Causely

Visibility42

Vibe100

AI Visibility & Sentiment

Causely

Causely is a Causal AI platform that helps engineering teams achieve continuous application reliability by instantly identifying the root cause behind system symptoms. The platform creates live causal models that enable teams to quickly diagnose and resolve incidents in complex microservices environments.

Active Monitoring

causely.io

Software

AI Visibility Score

42/100

Moderate

Sentiment Score

100/100

AI Perception

Summary

Causely has successfully captured a dominant 80% visibility rate among AI-First Technical Researchers, yet it remains critically invisible in the high-intent 'alternatives to legacy' conversations that drive enterprise displacement. While Claude and Gemini champion the brand in causal AI queries, its 18% presence in Google AI Overviews indicates a failure to capture the broad-market search traffic currently monopolized by incumbents like Dynatrace and Datadog.

Value Proposition

Unlike traditional observability tools that only show symptoms, Causely uses Causal AI to instantly reveal the single root cause behind incidents, dramatically reducing mean time to resolution and preventing revenue loss.

Overview

Mission

Enabling teams who can't afford downtime to achieve continuous application reliability through Causal AI.

Products & Services

Causal AI PlatformLive Causal ModelingRoot Cause AnalysisIncident PreventionTelemetry Intelligence

Agent Breakdown

AI Platforms

How often do different AI platforms reference Causely?

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Conversation Analysis

Topics

What conversations is Causely included in — or excluded from?

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Buyer Personas

Personas

Who does each AI platform recommend Causely to, and when?

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Programmatic Testing

Sample Conversations

We programmatically analyze questions that real customers are asking to AI agents and chatbots, extract brand mentions and sentiment, analyze every response, and synthesize the data into an action plan to increase AI visibility.

ChatGPT

Claude

Gemini

AI Overviews

Accelerating Incident Resolution And RCA(3 queries)

“how can I find the root cause of microservices failures faster, suggest some specific tools”

0/4 platforms mentioned

ChatGPT

1.Jaeger

2.Zipkin

3.Datadog APM

4.New Relic APM

5.ELK Stack

+20 more

Claude

1.Jaeger

2.Zipkin

3.Tempo

4.Grafana

5.AWS X-Ray

+22 more

Gemini

1.Jaeger

2.Honeycomb

3.AWS X-Ray

4.Lambda

5.ECS

+19 more

AI Overviews

1.New Relic

2.Dynatrace

3.Davis

4.Instana

5.IBM

+10 more

“help me automate RCA for Kubernetes clusters where Datadog only shows the symptoms”

1/4 platforms mentioned

ChatGPT

1.Datadog

2.Kubernetes

3.Terraform Cloud

4.ArgoCD

5.Helm

+21 more

Claude

1.Datadog

2.Botkube

3.Slack

4.Robusta

5.Coroot

+11 more

Gemini

1.Kubernetes

2.Datadog

3.Komodor

4.Slack

5.Botkube

+6 more

AI Overviews

1.Datadog

2.Watchdog RCA

3.Kubernetes

4.Bits AI

5.Slack

6.Causely

“what's the best way to use causal AI for incident response in a complex cloud environment”

2/4 platforms mentioned

ChatGPT

1.Chronosphere

2.Dynatrace

3.Davis AI

4.BigPanda

5.Slack

+7 more

Claude

1.Dynatrace Davis AI

2.Causely

3.Zebrium

4.Sumo Logic

5.Coralogix RCO

+8 more

Gemini

1.Datadog

2.Slack

3.Causely

4.Dynatrace

5.Davis AI

+2 more

AI Overviews

1.Databricks

2.IBM

Modernizing The Observability Stack(2 queries)

“I'm tired of manual telemetry analysis, are there platforms that do live causal modeling automatically”

4/4 platforms mentioned

ChatGPT

1.Causely

2.Dynatrace

3.Davis AI Engine

4.BayesiaLab

5.New Relic

+9 more

Claude

1.Dynatrace

2.Davis AI

3.Causely

4.Prometheus

5.OpenTelemetry

+10 more

Gemini

1.Causely

2.Dynatrace

3.Davis AI

4.Davis

5.Smartscape

+9 more

AI Overviews

1.Causely

2.OpenTelemetry

3.Dynatrace

4.ScienceLogic

5.Skylar Automated RCA

+6 more

“suggest a tech stack for an SRE team that wants to move beyond standard dashboards and metrics”

0/4 platforms mentioned

ChatGPT

1.Prometheus

2.Mimir

3.Thanos

4.Datadog

5.Grafana

+24 more

Claude

1.Grafana

2.Mimir

3.Loki

4.Tempo

5.Datadog

+17 more

Gemini

1.Honeycomb

2.Datadog

3.OpenTelemetry

4.Grafana

5.Tempo

+12 more

AI Overviews

1.Sherlocks.ai

2.Slack

3.Jira

4.Agent0

5.Dash0

+11 more

Reliability Platform Evaluation & Trust(3 queries)

“best reviewed alternatives to New Relic and Dynatrace for enterprise incident management”

0/4 platforms mentioned

ChatGPT

1.New Relic

2.Dynatrace

3.Gartner

4.Forrester

5.G2

+21 more

Claude

1.New Relic

2.Dynatrace

3.Datadog

4.AWS

5.Azure

+19 more

Gemini

1.New Relic

2.Dynatrace

3.Datadog

4.Splunk Observability Cloud

5.SignalFx

+28 more

AI Overviews

1.New Relic

2.Dynatrace

3.Datadog

4.Splunk

5.SignalFx

+17 more

“who are the most trusted providers in the causal AI and AIOps space right now”

3/4 platforms mentioned

ChatGPT

1.Dynatrace

2.Davis AI

3.Smartscape

4.Causely

5.IBM Instana

+10 more

Claude

1.Moogsoft

2.Dell

3.BigPanda

4.Slack

5.Dynatrace

9.Causely

+9 more

Gemini

1.BigPanda

2.Slack

3.PagerDuty

4.Moogsoft

5.Dell

8.Causely

+7 more

AI Overviews

1.Dynatrace

2.Davis® AI

3.IBM

4.IBM Cloud Pak for AIOps

5.Instana

+16 more

“what should I look for when evaluating automated root cause analysis software”

2/4 platforms mentioned

ChatGPT

1.Slack

2.Kubernetes

3.Nobl9

4.Dynatrace Davis AI

5.BigPanda

+10 more

Claude

1.Slack

2.PagerDuty

3.Jira

4.ServiceNow

5.Causely

+6 more

Gemini

1.Datadog

2.New Relic

3.Slack

4.Causely

5.Shoreline.io

+5 more

AI Overviews

1.Lakeside Software

2.ScienceLogic

3.ServiceNow

4.Fabrico

5.Reliability Center Inc.

+1 more

Proactive Incident Prevention(2 queries)

“how can my team stop incidents before they happen using telemetry intelligence, what tools should we buy”

0/4 platforms mentioned

ChatGPT

1.OpenTelemetry

2.Datadog

3.Watchdog AI

4.Dynatrace

5.Davis AI

+22 more

Claude

1.Datadog

2.Dynatrace

3.Davis AI

4.New Relic

5.Honeycomb

+33 more

Gemini

1.Datadog

2.Watchdog

3.Dynatrace

4.Davis

5.New Relic

+15 more

AI Overviews

1.Riverbed

2.Xurrent IMR

3.PagerDuty

“ways to achieve continuous application reliability in a microservices setup, specific brand recommendations please”

0/4 platforms mentioned

ChatGPT

1.Datadog

2.New Relic

3.Grafana Stack

4.Grafana

5.Prometheus

+43 more

Claude

1.Grafana

2.Prometheus

3.Datadog

4.Loki

5.Tempo

+15 more

Gemini

1.Honeycomb

2.Datadog

3.Grafana Cloud

4.LGTM Stack

5.Prometheus

+15 more

AI Overviews

1.TechBlocks

2.Datadog

3.Prometheus

4.Grafana

5.Kubernetes

+16 more

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

High authority among AI-First Technical Researchers with an 80% mention rate and strong positive sentiment.

Strength

Exceptional performance on Gemini (avg pos 5.4) and Claude (71% mention rate) for modern observability queries.

Strength

Consistently ranked #1 across all platforms for direct brand vibe checks, indicating a clear and well-defined brand identity.

Gap

Total absence in 'Proactive Incident Prevention' queries, missing a key SRE pain point regarding incident avoidance.

Gap

Frequent failure to appear in competitive displacement searches specifically targeting alternatives to New Relic and Dynatrace.

Gap

Significant underperformance in Google AI Overviews (18% mention rate) compared to its presence in standalone LLMs.

Opportunity

Capture the Burnout-Averse SRE Manager persona by pivoting messaging from theoretical Causal AI to practical operational relief.

Opportunity

Leverage strong Claude and Gemini sentiment to influence mid-funnel evaluation criteria for automated RCA platforms.

Opportunity

Bridge the 'Causal AI' terminology with 'Automated RCA' to win broader, less academic incident response queries.

Technical Health

Site Health for AI Visibility

How well Causely's website is optimized for AI agent discovery and comprehension.

94/100

19 passed 2 warnings

Audited 2/27/2026

Crawlability96

Can AI bots find your pages?

Technical96

SSL, mobile, doctype basics

On-Page SEO93

Titles, descriptions, headings

Content Quality87

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG100

Open Graph, Twitter cards

AI Readability60

How well AI can parse your content

Warnings

3 render-blocking resource(s) detected

Consider deferring or async-loading non-critical scripts and stylesheets.

Title is too short (7 characters)

Expand the title to 50-60 characters with descriptive keywords.

Want a full technical audit with AI-specific recommendations?

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Brand Identity

Brand Voice & Style

How AI perceives Causely's communication style and personality

Causely communicates with confident technical authority while remaining accessible to engineering professionals. The brand voice is direct and solution-focused, emphasizing clarity over complexity. There's an underlying sense of urgency that resonates with teams who understand the cost of downtime, balanced with reassurance that their AI-powered approach delivers results. The tone is modern, intelligent, and pragmatic—speaking peer-to-peer with technical decision-makers.

Core Tone Traits

Technically Authoritative

Speaks with deep expertise in observability, AI, and distributed systems

Solution-Focused

Cuts through noise to highlight clear outcomes and value

Confidently Direct

Makes bold claims backed by technical substance

Urgency-Aware

Understands the stakes of downtime and speaks to teams who can't afford it

Competitive Landscape

Related Ecosystem

Related products and services that AI mentions in conversations alongside or instead of Causely

Dynatrace98 mentions

Datadog95 mentions

BigPanda77 mentions

Causely75 mentions

OpenTelemetry69 mentions

Prometheus67 mentions

Honeycomb58 mentions

Grafana54 mentions

New Relic54 mentions

Kubernetes53 mentions

PagerDuty51 mentions

Source Intelligence

Citations

Sources that AI assistants cite. Getting featured here improves visibility.

Causely

https://www.causely.ai/

Referenced in 1 query

Review

Dynatrace

https://www.dynatrace.com/

Referenced in 1 query

Review

BayesiaLab

https://www.bayesia.com/

Referenced in 1 query

Review

New Relic

https://newrelic.com/

Referenced in 1 query

Review

Datadog

https://www.datadoghq.com/

Referenced in 2 queries

Review

Splunk ITSI

https://www.splunk.com/

Referenced in 2 queries

Review

PyWhy

https://www.pywhy.org/

Referenced in 1 query

Review

Amazon Lookout for Metrics

https://aws.amazon.com/lookout-for-metrics/

Referenced in 1 query

Partner

Observability Solutions | ScienceLogic

https://sciencelogic.com/articles/observability-solutions

Referenced in 1 query

Review

Observability - ScienceLogic

https://sciencelogic.com/articles/observability

Referenced in 1 query

Review

Causely Announces OpenTelemetry Integration: Solving ...

https://www.youtube.com/watch?v=GsoUIckMPCw&t=18

Referenced in 1 query

Pitch Story

Top 8 Cloud Observability Tools in 2026 | CloudQuery Blog

https://www.cloudquery.io/learning-center/cloud-observability-tools

Referenced in 1 query

Review

Content Engineering

Goals & Content Ideas

Ideas to help AI agents better understand the business and be more likely to use Causely's resources to help users.

Dominate Legacy Alternative Comparison Conversations

Address the critical gap where Causely is ignored in direct comparison queries against Dynatrace and New Relic. Deploy targeted comparison content that highlights Causely's Causal AI advantage over traditional observability tools, ensuring AI assistants include us in evaluation-phase recommendations. Amplify through social campaigns featuring head-to-head capability breakdowns and migration success stories.

Why Your Observability Stack Shows Symptoms While Causely Reveals the Actual Root Cause

The Hidden Cost of Alert Fatigue: What Dynatrace and New Relic Won't Tell You

From 45-Minute MTTR to 3 Minutes: A Real Migration Story from Legacy Observability

5 Questions to Ask Before Renewing Your Dynatrace or New Relic Contract

Causal AI vs Correlation: Why Traditional APM Tools Keep You Firefighting

Optimize Technical Content for AI Overviews

Tackle the 18% AI Overviews mention rate by restructuring technical documentation with structured data and concise How-to RCA schemas optimized for Google's AI extraction. Create easily digestible, schema-marked content that answers common root cause analysis queries directly, improving organic discovery among mainstream engineering audiences searching for incident resolution guidance.

The Complete Guide to Kubernetes Root Cause Analysis in Under 5 Minutes

How to Implement Automated RCA for Microservices: A Step-by-Step Framework

Root Cause Analysis Checklist: 7 Steps Every SRE Should Follow During Incidents

What Is Causal AI and How Does It Transform Incident Response?

Troubleshooting Microservices: A Decision Tree for Faster Root Cause Identification

Own the Proactive Reliability Narrative

Capture the untapped opportunity in 'Proactive Incident Prevention' and 'Continuous Reliability' query categories where Causely currently has zero mentions. Develop authoritative content pillars that position Causely as the solution for SRE leaders ready to move beyond reactive firefighting, using social proof and thought leadership to establish category ownership.

The Shift from Reactive to Proactive: What Continuous Reliability Actually Looks Like

Why the Best Incident Is the One That Never Happens: Proactive Prevention Strategies

Building a Culture of Continuous Reliability in Complex Microservices Environments

3 Warning Signs Your Team Is Stuck in Reactive Mode and How to Break Free

Proactive Incident Prevention: The Metrics That Actually Matter for SRE Teams

Boost ChatGPT Visibility Through Technical Repositories

Address the significant ChatGPT visibility gap where mention rates lag at 46% compared to other AI assistants. Seed technical case studies, Kubernetes-specific RCA examples, and detailed implementation guides into AI-accessible repositories like GitHub, Stack Overflow, and developer documentation hubs that ChatGPT references for technical recommendations.

Real-World Kubernetes RCA: How One Team Reduced Incident Resolution Time by 90%

Open Source RCA Patterns: Lessons from Production Microservices Failures

Debugging Kubernetes at Scale: A Practitioner's Guide to Causal Analysis

The Anatomy of a Production Incident: From Symptom Alert to Root Cause in Minutes

Common Kubernetes Failure Modes and How Causal AI Identifies Them Instantly

Content Engineering

Recommended Actions

Deploy a targeted 'Legacy Alternative' content strategy specifically comparing Causely to Dynatrace and New Relic.

Causely is currently ignored in direct comparison queries, allowing incumbents to retain market share by default during the evaluation phase.

Impact: High

Optimize technical documentation for Google AI Overviews using structured data and concise 'How-to' RCA schemas.

The 18% mention rate in AI Overviews is the brand's biggest bottleneck for organic discovery among mainstream users.

Impact: High

Develop specific content pillars around 'Proactive Incident Prevention' and 'Continuous Reliability' in microservices.

These query categories returned zero mentions, representing a massive untapped opportunity to reach SRE leaders looking to move beyond reactive fire-fighting.

Impact: Medium

Increase mention frequency on ChatGPT by seeding technical case studies and Kubernetes-specific RCA examples into AI-accessible repositories.

ChatGPT lags significantly behind Claude and Gemini in both mention rate (46%) and average position (12.9), requiring more direct data influence.

Impact: Medium

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