ML models automatically monitor business metrics and send alerts for abnormal drops or spikes.
Businesses struggle to monitor metrics manually, missing abnormal drops or spikes that impact revenue and service.
Businesses struggle to monitor metrics manually, missing abnormal drops or spikes that impact revenue and service.
Automatic ML monitoring of database metrics with real-time alerts via Slack, no engineering needed.
Automatic ML monitoring of database metrics with real-time alerts via Slack, no engineering needed.
Orbiter provides machine learning models that automatically monitor business performance metrics. The platform detects abnormal drops or spikes in metrics and sends alerts, enabling quick responses to potential issues. Onboarding requires only database credentials, taking less than five minutes without engineering support.
Orbiter integrates with databases to track key business metrics in real time. Machine learning algorithms identify anomalies, such as unexpected declines in revenue or user engagement. Alerts are delivered through channels like Slack, supporting better customer service and revenue management. This no-code approach suits teams seeking efficient monitoring.
Businesses use Orbiter to oversee performance indicators across operations. It applies to metrics like sales volume, user retention, or system health. The system supports B2B and enterprise environments, focusing on SaaS and machine learning categories. Teams gain visibility into data without manual oversight.
Users connect their database to activate monitoring. The platform handles model training and anomaly detection automatically. Integration with communication tools ensures timely notifications. This simplicity allows rapid deployment for various company sizes.
Built on machine learning, Orbiter processes metrics continuously. It distinguishes normal fluctuations from significant events. The solution emphasizes ease of use, targeting non-technical users. Available information highlights its focus on real-time insights.
Orbiter emerged from Y Combinator Winter 2020 batch. Founders brought experience from Tesla, Facebook, DoorDash, and other ventures. The team operated from San Francisco, with a small group of three employees. Current status reflects inactivity per official listings.
Orbiter provides machine learning models that automatically monitor business performance metrics. The platform detects abnormal drops or spikes in metrics and sends alerts, enabling quick responses to potential issues. Onboarding requires only database credentials, taking less than five minutes without engineering support.
Orbiter integrates with databases to track key business metrics in real time. Machine learning algorithms identify anomalies, such as unexpected declines in revenue or user engagement. Alerts are delivered through channels like Slack, supporting better customer service and revenue management. This no-code approach suits teams seeking efficient monitoring.
Businesses use Orbiter to oversee performance indicators across operations. It applies to metrics like sales volume, user retention, or system health. The system supports B2B and enterprise environments, focusing on SaaS and machine learning categories. Teams gain visibility into data without manual oversight.
Users connect their database to activate monitoring. The platform handles model training and anomaly detection automatically. Integration with communication tools ensures timely notifications. This simplicity allows rapid deployment for various company sizes.
Built on machine learning, Orbiter processes metrics continuously. It distinguishes normal fluctuations from significant events. The solution emphasizes ease of use, targeting non-technical users. Available information highlights its focus on real-time insights.
Orbiter emerged from Y Combinator Winter 2020 batch. Founders brought experience from Tesla, Facebook, DoorDash, and other ventures. The team operated from San Francisco, with a small group of three employees. Current status reflects inactivity per official listings.
Total Raised: Y Combinator seed
Last Round: Y Combinator Winter 2020
Total Raised: Y Combinator seed
Last Round: Y Combinator Winter 2020
SaaS
SaaS
B2B enterprises needing metric monitoring
B2B enterprises needing metric monitoring
unknown
Hiring: unknown
unknown
Hiring: unknown
