Faster resolution
Causality chains, conversational AI query, and semantic deduplication collapse hours of manual cross team correlation into seconds, directly cutting mean time to resolution on multi service incidents.
AIOps & AI Log Management
Every log. Every event.One root cause,automatically.
XpediOo AIOps & AI Log Management ingests every log line and every monitoring event your enterprise produces, alerts fired by APM, network, synthetic, and ITSM tools included. It uses machine learning to deduplicate and correlate them, collapses alert storms into a handful of actionable incidents and, within approval gated guardrails, resolves recurring failures before a human ever gets paged.
AI Event Correlation (AIOps) · Correlations
120+
AI features
40+
Log and event sources
7
Capability domains
Overview
From alert storms and log noise to one root cause
Enterprise environments don't just bury teams in log lines. They bury them in alerts. A single upstream failure can trigger hundreds of near simultaneous notifications across APM tools, network monitors, synthetic checks, and ticketing systems, each one looking like its own incident. On call engineers routinely spend the most critical minutes of an outage untangling which alerts are symptoms and which one is the cause, before log analysis has even started.
XpediOo AIOps & AI Log Management solves both problems in one platform. Every log is ingested and normalized, and machine learning mines it for the handful of patterns that matter. In parallel, every event fired by connected monitoring, network, and ITSM tools is deduplicated, grouped by topology, and correlated in real time. This is the AIOps core, purpose built to turn a flood of alerts into a small number of actionable incidents. Both streams feed the same root cause engine, so the platform explains a single incident with the full picture: the alerts that fired, how they relate, and the log lines that explain why. Predictive models and approval gated automation stand ready to resolve the recurring ones on their own.
Seven capability areas
From raw logs and alert storms to automated resolution
Moving from universal log and event ingestion, through AI driven pattern detection and AIOps noise reduction, into root cause correlation, predictive automation, and the governance controls that keep it all audit ready.
Ch. 01
Universal log ingestion and normalization
A single ingestion layer that speaks every log format your estate produces, normalized into one queryable schema before it reaches search or AI analysis.
- Native collectors for Syslog, Windows Event Log, journald, and container stdout/stderr
- Cloud native log sources: AWS CloudWatch Logs, Azure Monitor Logs, GCP Cloud Logging
- Kubernetes pod, node, and control plane log aggregation via DaemonSet collectors
- Schema on write normalization across JSON, key value, CSV, and unstructured free text
Ch. 02
Multisource event ingestion
Every alert your monitoring and ITSM stack produces (metrics, APM, network, synthetic checks, and tickets) lands in the same event stream as your logs, ready for AI correlation.
- Native connectors for APM and metrics tools: Prometheus/Alertmanager, Datadog, New Relic, Dynatrace
- Cloud native alerting: AWS CloudWatch Alarms, Azure Monitor Alerts, GCP Alerting
- Network fault ingestion: SNMP traps, NetFlow anomalies, and firewall/IDS alerts
- Bidirectional ITSM sync: ServiceNow, Jira Service Management, PagerDuty, and Opsgenie
Ch. 03
AI powered log analysis and pattern detection
Machine learning clusters millions of log lines into a handful of recurring patterns, then flags the ones that deviate from the historical baseline.
- Automatic log clustering and template extraction (log signature mining)
- Statistical and ML based anomaly detection on log volume, rate, and pattern mix
- Rare event and first occurrence surfacing for never before seen error signatures
- Natural language log search: ask questions instead of writing regex
Ch. 04
AIOps event correlation and noise reduction
The core of AIOps: collapsing a flood of alerts from a dozen disconnected monitoring tools into a small number of deduplicated, topology aware, prioritized incidents.
- Real time event deduplication and grouping across every connected monitoring tool
- Topology and dependency aware correlation: alerts on related services merge into one incident
- Alert storm and flap detection with dynamic suppression during known incidents
- Severity rescoring that blends event confidence with business service impact
Ch. 05
Root cause correlation engine
Correlates anomalous log patterns and deduplicated events across services, hosts, and time windows into a single causal narrative, not a wall of unrelated alerts.
- Cross service correlation of logs, events, and alerts aligned to distributed trace and span IDs
- Temporal correlation linking deploys, config changes, log spikes, and event bursts
- Causal chain visualization from symptom back to originating event
- Confidence scored root cause candidates ranked by evidence strength across both signal types
Ch. 06
Predictive AIOps and auto remediation
Moves from explaining what already happened to forecasting what is about to happen and, within approved guardrails, fixing it automatically.
- Predictive failure scoring from leading log pattern and event frequency indicators
- AI generated remediation playbooks mapped to recurring event and log signatures
- Approval gated auto remediation with full execution and rollback history
- Closed loop learning: outcomes of past remediations refine future recommendations
Ch. 07
Governance, retention and compliance
Enterprise grade controls for how long logs and events live, who can see them, and how the platform holds up under audit.
- Configurable hot / warm / cold retention tiers with automated lifecycle policies
- Field level PII redaction and masking at ingestion time
- Framework mapped audit trails: SOC 2, HIPAA, PCI DSS, GDPR, and ISO 27001
- Role based access control and per tenant log and event source isolation
Technical deep dive
Ingestion, noise reduction and remediation in depth
The seven areas below expand into full technical detail: every source, model, and workflow behind the platform, covering both AI driven log analysis and the AIOps event correlation core. Select a section to expand it.
Source coverage
- Infrastructure: Syslog, journald, Windows Event Log, and SNMP traps
- Cloud native: AWS CloudWatch Logs, Azure Monitor Logs, GCP Cloud Logging
- Containers and orchestration: Kubernetes pod, node, and control plane logs via DaemonSet collectors
- Application: structured JSON, Log4j/Logback, Python logging, .NET diagnostics, and custom agents
Ingestion pipeline
- Schema on write parsing across JSON, key value, CSV, and free text formats
- Configurable pattern library for legacy and proprietary log formats
- Buffered, back pressure aware collectors that survive downstream outages
- Real time and batch ingestion modes, selectable per source
Enrichment at ingest
- Automatic host, service, and environment tagging from CMDB and cloud metadata
- Trace and span ID extraction for cross referencing with distributed tracing
- GeoIP, user agent, and severity level enrichment for common log families
Monitoring and APM sources
- Prometheus/Alertmanager, Datadog, New Relic, and Dynatrace alert webhooks
- Cloud provider native alerts: AWS CloudWatch Alarms, Azure Monitor Alerts, GCP Alerting
- XpediOo AI Observability alerts ingested natively, with full topology context attached
Network and synthetic sources
- SNMP traps and NetFlow based anomaly alerts
- Firewall, IDS/IPS, and VPN security event feeds
- Synthetic transaction and uptime check failures from external probes
ITSM and ticketing sync
- Bidirectional integration with ServiceNow and Jira Service Management
- PagerDuty and Opsgenie incident and on call event ingestion
- Generic webhook connector for any tool that can post a JSON payload
Pattern mining and clustering
- Automatic log signature extraction: millions of lines reduced to hundreds of templates
- Dynamic reclustering as new log patterns emerge in production
- Template drift tracking to flag when a known pattern starts behaving differently
Anomaly detection
- Volume and rate anomaly detection per service, host, and log level
- Pattern mix anomaly detection: same volume, different composition
- Seasonality aware baselines (hourly, daily, weekly) to avoid false positives
- First occurrence alerts for signatures never seen in the training window
Search and exploration
- Natural language query interface: ask questions instead of writing regex or a query DSL
- Saved searches and shareable investigation links
- Field level faceted filtering across normalized log attributes
Deduplication and grouping
- Real time event deduplication across every connected monitoring and ITSM tool
- Topology and dependency aware grouping: alerts on related services merge into one incident
- Fingerprint based matching for near duplicate alerts with differing wording or IDs
Storm and flap control
- Alert storm detection with automatic suppression once a root incident is identified
- Flap detection to prevent unstable signals from retriggering the same alert repeatedly
- Dynamic suppression during acknowledged incidents and scheduled maintenance windows
Prioritization and routing
- Severity rescoring blending event confidence with business service impact
- Multichannel delivery: Slack, Microsoft Teams, PagerDuty, ServiceNow, email, and SMS
- On call schedule and escalation policy integration
Noise analytics
- Noise reduction dashboard tracking raw alert to incident compression ratio over time
- Mean time to acknowledge and mean time to resolve trending by service
- Alert rule tuning recommendations for chronically noisy sources
Correlation signals
- Distributed trace and span ID alignment across service boundaries
- Deploy and configuration change timeline overlay on log anomaly and event spikes
- Topology aware correlation using the live dependency graph from AI Observability, spanning both logs and events
Causal analysis
- Causal chain visualization from downstream symptom back to originating event
- Confidence scored root cause candidates ranked by supporting evidence
- Multi incident clustering when a single root cause produces several symptoms
Investigation workspace
- One click drill down from a correlated incident to raw underlying log lines and source events
- AI generated incident summary with a plain language explanation of the chain
- Exportable incident timelines for postmortems and audits
Predictive signals
- Failure likelihood scoring from leading log pattern and event frequency indicators
- Capacity and saturation forecasting derived from log volume and event rate trends
- Early warning alerts before a known failure signature fully manifests
Remediation playbooks
- AI generated remediation runbooks mapped to recurring incident signatures
- Library of prebuilt playbooks for common failure classes: disk, memory, connection pool exhaustion
- Custom playbook authoring with parameterized remediation steps
Governed automation
- Approval gated execution with a mandatory human sign off step by default
- Full execution and rollback history for every automated action
- Closed loop learning: remediation outcomes feed back into future recommendations
Retention and lifecycle
- Configurable hot / warm / cold retention tiers with automated tiering policies
- Per source retention overrides for regulatory or contractual requirements
- Immutable archive export to long term object storage
Privacy and access control
- Field level PII detection and redaction at ingestion time
- Role based access control scoped to log source, event source, service, and environment
- Per tenant log and event isolation for managed service provider and multi business unit deployments
Audit and compliance
- Framework mapped audit trails: SOC 2, HIPAA, PCI DSS, GDPR, and ISO 27001
- Immutable access and query logs for every user action on sensitive log and event data
- Scheduled compliance reports exportable for auditor review
Complete capability reference
Two engines, one AIOps story
AI Log Analysis and AI Event Correlation are XpediOo's two AI native surfaces. One reasons over raw log text, the other over the signals every other part of the platform produces. Together they form this platform's AIOps layer: the part of the system that doesn't just show data, but explains it, groups it, predicts it, and tells operators what to do next. Below is every high and medium impact capability on both surfaces (44 in total), each with a live product screenshot, what it does, and the business outcome it drives.
44 capabilities documented 20 high impact 24 medium impact
Less noise, less fatigue
False positive learning, alert noise correlation, and drift detection mean the system gets quieter and more accurate over time instead of paging the same person for the same nonissue every week.
Audit ready by default
PII masking, retention policies, SLA tracking, and compliance export are built into both engines natively, not bolted on, so security and compliance reviews are a formality rather than a project.
The foundation: detection and correlation
Every capability below is an extension of two foundational, always on engines. They are treated here as Critical / Foundational rather than scored High or Medium, since without them nothing downstream has data to work with.
AI Log Analysis
FoundationalAnomaly patterns
The default, foundational view of AI Log Analysis: AI detected anomaly patterns across every ingested log source, with severity, trend, and confidence for each one. Every high and medium impact Log Analysis capability in this document builds on the anomaly stream produced here.
AI Event Correlation (AIOps)
FoundationalCorrelations
The default, foundational view of Event Correlation: multi signal grouping across anomalies, Kubernetes events, remediations, deployments, and external alerts into a single correlated incident view. Every high and medium impact AIOps capability in this document builds on the correlation groups produced here.
AI Log Analysis
10 high impact capabilities, 11 medium impact capabilities
High impact capabilities
Cross service causality analysis
HighUses AI to trace how a failure in one service propagates into failures in dependent services, reconstructing the full chain from root cause to symptoms.
Business impact
Collapses hours of manual cross team log correlation into an automatically generated causal chain, dramatically shortening root cause analysis during multi service incidents.
AI Query (conversational log search)
HighA conversational interface for querying log data in plain English, with the AI translating questions into the underlying filters and returning direct answers grounded in the matching log entries.
Business impact
Makes log data accessible to engineers who aren't fluent in query syntax and speeds up ad hoc investigation for everyone else.
False positive learning
HighLearns from dismissal feedback which alert patterns are consistently false positives and automatically suppresses future recurrences of them.
Business impact
Directly reduces alert fatigue (the single biggest driver of ignored or delayed responses to real incidents) by keeping the noise to signal ratio in check over time.
Semantic anomaly deduplication (Smart Dedup)
HighUses semantic similarity, not just exact string matching, to cluster anomalies that are really the same underlying root cause reported multiple times, surfacing one canonical entry per cluster.
Business impact
Cuts through duplicate noise in incident lists and reporting, so headline metrics reflect the true number of distinct problems rather than an inflated raw event count.
AI shift handoff and digest report
HighGenerates an automatic AI written summary of anomalies, resolutions, and outstanding risks over a chosen period: an 8 hour shift, a day, or a week.
Business impact
Standardizes shift handoffs and management reporting, ensuring nothing falls through the cracks when responsibility changes hands, and gives leadership a digestible operational pulse.
Predictive anomaly forecasting
HighAI analyzes the historical cadence of recurring issues and predicts when the same pattern is statistically likely to reoccur, with a confidence score and a 24 hour forecast window.
Business impact
Shifts operations from purely reactive firefighting to proactive prevention, letting teams schedule fixes or capacity changes ahead of a predicted recurrence.
Log baseline and drift detection
HighEstablishes a statistical baseline of normal log behavior per source and continuously checks for gradual deviation in error rate, warning rate, or volume.
Business impact
Catches slow building degradations (a memory leak, a creeping error rate) that single anomaly detection would miss because no individual event looks alarming on its own.
Intelligent alert noise correlation
HighGroups alert firings that stem from the same underlying incident into a single correlated notification instead of firing one alert per rule per event.
Business impact
Prevents an on call engineer's phone from lighting up with twenty pages for one incident, reducing response fatigue and making the true blast radius obvious immediately.
SLA tracking
HighTracks the time from anomaly detection to resolution against configured response time targets per severity, flagging breaches as they approach or occur.
Business impact
Gives engineering leadership objective, real time visibility into whether operational commitments, internal or contractual, are being met before a customer has to ask.
PII masking and data redaction
HighAutomatically detects and masks sensitive personal data (emails, card numbers, SSNs, tokens) in log content before it is stored, displayed, or sent to an LLM.
Business impact
Reduces regulatory exposure (GDPR, PCI DSS, HIPAA) by ensuring sensitive data never lands unmasked in a system engineers routinely have broad access to.
Medium impact capabilities
AI Insights (pattern intelligence)
MediumEvery detected anomaly gets an AI writeup explaining what happened, why it's risky, and what's likely to break next, with no need to parse raw stack traces or correlate logs by hand.
Business impact
On call engineers and less technical stakeholders (support, leadership) reach a shared understanding of incident severity in seconds, speeding up triage and cross team communication.
Live stream
MediumA live, continuously updating feed of log entries as they are emitted by your infrastructure, filterable by source and severity level in real time.
Business impact
Teams can watch a deployment or incident fix take effect live instead of waiting on periodic refreshes, shortening validation time after a change.
Log search
MediumFull text and structured search across the entire retained log history, with filters for time range, source, severity, and tags, plus an AI assisted search mode.
Business impact
Root cause investigations that used to mean grepping through server files now take seconds, materially reducing incident resolution time and audit turnaround.
Log sources
MediumA managed inventory of every system, service, and environment feeding logs into the platform, with health status for each connection.
Business impact
Gaps in log coverage (a silent agent, a misconfigured shipper) are visible immediately, preventing blind spots that would otherwise hide real incidents.
Schedules
MediumConfigures anomaly scans to run automatically on a recurring cadence (hourly, daily, or custom) instead of requiring someone to trigger them manually.
Business impact
Detection coverage no longer depends on an engineer remembering to run a scan, which is critical for 24/7 operations and off hours incident coverage.
Alert rules
MediumDefines precise trigger conditions (thresholds, patterns, or combinations) that fire an alert the moment matching log activity occurs.
Business impact
Turns passive log storage into an active early warning system, so responders are notified proactively instead of discovering problems reactively.
Volume and trends
MediumTime series visualization of log throughput and severity mix across sources, surfacing volume spikes, dropoffs, and long term trend shifts.
Business impact
Capacity planning and anomaly detection both benefit: sudden volume changes are often the first visible sign of an outage, security event, or silent failure.
Run history
MediumA full audit trail of every anomaly detection run (when it executed, what configuration was used, and what it found) with run to run comparison.
Business impact
Gives compliance and engineering leadership a verifiable record that monitoring is actually happening consistently, and lets teams compare results run over run.
Field analytics
MediumExtracts structured fields (IP addresses, endpoints, response times, user IDs) out of unstructured log text using built in or AI suggested patterns, then aggregates them like a spreadsheet.
Business impact
Turns free text logs into queryable business and operational metrics, e.g. "top IPs causing 500 errors", without needing a data engineering project.
Retention
MediumDefines automated policies for how long log data of each type or source is retained before deletion, enforced without manual cleanup.
Business impact
Balances storage cost against compliance requirements (e.g. HIPAA, SOC 2 retention windows) automatically, removing a common source of audit findings and storage overruns.
Export and audit
MediumExports raw or filtered logs for external analysis, and reviews a compliance audit log of every retention and export action taken on the data.
Business impact
Satisfies auditor and regulator requests for evidence of data handling controls, and supports eDiscovery or forensic investigations without engineering involvement.
AI Event Correlation (AIOps)
10 high impact capabilities, 13 medium impact capabilities
High impact capabilities
Cross service causality analysis
HighSignals from anomalies, Kubernetes events, remediations, deployments, and external alerts within a 30 minute window across two or more resources are sent to the LLM for causal analysis, which identifies the root cause, how it propagated, and the downstream symptoms.
Business impact
Collapses hours of manual, cross team correlation work into an automatically generated causal chain. Chains below 60% confidence are discarded, and repeat triggers are suppressed for an hour to avoid noise.
AI Query (conversational event search)
HighA conversational interface for asking plain English questions about correlated events, grounded in the full real time signal feed plus every AI correlated group.
Business impact
Gives on call engineers immediate answers to the questions that come up in almost every incident review, such as "why did payment-service start alerting?", with no query to write.
False positive learning
HighEvery dismissal of a correlated group is a signal. Root cause patterns dismissed five or more times with a high false positive rate are automatically suppressed, so future groups are created as "Suppressed" instead of noisy "Active" alerts.
Business impact
Keeps the correlation engine self tuning: the patterns that page on call for nothing get quieter over time without anyone editing a rule.
Semantic correlation deduplication (Smart Dedup)
HighClusters alert correlation groups by semantic similarity of their root cause, turning "DB latency spike on db-server-01" and "db-server-01 experiencing high query latency" from different analysis runs into one canonical recurring root cause with a merged signal count.
Business impact
Headline noise reduction metrics reflect the true number of distinct operational problems, not an inflated count of redetected duplicates across runs.
AI shift handoff and digest report
HighGenerates a plain English operational briefing covering stability, correlated group trends, resource health, and recommended actions, ready for shift handoffs, executive reviews, or SOC 2 operational audits.
Business impact
Gives incoming shifts and leadership a consistent, digestible operational summary without anyone having to manually assemble one from raw alert history.
Correlation settings (weighted scoring)
HighTunable composite scoring across three strategies (Topology for the service dependency graph, Timing for the coincidence window, and Wording for message similarity) that determines how aggressively signals are grouped together.
Business impact
Lets operators tune correlation behavior to their environment: raise Topology weight where the dependency map is well maintained, or raise Wording weight in noisy, repetitive alert environments.
Event correlation baseline and drift detection
HighContinuously learns normal signal behavior per source type per hour of day and alerts when volume or critical signal rate deviates significantly from that learned baseline.
Business impact
Surfaces slow building shifts in alert behavior (a source quietly getting noisier or a critical rate creep) that a single correlation run would never flag on its own.
PII masking on event correlation
HighApplies the same shared masking rule set used by Log Analysis to every signal message before it reaches the correlation AI, with a live tester for event style content.
Business impact
Guarantees consistent data handling policy across both AIOps surfaces: one set of redaction rules, enforced everywhere signals reach an LLM, with no separate configuration to maintain.
SLA tracking
HighTracks acknowledgement and resolution windows for correlated event groups by severity, with an automatic breach checker and escalation tracking.
Business impact
Gives leadership the same objective, real time SLA visibility for cross system incidents that Log Analysis provides for individual anomalies.
Event sources
HighA live registry of every signal type feeding the correlation engine (anomalies, Kubernetes events, remediation actions, change and deployment events, and external webhook or polled integrations), each with a 7 day event count and health indicator.
Business impact
Gives operators one place to confirm every upstream signal pipeline is actually delivering before they trust the correlation results built on top of it.
Medium impact capabilities
Anomaly patterns (recurring signal shapes)
MediumSurfaces recurring signal shapes across the last 7 days (the same Kubernetes warning, anomaly type, or change repeatedly hitting the same resource), not just a single run's grouping.
Business impact
Highlights the chronic, slow burning issues that a one off correlation run would miss because no individual occurrence looks urgent.
AI Insights
MediumAn AI generated pattern intelligence summary drawn from recurring signal patterns across every correlation source, each with a severity tag and a plain English explanation.
Business impact
Gives responders a fast, prioritized reading list of what actually matters across the noise, without opening every individual correlation group.
Live stream
MediumA live, continuously updating feed of every signal type (anomalies, Kubernetes events, remediations, changes, external alerts, app server alerts), filterable by source as they occur.
Business impact
Lets an operator watch a live incident or a change rollout unfold signal by signal in real time, across every source type at once.
Event search
MediumFull text and structured search across every correlated signal, with filters for source type and severity and a breakdown of matches by category.
Business impact
Turns ad hoc "what happened with this resource last week" questions into a search of a few seconds instead of a manual sweep across multiple source pages.
Alert rules
MediumRules evaluated against correlated event groups every time analysis runs, matching on correlation type, severity, resource, or signal source rather than raw log text.
Business impact
Lets teams route notifications for specific correlation shapes (e.g. any critical severity group touching payment-api) without waiting on a human to notice it.
Volume and trends
MediumReal event throughput over time across all signal sources, with hourly bucketing and breakdowns by severity.
Business impact
Gives capacity and on call planning teams the same trend visibility over correlated events that Log Analysis provides over raw log volume.
Run history
MediumEvery "Correlate Alerts" analysis run over the last 30 days, with signal counts, new groups, flapping, suppression, and noise reduction percentage, plus the ability to diff two runs.
Business impact
Gives engineering leadership a verifiable, comparable record that correlation is running consistently and actually reducing noise run over run.
Field analytics
MediumReady made, one click analyses over real correlated signals: noisiest resources, critical only hotspots, top signal source types, most frequent event types, Kubernetes warning hotspots, and external alert hotspots.
Business impact
Gives on call engineers immediate answers to the questions that come up in almost every incident review, with no query to write.
Maintenance windows
MediumSignals whose resource and severity fall inside an active, scheduled maintenance window are suppressed before correlation runs.
Business impact
Known safe changes (a scheduled database maintenance, a planned patch window) don't spam the team with new alert groups.
Noise trends
MediumTracks noise reduction percentage and mean time to acknowledge/resolve (MTTA/MTTR) for correlated groups over time, across every historical "Correlate Alerts" run rather than just the latest snapshot.
Business impact
Lets leadership see whether the correlation engine's effectiveness is actually improving over time, not just what it looks like today.
Retention
MediumAutomated purge policies for correlated event groups past a configured compliance retention window, with a full retention audit trail.
Business impact
Extends the same storage cost and compliance balancing that Log Analysis retention provides to the correlation domain's own data.
Export and audit
MediumExports filtered correlated event groups as CSV, JSON, or a formatted PDF report, and maintains a compliance audit trail of every correlation run and export with PII masking counts.
Business impact
Gives auditors and compliance teams the same SOC 2 and GDPR ready evidence trail for cross system incident correlation that exists for raw log handling.
Topology aliases
MediumMaps a signal's resource identifier to its dependency map application name when automatic name normalization isn't enough: for example, a Kubernetes pod name versus a dependency map service name that differ structurally.
Business impact
Keeps topology weighted correlation accurate even when naming conventions differ across the systems feeding signals into the engine, instead of silently under correlating them.
Explainable AIOps
An AIOps layer that actually reasons
Most "AIOps" claims amount to a single anomaly detection model bolted onto a dashboard. XpediOo's AI Log Analysis and AI Event Correlation go considerably further: causal reasoning across services, conversational access to both raw logs and correlated incidents, self tuning false positive suppression, semantic deduplication, drift detection against learned baselines, and compliance grade retention, export, and PII handling. All of it is built natively into two pages that already share a detection and correlation core.
For operators
Fewer pages for the same incident, faster root cause attribution, and a conversational way to ask questions instead of writing queries.
For engineering leadership
Objective SLA visibility, run history you can audit, and a system that gets measurably quieter over time instead of noisier.
For compliance and security
PII masking and retention are enforced by default across both engines, with an audit trail ready for SOC 2, GDPR, or HIPAA review on request.
Appendix
Sources and workflows at a glance
Appendix
Supported log sources
| Domain | Coverage |
|---|---|
| Infrastructure and OS | Syslog, journald, Windows Event Log, and SNMP traps |
| Cloud native logs | AWS CloudWatch Logs, Azure Monitor Logs, and GCP Cloud Logging |
| Containers and orchestration | Kubernetes pod, node, and control plane logs via DaemonSet collectors |
| Application frameworks | Log4j/Logback, Python logging, .NET diagnostics, Node.js, and Go structured logging |
| Web and network | NGINX, Apache, HAProxy, load balancer, and firewall logs |
| Databases | PostgreSQL, MySQL, MS SQL, Oracle, and MongoDB slow query and error logs |
| Security | IDS/IPS, endpoint detection, VPN, and identity provider audit logs |
| Message queues and streaming | Kafka, RabbitMQ, and other broker operational logs |
Appendix: AIOps
Supported event and alert sources
| Domain | Coverage |
|---|---|
| Monitoring and APM | Prometheus/Alertmanager, Datadog, New Relic, and Dynatrace alert webhooks |
| Cloud native alerting | AWS CloudWatch Alarms, Azure Monitor Alerts, and GCP Alerting |
| Network and security events | SNMP traps, NetFlow anomalies, firewall/IDS/IPS, and VPN events |
| Synthetic and uptime checks | External probe and synthetic transaction check failures |
| ITSM and incident tools | ServiceNow, Jira Service Management, PagerDuty, and Opsgenie |
| Internal observability | Native ingestion of XpediOo AI Observability alerts, with full topology context |
At a glance
AI assisted workflows
| Capability | Primary question it answers |
|---|---|
| Log pattern mining | What are the handful of recurring patterns hiding inside millions of log lines? |
| Anomaly detection | Which log pattern just deviated from its historical baseline? |
| Event correlation and deduplication | Which of these alerts from five different monitoring tools describe the same underlying incident? |
| Alert storm suppression | How do we stop ten thousand alerts from one root cause from flooding every on call channel? |
| Root cause correlation | Which upstream event actually caused this cascade of symptoms? |
| Predictive failure scoring | How likely is this signature to escalate into an outage? |
| Auto remediation | Can this recurring failure be resolved automatically, within approved guardrails? |
| Natural language search | What happened, described in plain English instead of a query language? |
Why XpediOo AIOps & AI Log Management
Explainable AI, automation with guardrails
Most tools stop at either log search or alert routing. XpediOo AIOps & AI Log Management does both: it collapses alert storms into a handful of correlated incidents, mines log lines for the patterns that matter, and produces a ranked, evidence backed root cause. For recurring failure classes, it adds an automated fix that runs inside approval gates your team controls.
Every log and event, one pipeline
Infrastructure, cloud, container, application, network, and security logs, plus every alert fired by APM, network, synthetic, and ITSM tools, are ingested, normalized, and correlated in a single schema. No per tool silos, no per source noise reduction logic.
Noise reduction is the core, not a feature
AIOps event correlation runs continuously in the background, collapsing alert storms from a dozen disconnected tools into a handful of deduplicated, topology aware incidents before a human ever sees them.
Automation with guardrails
Auto remediation runs inside approval gates with full rollback history, so automated response earns operator trust instead of demanding it upfront.
Built to work with AI Observability
Topology and dependency data from XpediOo AI Observability, along with its native alert ingestion, feed directly into event correlation, so root cause analysis reasons over the same map your infrastructure team already trusts.
Get started
Stop chasing alerts. Start resolving incidents.
Walk through how XpediOo AIOps & AI Log Management ingests, correlates, and automatically remediates incidents hiding inside your logs and event storms.