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Measure it. Prove it. Tell the customers it hit.

Observer reads the metrics you already collect, keeps a measured record of every incident and tells the customers it affected. Your AI SRE finds the cause. Observer proves the impact.

Measure

Status from your own metrics.

An agent inside your network reads the numbers your engineers already trust and sends out one value and its status per check.

Prove

A record your customers can verify.

The same measurements become SLA attainment per customer, signed statements and the numbers leadership reads.

Tell

The right customers, in time.

Status pages for every audience, incidents scoped to the customers they hit, and the channels your team already watches.

Developers

Status as code.

Config lives in your repo and ships from CI. Everything else goes through the REST API or MCP.

YAML configobserver.yaml

apiVersion: observer/v1
metrics:
- key: checkout-error-rate
title: Checkout error rate
source_type: prometheus
source_config:
query: >-
100 * sum(rate(http_requests_total{job="checkout",code=~"5.."}[5m]))
/ sum(rate(http_requests_total{job="checkout"}[5m]))
thresholds:
healthy_operation: under
healthy_value: 1 # under 1% is healthy
unhealthy_operation: over
unhealthy_value: 5 # over 5% is unhealthy

CLIPlan, apply, and export what you already have.

$ observer apply -f observer.yaml --dry-run

GitHub ActionPlans on pull requests, applies on merge.

- uses: useobserver/cli@v1

REST APIEverything under /api/v1, described by OpenAPI.

$ curl https://use.observer/api/v1/slos \
-H "Authorization: Bearer $OBSERVER_API_KEY"

MCP serverOne tool per API operation, under your key's scopes.

https://mcp.use.observer/mcp

Set up with your AI agentPaste into Claude Code, Cursor or Codex. You approve one link; the agent does the rest.

Set up Observer for me. Instructions: https://use.observer/llms.txt

Upserts by key. Console edits to managed objects show as drift.

Connect one metric. See its record in ten minutes.