A demo shows Jev listening to a speaker and checking off bullet points from a list as they are covered. This provides live feedback to ensure all key topics are addressed, acting as a real-time monitor for structured communication tasks like interviews, sales pitches, or presentations.
The near-zero cost and high speed of models like Jev remove the financial and time barriers for analyzing large datasets (e.g., 5GB of JSON). This opens up opportunities for data exploration that were previously considered too expensive or time-consuming to be worthwhile.
In a to-do list demo, Jev processes continuous speech without pauses. It sequentially classifies when an action can be taken, matches the speech to an existing item, and then identifies the correct function to call (e.g., complete, remove). This creates a seamless, real-time user experience.
A demo of Jev playing chess shows it's 10x faster than a traditional LLM. It wins by quickly evaluating all possible moves and their immediate consequences. This demonstrates that in systems with a limited but large set of actions, decision speed can be a greater advantage than raw reasoning power.
Navigating a webpage seems infinite (any pixel is a target). However, Jev can automate this by identifying the finite set of clickable elements (e.g., buttons in the DOM). This reframing of the problem from an open canvas to a discrete choice set makes complex automation tasks feasible and fast.
In a simulation with multiple agents having different tasks, Jev can control each agent's movement at every step. By doing just-in-time checks, it ensures agents reach their goals efficiently without interfering or colliding with each other, a key challenge in agentic programming and robotic swarms.
Jev can be layered on top of other tools or models to create a navigation or routing system. It can parse user input to determine which tool to activate and what action to perform, effectively directing traffic within a complex application or agentic system at near-zero latency.
When a decision model makes a mistake, developers can debug it by analyzing its limited choices and logic, similar to fixing an if-else statement. This contrasts with generative LLMs, where fixing errors often involves guessing different prompts. This sense of control makes development more predictable and structured.
Jev can quickly compare vast numbers of records (e.g., contacts, passwords) to identify duplicates for merging. This is a practical, high-impact use case for cleaning messy data, which is often too expensive or slow with traditional LLMs, especially for hundreds of thousands of items.
The key indicator for using a decision model isn't the complexity of the application (e.g., a 3D game) but the nature of its inputs. If user interaction is limited to a constrained set of actions (e.g., controller buttons, API calls), a decision model is an ideal fit, unlike tasks requiring open-ended analysis.