Incidents, fixed
end to end.

The moment Dynatrace flags a real problem, six AI specialists find the cause in your live data, open the fix in GitLab, and prove your service recovered. You approve every change.

Runs on the stack you already have

Dynatrace
GitLab
Elastic
MongoDB
BigQuery

See it work

One real incident, start to finish

Watch the crew take a real incident from page to verified fix in 90 seconds.

0:00 / 0:00

How it works

From a 2am page to a verified fix, on its own

A single coding agent can't see your production, your customers, or your revenue. HiveMind is a team of specialists that can. It shows its work at every step.

1. It catches the problem

The page wakes HiveMind, not you

When Dynatrace Davis flags a real problem, HiveMind wakes up on its own. No polling, no rules to wire up. It pulls the cause and the evidence from your live Grail data.

  • Triggered by a real Davis problem
  • Reads your own telemetry
  • Opens an incident room in seconds
Davis problem

2. It finds and writes the fix

Six specialists, one merge request

The crew reads your live logs and traces, pins the slowdown to the exact deploy, and opens the fix as a GitLab merge request. It even names the customers and revenue at risk.

  • Root cause from logs and traces
  • A real, reviewable GitLab MR
  • Customer and revenue impact, quantified
p951,320ms
errors1,271
deploy14:09
tracecheckout
MR #52
downstream_delay_ms → 0

3. It proves the recovery

The proof comes from Dynatrace

Once you approve, the fix ships. A Dynatrace Site Reliability Guardian confirms the service is healthy again. The verdict comes from Dynatrace, not us.

  • You approve before anything ships
  • The fix ships only when you say yes
  • SRG recovery check goes from fail to pass
checkout p95 latency
3ms
SRG: PASS
300ms goal
before fixMR #52 mergedafter fix

A real team, not a chatbot

Six specialists, each wired into a real system

One alert and they all jump in. Each is fluent in the platform it drives, so together they see your production, your code, your customers, and your revenue at once.

Detective

Dynatrace

Finds the root cause in your live Grail data.

LogDiver

Elastic

Pins the slowdown to the exact deploy.

CodeArch

GitLab

Opens the fix as a merge request you can read.

Liaison

BigQuery

Names the customers and the revenue at risk.

Scribe

MongoDB Atlas

Writes the incident record as it happens.

Reviewer

Dynatrace SRG

Signs off on the fix, then confirms the service recovered.

Every claim comes with proof

You never have to take its word for it

Every step links to the real artifact behind it. A script can't fake a live Grail query, a Davis problem on your tenant, a merge request in your repo, or a Site Reliability Guardian flipping green.

  • The real DQL query, run on your Grail data
  • The real Davis problem, linked to your tenant
  • The real GitLab merge request
  • A Dynatrace recovery check that goes fail → pass
detective · execute_dql
# real query, on your Grail data
fetch spans, from:now()-30m
| filter dt.entity.service == "checkout"
| summarize p95 = percentile(duration, 95),
by:{bin(timestamp, 1m)}
→ p95 climbed 80ms → 1,320ms after the payment-service deploy
→ problem P-2506-4471 · root cause: payment-service

On-call used to start with a blank terminal at 2am. Now it starts with a root cause, a merge request, and a recovery check already waiting. A human still says yes before anything ships.

The on-call reality, before and after HiveMind

Turn your next 2am page into a merge request.

Connect Dynatrace and GitLab in about two minutes, then watch your first real incident get fixed from start to finish.

SEV1 · checkoutresolved in 4m
CodeArch opened MR #52, a one-line fix
You approved: “Ship it.”
SRG recovery check: PASS · p95 back to 3ms
Incident resolved. Fix merged, recovery verified.