# Asama -- AI Infrastructure Engineer

AI powered infrastructure observability and remediation. Asama connects to your servers, switches and storage, builds a live model of the stack, finds the fault, explains the cause, and runs the fix once you approve it.

Not a monitoring tool, not an observability dashboard, not a copilot on your alerts -- Asama carries the fix through instead of stopping at a suggestion.

## The maturity curve

Every infrastructure operation sits on one of four levels: L1 fragmented monitoring, L2 centralised observability, L3 assisted analysis (a better interface on the same problem -- it still needs expert modelling, sees one domain, and stops at a sentence), and L4 autonomous understanding, where the platform holds a live model of the stack and reasons causally while you review the diagnosis and approve the plan.

Most teams running their own infrastructure are at L1 or L2, and are being sold L3. L3 moves the interface. It does not move the work.

## Platform -- what changes for you

- You stop configuring what to watch -- baselines are learned, and drift is measured against the peer group, not a number picked in 2019.
- You stop correlating -- one root cause arrives as one incident instead of forty alerts.
- You stop rebuilding context -- the March firmware upgrade and last quarter's RMA are part of the reasoning.
- You stop being the one who executes -- you review a plan and approve it.

Core capabilities:

1. **Learned baselines, not thresholds** -- Asama learns how each class of machine behaves and flags deviation. No tags, no rules.
2. **A live topology graph** -- physical, virtual and architectural dependencies, so failures are followed as they propagate.
3. **Memory that persists** -- past faults, upgrades, RMAs and incidents stay in context and inform the next diagnosis.
4. **Reasoning across vendors** -- mixed-vendor telemetry normalised into one model, so reasoning crosses vendor boundaries.

A failing drive shows up as an application timeout, a kernel error, a firmware mismatch, a thermal reading and a wear counter. Five tools each raise their own alert. Asama reports it once, as a component that is failing.

## Coverage

Wide enough to find it, deep enough to fix it -- across hardware, BMC and out-of-band, BIOS and firmware, kernel and OS, and virtualization. Visibility is GA, detection is validated, RCA and remediation are expanding. Network reasoning is the active build.

## Outcomes

- Failures surface before they become incidents -- degradation appears as peer-group deviation while the workload is still healthy.
- One incident, not forty alerts.
- 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.
- Reliability holds at 3am -- the reasoning that sat with your most senior engineer is in the platform, around the clock.

## Where this goes

Infrastructure should get better with age. 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.

## Get started

See how Asama investigates, explains and remediates a real infrastructure problem -- book a demo or talk to an engineer at contactus@asama.ai.