Kebra makes field operations queryable by capturing what happens across physical work sites and building a live operating memory of the field. The platform deploys AI agents that automate the follow-up work, enabling technicians to focus on execution rather than documentation and coordination. By grounding AI in real-world field data, Kebra overcomes the model's knowledge gap that limits AI effectiveness in operational environments.
Funding
Funding not disclosed
Founders
Product
Problem
AI systems in field operations are limited by what they know about actual on-the-ground conditions and events. Without comprehensive, real-time visibility into what happens across physical work sites, AI models cannot provide accurate insights or automate tasks effectively, leaving significant gaps in operational intelligence and efficiency.
Solution
Kebra captures what happens across physical operations and builds a live operating memory of the field, making it queryable for AI systems. The platform deploys AI agents that automate the work that follows field activities, reducing manual data entry and coordination overhead. By creating a structured, accessible record of field events, Kebra enables AI models to operate with the contextual knowledge they need to deliver meaningful support. This approach transforms raw field observations into actionable operational intelligence that drives productivity for technicians.
Target Audience
Field service organizations, operations managers, and technical teams that need AI-driven automation and real-time visibility into distributed physical work environments.
Features
- Live operating memory system that continuously records and organizes field events and activities
- AI agent deployment that automates follow-up workflows triggered by field operations
- Queryable field data architecture that makes operational information accessible to AI models
- Event capture across physical work sites for comprehensive operational visibility