AmrudAi — AI IT observability
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Home AI & Autonomous RCA

The Enterprise AI Unified Observability Platform

Make the next decision with the evidence in front of you.

AmrudAi’s AI and root-cause architecture combines qualified telemetry, temporal topology, protocol state, metrics, logs and configuration-change evidence. The investigation ranks defensible hypotheses and preserves contradictions and missing information, including an INCONCLUSIVE outcome. Enterprise NOC and service teams retain control of diagnostic scope, data boundaries and privileged actions.

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Capabilities & benefits Operational workflow Deployment & coverage Common questions

The application experience

Investigation Workbench

See the complete workspace. Enlarge any image to inspect its panels, navigation and evidence at actual size.

Evidence-Led RCA Workbench — illustrative product view with sample data. Illustrative product view · Sample data
Bring hypotheses, contradictions, configuration changes and remediation checks into one investigation.
Adaptive Baselines & Anomaly Detection — illustrative product view with sample data. Illustrative product view · Sample data
Compare observed behavior with learned seasonality, quality gates and independently reviewed outcomes.
Alert Qualification & Noise Control — illustrative product view with sample data. Illustrative product view · Sample data
Preserve evidence while deduplicating events, verifying vantage points and grouping common causes.

AI & autonomous RCA

Learn the pattern. Verify the cause. Explain the next action.

AmrudAi brings statistical learning, causal investigation and a domain-aware Copilot into one evidence workflow. Each layer has a defined role, so a useful explanation remains connected to the observations that support it.

Architecture illustration Three connected layers of intelligence

  1. Statistical ML

    Learn the expected range and identify meaningful departures.

  2. Causal qualification

    Test dependencies, independent observations and competing explanations.

  3. Evidence-grounded Copilot

    Explain impact, answer operational questions and prepare a reviewable action.

Metrics, flows, logs, changes and service checks enter a shared investigation. Their source, time window and evidence quality remain attached through all three layers.

Statistical ML & dynamic baselines

Recognize the difference between normal demand and an emerging fault.

Hourly, daily and weekly patterns give a metric its operating context. Rolling, robust and seasonal models learn the expected range, while persistence and peer comparisons help separate a predictable workload rise from a condition that needs attention.

Related signals add meaning to an excursion. A growing queue beside higher latency tells a different story from higher throughput with stable response time. Collection resolution and source freshness stay visible when assessing brief bursts or gradual deterioration.

  • Seasonal baselines reduce repeated manual threshold adjustment.
  • Short-lived traffic, latency and retransmission bursts are assessed against the available measurement resolution.
  • Sustained memory growth, storage pressure and resource contention are compared with workload demand and restart history.

Operational benefit Engineers spend less time maintaining static thresholds and gain a clearer starting point for investigation.

Causal topology & cross-region qualification

Check the dependency and the independent observation.

The incident-time graph relates L2 links, L3 paths, overlays, hosts, containers, databases and business services. AmrudAi evaluates the relationships that existed when the symptom appeared, including redundant paths and shared-risk dependencies.

Permitted Sensors in independent regions compare reachability to the same destination and service. A failure on one path and success on another help narrow the fault domain. Contradictory results remain part of the investigation, and unavailable Sensor evidence is treated as a collection limitation.

  • Deduplication and maintenance policy remove repeated or expected observations from the escalation path.
  • Dependency-aware grouping brings related downstream symptoms into a common incident.
  • Competing hypotheses are revised as configuration, transport, log and service evidence arrives.

Operational benefit The NOC can distinguish a local path issue, regional transit problem and destination failure before escalating ownership.

Evidence-grounded LLM & agentic Copilot

Turn the investigation into an explanation an engineer can use.

The Copilot answers questions about what changed, which services are affected, why an explanation is supported and what to verify next. Its bounded evidence context includes source references, time windows, operational scope and supporting or conflicting observations.

Autonomous evidence acquisition selects useful diagnostic checks within explicit target, time and resource limits. The Copilot prepares CLI guidance, Ansible proposals or Terraform change plans with prerequisites, expected effects and recovery checks. Privileged execution follows the authorized policy, approval and audit workflow.

  • Natural-language answers link back to the shared investigation record.
  • Missing, failed or denied diagnostic checks remain visible alongside successful results.
  • Severity describes business impact; evidence confidence describes the support for an explanation.

Operational benefit The next engineer receives the findings, remaining questions and proposed validation without repeating the same disconnected searches.

Alert qualification & noise control

Fewer repeated interruptions. A fuller explanation.

Qualification reduces the number of separate items an engineer must review while keeping the contributing observations available. The illustration below follows one event storm through that process.

Workflow illustration One example event storm
  1. 250,000

    Raw observations

    Retain source identity, timestamps and collection quality.

  2. 1,500

    Distinct candidates

    Group repeated observations and preserve their counts and duration.

  3. 42

    Verified candidate groups

    Compare permitted regional observations of the destination and service.

  4. 6

    Related symptom groups

    Connect downstream effects through their incident-time dependencies.

  5. 1

    Root-cause incident

    Bring the supported explanation and contributing evidence together.

Illustrative counts, not measured customer results. The item being counted changes as observations become candidates, symptom groups and an incident.

What the example means

Keep the facts. Reduce the repetition.

Repeated events retain their first and last observation times. Related symptoms retain their dependencies. Suppression reasons and recovery conditions stay attached to the investigation.

99.4% deduplication
250,000 raw observations become 1,500 distinct candidates in the first grouping step.
One incident, with its evidence
42 verified candidate groups become six related symptom groups and then one consolidated incident.

The full illustration has 99.9996% fewer displayed items. That ratio describes grouping; it is not a false-positive reduction, detection-accuracy or customer-productivity measurement.

Designed for enterprise operations

The investigation remains useful throughout the incident.

AI assistance works within the same identity, access, evidence and operating model as the rest of AmrudAi.

One accountable incident record
Timeline, blast radius, configuration differences, metric changes, logs and transport evidence remain connected in the Investigation Workbench. Earlier conclusions are preserved when new evidence changes the explanation.
Customer-controlled AI placement
Local, private or approved hosted model access follows the deployment data policy. The AI Gateway scopes evidence, masks restricted fields and records model, prompt and decision provenance.
Monitoring has operating priority
Collection, telemetry durability, alerting and deterministic RCA retain resource priority. Optional model work queues or yields under pressure, and the evidence workflow remains available when a model provider is unavailable.
Actions include validation
Target scope, prerequisites, approval, audit and recovery checks travel with the proposed action. Service verification closes the investigation with evidence of the result.

Complete the operational workflow

More context. More useful decisions.

AI assurance that stays inspectable

Retain model, rule and prompt provenance, evaluated outcomes, resource budgets and data policy with the operating record. Drift review and challenger evaluation use measured results; uncertainty and inconclusive explanations remain valid outcomes.

The capability in detail

Three layers of intelligence. One accountable investigation.

01

Statistical ML and dynamic baselines

Learn the expected operating range for each metric using rolling, robust and seasonal models. Hourly, daily and weekly patterns help distinguish the predictable morning workload from a new problem, reducing repeated manual threshold adjustment.

02

Micro-bursts and gradual deterioration

Correlate short-lived retransmission, traffic or latency bursts with sustained memory growth, storage pressure and queue buildup. Evaluate the available collection resolution and the duration of the condition so the explanation fits the observed evidence.

03

Multivariate and peer context

Compare related signals and comparable workload cohorts. A rising queue, changing request rate and increasing latency together can be more informative than an isolated threshold crossing. Cohort membership and evidence sufficiency remain visible.

04

One causal operational graph

Relate physical and logical dependencies across L2, L3, overlays, hosts, containers, databases and business services. Evaluate the relationships that existed during the incident, including redundancy and shared-risk dependencies.

05

Cross-region qualification

Compare permitted Sensor observations of the same destination and service. Independent success and failure results help distinguish a local path, regional transit problem and destination failure before widening an escalation.

06

A quieter incident queue

Deduplicate repeated observations, apply maintenance and expected-state policy, qualify the condition and group dependent symptoms. The incident retains its contributing events, suppression reasons and recovery conditions.

07

Competing hypotheses and progressive RCA

Evaluate each explanation against supporting facts, contradictory observations and missing expected effects. Preserve earlier RCA revisions when new evidence strengthens, changes or rejects the diagnosis.

08

Evidence-grounded Incident Copilot

Ask what changed, which services are affected, why a cause is supported and what to verify next. The domain-aware Copilot uses a bounded evidence context with source references, time windows and operational scope attached to the answer.

09

Autonomous evidence acquisition

The investigation planner selects useful diagnostic checks within explicit authority, target, time and resource limits. It can collect permitted evidence while keeping each result and any denied, missing or failed check visible to the operator.

10

Reviewable remediation plans

Generate CLI guidance, Ansible playbook proposals and Terraform change plans with explicit targets, prerequisites, expected effects and recovery checks. Privileged execution follows the approved action workflow, including policy, access, blast-radius and audit controls.

11

Tenant-aware AI placement

Use local, private or approved hosted model access under the deployment's data policy. The AI Gateway controls the evidence sent to a model, masks restricted fields and retains model, prompt and decision provenance.

12

Monitoring remains protected

Resource controls prioritize collection, telemetry durability, alerting and deterministic RCA. Optional model work queues or yields under pressure, and operational investigation remains available when a model provider is unavailable.

From information to action

From an unusual signal to a validated operational outcome.

  1. 01

    Learn and observe

    Establish the relevant baseline, collect the change and retain source quality, timestamps and monitoring context.

  2. 02

    Qualify and correlate

    Check persistence, independent vantages, service dependencies and contradictory evidence before assigning operational meaning.

  3. 03

    Explain and prepare

    Open the hypothesis, impact and evidence in the Workbench. Ask the Copilot for the next check or a reviewable action plan.

  4. 04

    Act and verify

    Use the authorized workflow, verify the affected service and preserve the decision, result and recovery evidence for the next shift.

Intelligence that fits enterprise operations

Fit the way your team operates.

Bring machine learning and AI assistance into the same identity, access, evidence and operating model as the rest of the platform.

  • A shared investigation across network, infrastructure, cloud, workload and service domains.
  • Explicit evidence coverage, severity, confidence, contradictions and source freshness.
  • Customer-controlled data egress and local/private inference options.
  • Controlled diagnostics and remediation with operational authority and post-action validation.
Discuss your environment

Connect the workflow

Investigation Workbench eBPF observability Cross-region alert qualification Network Change & Configuration Assurance

How does AmrudAi use AI in incident investigation?

How is this different from adding a chatbot to a monitoring dashboard?

The statistical and causal engines perform qualification and correlation over the operational evidence. The Copilot receives that scoped investigation context and explains it in plain language. Answers connect back to the underlying metrics, events, dependencies and tests, so an engineer can inspect the basis for the recommendation.

Does a high confidence label mean the cause is certain?

Confidence expresses how strongly the available evidence supports a hypothesis. It is separate from incident severity and is not a probability guarantee. AmrudAi preserves contradictions and missing evidence, and revises the conclusion when stronger information arrives.

Can the Copilot make privileged changes by itself?

The Copilot prepares an explanation or structured action proposal. Execution is authorized separately by the deterministic action policy, the user's scope and any required approval. Target validation, command constraints, change evidence and recovery checks stay attached to the action.

What happens if an AI model is unavailable?

Collection, alert evaluation, deterministic RCA, topology, evidence search and manual investigation continue. The interface identifies unavailable assistance and preserves the current investigation, so the NOC retains its operational workflow.

Can we measure alert reduction and productivity in our own environment?

Yes. Measure raw events, deduplicated observations, qualified alerts, grouped incidents and operator notifications separately. Compare handling time and restoration outcomes for matched incident cohorts, with sample size, scope and measurement gaps visible.

Follow the evidence behind the answer

How does AmrudAi distinguish a likely cause from missing evidence?

AmrudAi’s documented RCA workflow separates observations, evidence quality and possible causes. It considers freshness, topology, timing, supporting facts and contradictions before proposing the next check. Missing telemetry can produce unknown or degraded monitoring states, and insufficient causal evidence can remain INCONCLUSIVE. An AI suggestion does not authorize a privileged action.

Evidence states and investigation decisions
Review Evidence to inspect Required distinction
Observed facts Source, timestamps, entity identity and collector health. A collection failure does not automatically mean the target failed.
Likely cause Supporting facts, contradictions, incident-time dependencies and confidence. Temporal proximity and model confidence do not establish causation.
Missing evidence Expected, received, stale and unavailable observations. UNKNOWN, STALE or MONITORING_DEGRADED may be appropriate; RCA may remain INCONCLUSIVE.
Suggested action Authorized diagnostic catalogue, targets, parameters and safety conditions. Deterministic policy and permissions govern execution independently of model wording.

These are documented reasoning and safety requirements. Evaluate their behavior with representative positive, contradictory and no-data cases.

See the workflow in your environment.

Explore the technology, service dependencies and operating requirements that matter to your team.

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