FOR ON-PREM, COLO & PRIVATE CLOUD INFRASTRUCTURE TEAMS
AI Infrastructure Engineer
Autonomously detects faults, investigates infrastructure incidents, and performs agentic remediation. Asama ingests telemetry across your hardware, OS, and orchestrators to isolate root causes, plan fixes, and automate with experts' approval.
Storage contention from a RAID rebuild backed up via NFS to the VM, while a concurrent rsyncd burst overwhelmed compute-01's undersized RX ring buffer (1024 vs. peer 4096), causing packet loss and retransmissions.
Reschedule/throttle the RAID rebuild, move the competing workload, and increase the NIC RX ring to match peer configuration.
Not a monitoring tool
Not an observability dashboard
Not a copilot on your alerts
Asama is not another place to look at your infrastructure. It is an engineer operating on it.
The maturity curve
Every infrastructure operation sits on
L1 to L4 levels of maturity
The levels differ in one thing: how much of the intelligence the platform brings, and how much lands on you. Find yours.
Fragmented monitoring
Platform
Raises threshold alerts, holds no shared context.
Infra engineer as Investigator
You triage the alerts and correlate by hand.
Zabbix · Nagios · OpManager · checkmk · ipmitool scripts
Centralised observability
Platform
Aggregates telemetry into queryable dashboards.
Infra engineer as Analyst
You build the graphs and reason to root cause.
Prometheus + Grafana · Elastic + Kibana · Dynatrace
Assisted analysis
Platform
AI-assisted analysis inside one domain. Needs expert modelling first.
Infra engineer as Consultant
You ask in English, but the diagnosis is still yours.
Elastic AI · terminal AI assistants over MCP · LLM models
Autonomous understanding
Platform
Holds a live model of the stack and reasons causally.
Infra engineer as Approver
You review the diagnosis and approve the plan.
Asama
Without full-stack context, L3 hallucinates.
Level 4
Detect, investigate, remediate. All three run on one graph.
Every host, its topology, every normal behavior, every past fault, held in a live knowledge graph. Real diagnosis comes from that. Safe remediation comes from real diagnosis.
What changes for you
01 Nothing to configure
Baselines are learned. No thresholds, no tags.
02 Nothing to correlate
One root cause arrives backed by an evidence trail.
03 Nothing to look up
Hardware history and event log is baked into the reasoning.
04 Nothing to execute
You review a sequenced plan with a rollback and approve it.
What makes Asama accurate
Learned baselines
Deviations are modelled against peer group and machine behaviour.
Live topology
Physical, virtual and architectural dependencies modelled as a live graph.
Persistent memory
Past faults, upgrades, RMA, fluctuations records stay in context for the next diagnosis.
Multi-vendor reasoning
Mixed fleets telemetry normalised into one model, so reasoning crosses vendor boundaries.
The Multi Layer Correlation
The cause rarely lives where the symptom appears
Asama connects changes, processes, configuration anomalies, and infrastructure dependencies to reconstruct how an incident propagates across the stack.
One investigation, connected from symptom to root cause.
Coverage
Wide enough to find it, deep enough to fix it
Most tools are wide and shallow, or deep in one narrow place.
Depth · seven questions, every layer
Visibility, health, baselining, detection, RCA, remediation planning and remediation, answered at each of the five layers.
Hardware
Drives, memory, power, thermals, fans, PCIe.
BMC and out of band
IPMI and Redfish sensor state, and access when the OS is gone.
BIOS and firmware
Revisions and settings compared against the peer group.
Kernel and OS
Kernel state, drivers, logs, config drift, CVE exposure.
Virtualization
Hypervisors, VMs, containers, and the metal under them.
Breadth · where we are honest
Infrastructure: compute, hardware, firmware, OS, virtualization. Published as of this quarter.
Outcomes
What changes on the next incident
Failures surface before they become incidents
Degradation appears as peer-group deviation while the workload is still healthy, when a maintenance window is cheap.
One incident, not forty alerts
Related signals across layers consolidate into a single fault with a single owner.
Every incident arrives diagnosed
Which component, which layer, what caused it, and what happens if you leave it.
Fixes ship with a rollback
Drain, isolate, patch, verify. Sequenced, approved by you, reversible.
The fleet stops repeating itself
Every resolved incident updates the model, so the same fault class is caught earlier next time.
Reliability holds at 3am
The reasoning that sat with your most senior engineer is in the platform, around the clock.
Trust as architecture
Every action logged, every investigation auditable. SOC 2 Type II compliant, with regular manual penetration testing. Your data encrypted everywhere and never used for training.
Learn more in our Trust Center
Security Overview
Encryption: In transit & at rest.
Audit Logs are enabled.
SOC 2 Type II compliant.
“Repeat incidents were the biggest problem for us. Asama now remembers all the repeat offenders and what actions were taken at different times to resolve them.”
Infrastructure lead
Adtech, owned data centres
Approximately 1,200 servers on premises
Running today on production fleets
We are best suited for small infra teams running high density workloads. If you feel understaffed, let us benchmark Asama against the stack you already have.
Pilot scope
300 to 400 servers
Duration
Two months
Benchmark
Side by side with the incumbent stack
Issue types covered
1,000 and counting
“We moved from manual triage to Asama led triage. Issues that took hours to get to the root cause now takes a couple of mins.”
Head of infrastructure
Mid market, on premises fleet
Five person infrastructure team
Where this goes
Infrastructure should get better with age
Every fleet degrades today. Firmware drifts apart, configurations diverge, hardware wears, and the knowledge of how the fleet behaves leaves with the people who hold it.
A system that holds a live model, remembers every fault and every fix, and compares each machine against its peers gets more accurate every month it runs. The fleet gets more reliable as it ages, and the expertise compounds in the platform instead of walking out of the door.
We have spent two decades inside this problem and built the whole chain, which is why we can say it and very few others can.
Start here
Your infrastructure already has the data. Give it an engineer.
See how Asama investigates, explains and remediates a real infrastructure problem.
Bring one recurring problem your current tooling has not solved. We will show you how Asama detects it, concludes, and acts.