Funding
Funding not disclosed
Founders
Product
Problem
Current AI assistants and workflow tools often operate without a unified temporal context, making it difficult for humans and models to coordinate actions, remember past events, or plan future steps reliably.
Solution
Óra provides a shared environment where humans and AI agents can record, view, and reason about events anchored in time. By structuring interactions around an explicit event timeline, the platform grounds language model outputs in a deterministic mapping between intent and action. This temporal grounding enhances the reliability of model predictions, supports memory of past interactions, and enables forward‑looking planning. The system is designed for applications that require synchronized context across participants, such as coordinated scheduling, workflow automation, and predictive planning scenarios.
Target Audience
Primary customers are developers and product teams building AI‑driven scheduling, coordination, or workflow automation solutions that need reliable temporal context for human‑AI collaboration.
Features
- Event‑centric data model that timestamps and links actions, decisions, and outcomes into a navigable timeline
- Real‑time shared workspace where humans and AI agents can read and write contextual information about past, present, and future events
- Built‑in grounding mechanisms that map natural‑language intents to deterministic actions based on the event structure
- Support for memory persistence, allowing models to recall prior events and maintain continuity across sessions
- Planning utilities that enable predictive scenario generation and workflow coordination using the temporal context
- API integrations for embedding the Óra environment into existing scheduling, search, and automation platforms