Data Is Mist

Incident intelligence for cloud teams

Know what changed before you touch production.

Data Is Mist connects deployments, traces, metrics, and dependencies into an investigation engineers can inspect—not another answer they have to trust blindly.

Talk to the builder See an example

Pre-launch · Looking for design partners

INC-042Investigating

Checkout errors

P1
  1. Deploymentpayment-service / release 8f21a
  2. Latency movedpayment p99 · 220ms → 1.8s
  3. Customer impactcheckout 5xx crossed 4.2%
Working hypothesis

The payment release is the first relevant change before downstream timeouts increased.

Synthetic example · not a live diagnostic

Designed to sit above the stack you already run.

Integration roadmap · no partnership or endorsement implied.

KubernetesKubernetes
AWSAWS
Google CloudGoogle Cloud
PrometheusPrometheus
GrafanaGrafana
OpenTelemetryOpenTelemetry
DatadogDatadog

A useful investigation

Start with the sequence, not a guess.

Most incidents leave evidence across several tools. The hard part is putting it in order and separating correlation from cause.

Observed evidence
14:18
Baseline stable

Checkout error rate below 0.2%

14:21
Release deployed

payment-service · 7 files changed

14:24
Dependency slowed

Payment API p99 increased 8×

14:27
Checkout degraded

Timeouts spread to two regions

What Data Is Mist should return

A ranked explanation with a way to disprove it.

Likely cause
Payment-service release 8f21a
Confidence
Medium — timing aligns; code path not yet verified
Next check
Compare slow traces before and after the release

No production action without human review.

Product direction

One place to conduct the investigation.

The initial product stays narrow: help an on-call engineer get from an alert to a testable root-cause hypothesis.

01

Build the incident timeline

Put alerts, deploys, configuration changes, traces, and service dependencies in chronological order.

Context
02

Connect symptoms to changes

Rank relevant changes and show the evidence for and against each working hypothesis.

Reasoning
03

Hand control back to the engineer

Recommend the next check, keep uncertainty visible, and require review before impactful action.

Control

How we want to build

Useful before autonomous.

Evidence over fluency

A confident paragraph is not a diagnosis. Every conclusion should lead back to observable signals.

Uncertainty stays visible

Engineers need to know what is known, what is inferred, and what still needs checking.

Fit the current stack

Use the telemetry teams already collect instead of asking them to replace their monitoring tools.

Design partners

Bring a real incident. Help shape the first useful version.

We want to learn from teams running distributed systems, especially SRE, platform, and infrastructure groups.

cto@dataismist.com