The first AI agent bridging incident investigation and customer issue resolution

Investigate production incidents in minutes, not hours

Most incident resolution time is investigation, not fixing. CauseFlow deploys multiple AI agents in parallel — root cause in ~3 minutes, end-to-end resolution in ~30 minutes vs. 2–4 hours. New agents are continuously added (documentation, runbooks, and more).

Now in beta — limited spots available for teams that want to co-create the product.

Root Cause Identified97% confidence

Memory leak in connection pool — auto-scaling triggered at 3:42 AM caused PostgreSQL max_connections overflow.

Connects to the tools your team already uses

Datadog

PagerDuty

Sentry

AWS CloudWatch

Grafana

New Relic

Splunk

Kubernetes

Datadog

PagerDuty

Sentry

AWS CloudWatch

Grafana

New Relic

Splunk

Kubernetes

Slack

GitHub

Jira

Linear

Notion

HubSpot

Salesforce

GitLab

Confluence

Microsoft Teams

ServiceNow

Slack

GitHub

Jira

Linear

Notion

HubSpot

Salesforce

GitLab

Confluence

Microsoft Teams

ServiceNow

Compliance & Certifications

LGPDGDPRSOC 2 Type IIISO 27001HIPAA

The Cost of Manual Investigation

2-4 hrs → ~30 min

Manual investigation time vs. CauseFlow end-to-end — including human approval

AI investigation: ~3 min. Human approval: up to 25 min. Fix execution: ~2 min.

$9,000/min

Average cost of downtime for Global 2000 companies

Splunk / Oxford Economics, 2024

43%

Of engineering teams spend too much time responding to alerts

Splunk State of Observability, 2025

How CauseFlow investigates your problems

Multiple specialized agents investigate in parallel — from alert to root cause in ~3 minutes. More agents added continuously.

Problem received — 4:07 AM

Via web interface, Slack, Jira, or API. CauseFlow classifies severity and begins investigating immediately.

Multiple agents investigate in parallel

Specialized agents analyze logs, metrics, infrastructure, deployments, code, and databases simultaneously — with new agents (documentation, runbooks, and more) being added continuously.

Root cause identified — confidence 91%

Multiple independent data sources converge on a single root cause with a confidence score and event timeline.

Exact fix proposed — human approves

You see the exact change, affected services, and estimated impact. Nothing executes without your approval.

Pattern learned — next time: under 2 minutes

The pattern is added to the Knowledge Base. Next occurrence resolves in under 2 minutes automatically.

Tamper-evident audit trail

Every agent action is recorded: sources accessed, data read, and decisions made. Full accountability.

Free on All Plans

Choose Your Deployment Mode

Same AI. Same root cause. You decide how your data flows.

Connected Mode

Direct read-only access to your systems via AWS IAM Role or OAuth2. Fastest setup, real-time analysis.

Read-only access — no write permissions required
Real-time analysis across all connected tools
Fastest investigation speed
Full audit trail of every access
All integrations supported

Included in all plans

Privacy-Enhancing Technology

Privacy-Preserving Mode

A lightweight Docker agent runs inside your infrastructure — on-premise or private cloud. It processes, masks, and anonymizes all sensitive data (PII, API keys, debug logs) before anything leaves your environment. This is edge-based data minimization aligned with GDPR Article 25 and LGPD — a Privacy-Enhancing Technology (PET) that lets your team use AI-powered investigation without exposing sensitive production data.

Raw data never leaves your environment
All identifiers automatically anonymized
Customer-controlled masking rules
Same AI-powered root cause analysis
GDPR Article 25 & LGPD compliant data minimization
Reversible mapping stays in your infrastructure

Included in all plans

Why Now?

Two converging forces are making automated incident investigation essential.

AI-Generated Code Creates More Incidents

41% of all code is now AI-generated. Incidents per PR are up 23.5% year-over-year. More code shipped faster means more production problems to investigate.

CodeRabbit, Dec 2025 Cortex Benchmark, 2026

Engineers Spend More Time on Repetitive Ops, Not Less

Despite billions invested in DevOps tooling, time spent on repetitive operational tasks rose from 25% to 30% of engineering time in 2025 — the first increase in five years. Current tools optimize workflows, but nobody automates the investigation itself.

Catchpoint SRE Report, Jan 2025

Why CauseFlow Exists

Current incident tools optimize the wrong part of the problem. Here's what they miss — and what we built to fix it.

Tools Optimize the Wrong 20%

Most incident resolution time is technical investigation — not coordination. Current tools automate coordination. Nobody automates the investigation itself. CauseFlow does.

Recurring Issues Resolve in Under 2 Minutes

First investigation: root cause in ~3 minutes. Second occurrence: pattern matched, same fix proposed in under 2 minutes. Your institutional knowledge stops living in engineers' heads.

The average incident investigation takes 2–4 hours across fragmented tools.

Nobody Bridges SRE and Customer Support

When a customer reports a problem, CauseFlow investigates it exactly like a monitoring alert — same AI, same depth. Root cause and customer-facing explanation generated together.

Works With Whatever You Have Connected

No GitHub? Code analysis is skipped — not broken. Start with one tool and add more over time. CauseFlow never fails because of a missing integration.

Your Reputation Grows With Your Response Time

Slow incident response means lost trust and reputation damage. CauseFlow gives teams of 2–50 engineers enterprise-grade investigation without enterprise pricing.

Platform Companies Don't Compete at the Application Layer

AWS didn't build Datadog. Google didn't build PagerDuty. AI platform companies provide the intelligence layer — we build the solution.

Benedict Evans, Feb 2026

One Pipeline. Two Problem Sources.

SRE alert at 4 AM: checkout returning 500s. CauseFlow investigates — root cause in minutes. Customer report at 9 AM: "exports aren't working." Same AI investigates. Root cause identified, customer explanation generated. Both solved by the same pipeline.

Root Cause Found87% confidence

Deployment v2.4.1 introduced a race condition in the data sync service, causing intermittent record deletion.

Use CauseFlow in the way that makes most sense for your team

Via API

Integrate CauseFlow into your alerting pipeline. Trigger investigations via REST API with result webhooks.

Via Web Interface

Access the dashboard, describe the problem, and follow the investigation in real-time with visible audit trail.

Coming Soon

Via Slack

Send a message in Slack describing the problem and the agent investigates right in the incident channel. Results appear as a thread.

Coming Soon

Via Jira/Trello

Assign a card to CauseFlow and it investigates automatically. The final report becomes a comment on the card.

Coming Soon

Via MCP Server

Use CauseFlow as an MCP tool inside your AI coding agent or IDE.

Coming Soon

Via AI Agent

Let the CauseFlow agent work autonomously within your workflow.

Enterprise Security Without Enterprise Complexity

On-demand reading

The agent reads data only during investigation, then discards it. Privacy-Preserving Mode available on all plans.

Isolated encryption

Each tenant has individual KMS encryption via AWS.

Immutable audit trail

Every agent action is logged and visible to the customer.

No external training

Your data is never used to train third-party models.

Read-only by default

The agent never writes without explicit approval (human-in-the-loop).

Right to deletion

Delete all your data at any time.

SOC 2 Type II in progress. ISO 27001 and HIPAA on roadmap.

LGPD CompliantGDPR Compliant

Learn more about Security

Stop losing engineering hours to incident investigation

Join the beta — investigate incidents with AI while your team focuses on building.