Identifying possible reasons behind business issues
When a metric drops, an error rate spikes, or a process breaks down, teams often know something went wrong long before they know why. The Root Cause Analyzer investigates business issues across data, logs, tickets, and reports to identify and rank the most likely underlying causes.
In short
The Root Cause Analyzer investigates data, logs, tickets, and reports to identify and rank the most likely causes behind business issues, errors, and process failures.
- Client Type Enterprises and Teams Needing Faster Diagnosis of Business Issues
- Problem Business issues require manual cross-referencing across data, logs, and tickets
- Solution AI agent for evidence-based root cause investigation and ranked hypotheses
- Industry Cross-Industry — Operations, Finance, Support & Analytics Functions
Knowing something went wrong is easy. Understanding why is not.
When a metric drops, an error rate spikes, or a process breaks down, teams often know something went wrong long before they know why. Tracing an issue back to its actual cause usually means manually cross-referencing data, logs, and tickets across systems, a process that eats up time precisely when a fast response matters most. Organizations needed a way to investigate a business issue automatically and surface the most likely causes, with evidence, rather than leaving root cause analysis to manual detective work.
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Business issues require manual cross-referencing across multiple systems
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Relevant evidence can be scattered across data, logs, tickets, and reports
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Teams can surface many correlated factors without knowing which are most likely causes
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Root cause investigation consumes time when fast response matters most
What the Root Cause Analyzer had to achieve.
Automatically investigate a described issue and identify its most likely causes.
Draw on multiple sources of evidence: business data, logs, tickets, and reports.
Rank possible causes by likelihood rather than listing every correlated factor.
Tie each proposed cause back to the specific evidence that supports it.
Reduce the time between an issue surfacing and its cause being understood.
An evidence-driven AI agent that investigates issues, ranks possible causes, and makes every finding checkable.
Flexible evidence gathering
The agent draws on whatever the client has: spreadsheets and databases, connected BI platforms, system logs, or support ticket data.
Correlation and anomaly analysis
The agent identifies what changed around the time the issue began, surfacing metrics, events, or patterns that plausibly relate to it.
Evidence-linked reasoning
Each proposed cause is tied to the specific data, logs, or events that support it, so findings can be checked rather than taken on faith.
Ranked cause hypotheses
Rather than a single guess, the agent presents multiple possible causes ranked by likelihood, so investigation can start with the most probable explanation.
Plain-language summary
Findings are delivered as a clear explanation of what likely happened and why, alongside the supporting evidence.
Four specific investigation challenges, four specific fixes.
Correlation is not causation
Factors that move together aren't necessarily related.
Ranked hypotheses with evidence
We had the agent present findings as ranked hypotheses with supporting evidence, rather than asserting a single definitive cause.
Drawing on scattered evidence
Relevant evidence is often split across systems.
Unified evidence gathering
We built the agent to pull from data, logs, and tickets together, rather than analyzing each source in isolation.
Avoiding an overwhelming list of possibilities
Too many surfaced factors can be as unhelpful as too few.
Likelihood-based ranking
We ranked causes by likelihood so the most probable explanations are presented first.
Keeping findings checkable
A proposed cause is only useful if it can be verified.
Evidence-linked hypotheses
We anchored every hypothesis to the specific evidence that generated it.
"The agent presents findings as ranked hypotheses with supporting evidence, rather than asserting a single definitive cause."
From manual detective work to evidence-based diagnosis.
Faster diagnosis of business issues without manual cross-referencing.
Works with existing data, logs, and ticketing systems without new infrastructure.
Multiple ranked hypotheses give teams a clear starting point for investigation.
Traceable evidence supports confident, well-founded next steps.
Faster path from surfaced issue to understood cause.
The Root Cause Analyzer turns a manual, time-consuming investigation into a fast, evidence-based diagnosis of business issues, drawing on data, logs, and tickets wherever they live. By combining correlation and anomaly analysis, ranked cause hypotheses, and evidence-linked reasoning, the agent shortens the path from a surfaced issue to an understood cause, making it a practical tool for operations, support, and analytics teams.
Common questions about the Root Cause Analyzer.
Find quick answers to the most common questions about this AI-powered root cause investigation capability.
How does the Root Cause Analyzer investigate a business issue?
The agent investigates a described issue by drawing on available business data, logs, tickets, reports, and other connected sources, then identifies changes, anomalies, and patterns that plausibly relate to when the issue began.
Can it work with data from multiple systems?
Yes. The agent can draw on whatever the client has, including spreadsheets and databases, connected BI platforms, system logs, or support ticket data, bringing scattered evidence together for investigation.
Does the agent identify one definitive cause?
Rather than asserting a single definitive cause, the agent presents multiple possible causes ranked by likelihood. Each proposed cause is tied to supporting evidence so teams can begin investigation with the most probable explanation while recognizing that correlation is not necessarily causation.
How are the proposed causes verified?
Every proposed cause is anchored to the specific data, logs, or events that generated the hypothesis. This evidence-linked reasoning makes findings checkable rather than requiring teams to take the analysis on faith.
What types of teams can use the Root Cause Analyzer?
The Root Cause Analyzer is designed for operations, support, finance, analytics, and other business teams that need faster diagnosis of issues across data, logs, tickets, and reports.
Spending too much time finding out why something went wrong?
Our AI architects can map your data, logs, tickets, and reporting sources into an evidence-based root cause investigation workflow — starting with the business issue you need to understand.
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