Specialized AI agents work in parallel, find the root cause, present evidence and propose the fix.
How it works
Read-only, minimal permission, certified. The rest happens in parallel.
+200 native integrations — Sentry, Datadog, GitHub, your databases. Read-only by default.
Logs, metrics, code, database, infra and docs. Each with its own scope and tools.
Full reasoning, collected evidence, proposed fix. You approve.
Two products, one framework
Production bug or customer who can't issue an invoice — the investigation is structured the same way.
Datadog, Sentry or PagerDuty alerts become structured investigations. Root cause tied to the exact commit.
L2/L3 support receives root cause with evidence — no pinging engineering for every ticket.
Your team's time
Engineers reviewing logs and dashboards is engineers not building product.
From alert to diagnosis with evidence — in parallel.
Each investigation ends with a proposed fix, not a "maybe look here".
Chat, API, Slack and dashboard — drop an incident in, get root cause back. Same brain, any surface.
Use cases
Alert investigation
CauseFlow classifies every event. High or critical automatically opens an investigation — low goes to the human review feed.
Event received
sentry.exception · pricing-service
Severity classification
based on blast radius, affected users, SLO
Investigation opened · 6 agents in parallel
logs, metrics, code, db, infra, docs
Fix proposed
requires human approval before executing
Notifications
Slack, email or Teams — with the reasoning, evidence and proposed fix. One click to approve.
🔍 INV-2847 · root cause identified
Stale CDN cache — CMS invalidation webhook failing with 403. Proposed fix: manual purge + secret rotation. 94% confidence.
INV-2844 · broken images
CORS policy reverted. 14 assets serving normally. Incident closed automatically — audit trail at /incidents/2844.
fix(payments): restore pool.release() in error branch
PR opened with regression test. Error branch coverage: 0% → 100%. Reviewers assigned automatically.
Knowledge bank
CauseFlow doesn't just plug into your tools and uncover the root cause. It becomes a knowledge bank about your company and products — learning from every incident and helping you make better decisions.
Paranoid by design
Read-only by default. The agent works alongside you, asks for clarification mid-investigation, and never writes without your sign-off.
The agent reads data only during an active investigation. After analysis, data is discarded. We don't store raw customer data.
Data read on demand, analyzed in-memory, discarded after investigation completion.
Every integration uses read-only credentials with the minimum scope via OAuth. The agent never has write access unless explicitly authorized.
OAuth scopes limited to read-only; write access requires explicit human approval.
The agent is read-only. Remediation actions require explicit user approval (human-in-the-loop) before any destructive action.
All write operations gated behind a human-in-the-loop approval flow.
Each customer has individual KMS encryption via AWS. Data is never mixed between customers. LLM calls contain data from exactly one tenant.
Per-tenant KMS keys, isolated ECS tasks, single-tenant LLM contexts.
Customer data is never used to train models for other customers. Fine-tuning is exclusive per account when applicable.
Zero cross-customer data sharing; per-account fine-tuning isolation.
Each investigation generates a detailed log in S3 with Object Lock (WORM). The log is visible to the customer and cannot be altered.
S3 Object Lock (WORM); includes sources accessed, data read, tokens processed, result.
Connect your stack in minutes and investigate real incidents on your infra. No contract, no card.
Datadog
PagerDuty
Sentry
AWS CloudWatch
Grafana
New Relic
Splunk
KubernetesDatadogPagerDutySentryAWS CloudWatchGrafanaNew RelicSplunkKubernetes
Slack
GitHub
Jira
Linear
Notion
HubSpot
Salesforce
GitLab
Confluence
Microsoft Teams
ServiceNowSlackGitHubJiraLinearNotionHubSpotSalesforceGitLabConfluenceMicrosoft TeamsServiceNowMemory leak in connection pool — auto-scaling triggered at 3:42 AM caused PostgreSQL max_connections overflow.
