Many of today’s edge enterprise AI solutions are still tethered to the cloud—but change is coming. The long-term cost of cloud AI is unsustainable. Shifting data processing away from remote data centers not only frees up bandwidth but also slashes upfront and ongoing compute costs of your AI systems. By moving hundreds of sensors to the true edge, one Latent AI customer eliminated cloud inference costs, drastically reduced energy consumption, and cut hardware installation costs—all while accelerating response time, enhancing security, and maintaining accuracy.

Enterprise AI teams are moving away from one-size-fits-all approaches in favor of greater control and visibility over their edge AI solutions. Latent AI tools integrate with existing enterprise AI platforms and simplify complex ML development, enabling teams to improve business processes so that engineers of all skill levels can innovate and scale projects rapidly. With LEIP, you can interact with the entire ML pipeline via a Python API, allowing both beginners and experts to design, optimize, and deploy machine learning models with ease. This intuitive, user-friendly approach leverages your existing expertise, empowering teams to apply tailored AI solutions—without the need for specialized AI data scientists.

Over time, edge AI models can become less accurate as real-world conditions change. For instance, a model trained to detect defects in manufacturing products might struggle if new types of defects emerge or the lighting conditions in the factory change. To make confident decisions, users must quickly adapt and redeploy edge AI with new data gathered in the field. LEIP software allows users to quickly leverage reusable pipelines to modify, retrain, and deploy edge AI. Using LEIP, the Navy experienced 18x faster model update and redeployment. LEIP also offered the Navy the ability to simplify advanced artificial intelligence techniques so that users in the field can manage AI models.