Vention unveils GRIIP, a generalized physical AI pipeline for factory automation

Vention has launched GRIIP, a unified physical AI stack intended to reduce the bespoke engineering normally required to deploy robotic automation in factories. The pipeline integrates the company’s internal models with third‑party foundation models for stereo vision and six‑degree‑of‑freedom pose estimation, and is delivered on the MachineMotion AI controller with OTA model updates over Wi‑Fi or LTE. Vention says the system automates scene capture, segmentation, pose calculation, grasp scoring and collision‑aware path planning so a CAD model can be converted to a working bin‑pick workflow in about 15 minutes and a fully operational cell in under 48 hours. In internal validation, the company reports continuous, lights‑out operation for three months with sustained pick reliability and cycle rates reaching up to five parts per minute. The architecture is positioned as task‑agnostic, applicable across bin picking, machine tending, kitting, depalletizing and similar workflows without per‑task bespoke engineering or large, hand‑curated datasets. Software updates to models — not hardware swaps — are central to the upgrade path Vention describes, shifting lifecycle economics toward continuous model maintenance and compatibility. The announcement is paired with a recent financing push to fund R&D in Physical AI and to expand pre‑engineered applications and regional operations, underscoring a strategic pivot to scale platform deployment across North America and Europe. That commercial intent frames GRIIP as part of a broader effort to compress automation project timelines via generative AI agents, automated configuration tools and programming assistants. Important open questions remain around edge robustness (e.g., reflective or heavily occluded parts), interoperability with legacy PLCs and safety certification for high‑volume production. Vention says enterprise demos are underway with wider rollouts expected in 2026; independent benchmarks and diverse pilot results will be critical to verify field performance and to validate the company’s promise of faster, lower‑cost automation adoption at scale.