trajectories, minimizing energy waste and improving in-flight mission accuracy across autonomous fleets.
Across sectors—from chip manufacturing to autonomous drones—LLMs aren’t a pilot project. They’re rewiring production logic at the code level.
Generic LLMs weren’t built for motion control. MCCoder is. Prioritize purpose-built tools that combine model intelligence with domain-specific safety workflows.
Legacy programmers will be outpaced by engineers who guide and verify AI-generated motion logic. Retrain for roles in:
Stop tracking just code commits. Start tracking:
Your board wants to see risk-adjusted productivity—not just faster lines of code.
MCCoder thrives on feedback. Create closed-loop feedback between engineers and machines—treat motion control as a continuously improving model, not a one-time integration.
Target hires with cross-discipline skills:
Upskill your existing automation engineers to become verifiers and feedback loop designers, not just script writers.
Ask every AI-powered control vendor:
A tool that works in a demo room isn’t enough. You need systems that handle scale and regulatory environments in production.
New capabilities = new risks. Key vectors to monitor:
Implement LLM observability tools, fail-safes, and multi-tiered safety checks.
Motion control coding is the final frontier of industrial automation—and LLMs are conquering it.
Are your machines running on human time, or AI time?
This is your moment to architect smarter factories—not just faster ones.
