ABOUT US

Enabling Production-Grade
Vision AI for the Real World.

We build Vision AI systems designed to operate on real production lines —
where surfaces vary, parts shift, and quality decisions matter.

OUR VISION

Powering Vision AI with
Production-Grade AI Infrastructure

Automotive body-in-white moving through a Vision AI inspection gate with industrial lighting and cameras on a production line.

We believe AI should not stop at research labs or isolated pilots. It must operate reliably in real production environments — where data is imperfect, conditions change continuously, and every decision impacts quality and throughput.

  • Vision AI is the application layer.
    It detects, classifies, and tracks defects inline on production lines, where surface variation, positional drift, and cycle time constraints leave no room for error.
  • AI Infrastructure is the foundation.
    It provides the compute, orchestration, simulation, and deployment backbone required to train, scale, and operate Vision AI systems reliably across cloud and edge environments.


At robolaunch, these two layers are designed together. By unifying cloud-based AI infrastructure with real-time edge execution, we enable Vision AI systems that move beyond proof-of-concept and remain stable at full production speed.

OUR IMPACT

Get to know who we are?

Founded in 2020

Established with a clear focus on turning AI from experimental prototypes into production-ready systems that operate reliably in real industrial environments.

Automotive body-in-white moving through a Vision AI inspection gate with industrial lighting and cameras on a production line.

20+ Vision AI in Production

Vision AI systems deployed for inline inspection and surface quality control on real production lines — operating under full cycle speeds, surface variability, and manufacturing constraints.

These systems are designed to detect, classify, and track defects where consistency, accuracy, and uptime are critical to production quality.

robolaunch AI Infrastructure dashboard showing cloud-based AI development tools, GPU configuration panel, and deployment options for Jupyter Notebook, Cloud IDE, and inference workloads.

100+ GPUs Orchestrated Across Cloud & Edge

AI infrastructure purpose-built to support large-scale training, simulation, and real-time inference pipelines required by Vision AI systems in production.

Our cloud-to-edge orchestration enables models to be trained, deployed, monitored, and operated reliably at production scale — without disrupting live operations.

Trusted in Live Production Environments

Selected by manufacturers and industrial partners to operate Vision AI systems where accuracy, uptime, and system stability directly impact production quality.

HOW WE WORK?

How We Build AI That Works in Production

Production-first mindset
We design AI systems around real production constraints — not lab assumptions or ideal conditions.

Systems, not isolated components
Vision models, data pipelines, and AI infrastructure are engineered as one integrated system.

Field-driven iteration
Live production feedback continuously improves models, deployment logic, and system behavior.

Reliability over demos
We prioritize stability, uptime, and maintainability over short-term proof-of-concepts.

Diagram showing a production-grade Vision AI workflow in an automotive factory, combining real production data, inline Vision AI inspection, scalable AI infrastructure, and low-latency edge deployment.

GET IN TOUCH

Move From AI Ideas
to Production Reality

AI only creates value when it runs reliably on real production lines.

If you’re evaluating Vision AI, scaling an existing inspection system, or building the infrastructure behind production-grade AI, we’re ready to work with you.

What You Can Expect
‍🔹
Discuss real production constraints and use cases
🔹Evaluate Vision AI inspection or quality control scenarios
🔹Align on cloud-to-edge AI infrastructure requirements
🔹Explore collaboration or partnership opportunities

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