Asama offers an AI‑driven infrastructure engineer that continuously monitors heterogeneous environments, detects anomalies, and provides plain‑English insights via an NLP‑powered interface. Its agentic remediation workflows can autonomously execute safe fixes across hardware, firmware, OS, and orchestration layers, reducing downtime and manual effort for enterprise infrastructure and SRE teams.
Funding
Funding not disclosed
Founders
Product
Problem
Infrastructure teams are overwhelmed by fragmented telemetry, noisy dashboards, and manual remediation processes that cannot keep pace with the scale and heterogeneity of modern hardware, firmware, OS, and orchestration layers.
Solution
Asama provides an AI‑driven infrastructure engineer that continuously monitors heterogeneous environments, detects anomalies, and performs root‑cause analysis using a unified memory layer that normalizes data across vendors and SKUs. Its NLP‑powered interface translates complex system states into plain‑English insights and actionable recommendations. Remediation is handled by agentic workflows that can autonomously execute safe, repeatable fixes at fleet scale while allowing human oversight when needed. The platform also tracks security posture and patch levels as continuous state, turning compliance into an ongoing condition rather than periodic audits. By converting observability into actionability, Asama reduces downtime, manual effort, and operational burnout.
Target Audience
Primary customers are enterprise infrastructure and site‑reliability engineering teams managing large, heterogeneous server, GPU, and cloud environments.
Features
- Unified memory layer that aggregates and normalizes telemetry from diverse hardware, firmware, OS, and orchestration tools
- AI‑based detection of performance issues, config drift, and silent failures without predefined thresholds
- Automated root‑cause analysis that correlates signals across the full stack in real time
- Agentic remediation workflows that execute safe fixes autonomously or with human‑in‑the‑loop control
- NLP‑driven conversational interface delivering contextual insights and remediation steps in plain English
- Continuous compliance tracking that maintains security and patch states as part of the infrastructure model