Why Dalton
Other tools start at the page. Dalton starts before it.
Dashboards show symptoms, alert tools group them, and most AI agents start once something has fired. Dalton reads your whole system all the time, so it is already investigating while the problem is small.
- Dashboards and alertsStarts at a threshold you setHands you a graph to read
- AIOpsStarts when alerts fireHands you fewer, grouped alerts
- AI SRE agentsStarts after an alert firesHands you an investigation of that alert
- DaltonStarts as soon as behavior driftsHands you the cause, the evidence and the fix
Nobody can hold a whole system in their head. Dalton does.
Code, CI/CD, cloud and observability on one map, current on every deploy. The map stays current between incidents, so an investigation never starts from zero.
Three teams, three tools, 57 hours and no cause. Once Dalton could read the logs, the cluster state and the code together, it found the cause in minutes.
What your people know stops leaving with them.
Ask Dalton about your system in plain words and it answers from the map and from what your team has taught it: for an audit, a handoff or a new engineer's first week.
You askedWhat changed across all repos in the last two hours?
- checkout-svc v2.14.8 removed the cap in getPool(n)03:39
- search-api raised its replicas from 4 to 602:58
- infra rotated the db-primary credentials02:10
We needed a change log across all our repos for a customer audit. Dalton generated it in minutes - work that would have taken us days.
Try it on your own production.
Two weeks on your own production, read-only on every integration, with no agents or sidecars in your services. Walk away at any point; there is nothing to unpick.
- Read-only on every integration by default
- No agents or sidecars in your services. Read-only API keys are enough; a small Kubernetes install is optional.
- Your data never trains a model, ours or anyone else's
- SOC 2 Type II certified
Questions about choosing Dalton.
How is Dalton different from our observability vendor's AI?
It reads across all your tools, not one. A vendor's assistant sees the data in its own product. Dalton joins your code, CI/CD, cloud and observability into one map, so it can follow a failure across all of them.
Which teams get the most from Dalton?
Teams that ship often and run many services. Engineering, SRE, platform and DevOps teams whose rate of change outpaces what people can review, including teams writing much of their code with AI.
Should we build this ourselves?
You can, but the agent is the easy part. The hard part is the map: your whole system, current on every deploy, with memory your team can audit and controls on every write. Ask us for our build-versus-buy guide.
What do we have to install?
Only read-only API keys, if you prefer. On Kubernetes you can also add one small, read-only install per cluster. There are no agents or sidecars in your services, and Dalton starts mapping each tool as soon as it is connected.
Start before the page.
A demo first, then a two-week proof of concept on your own production, read-only on every integration.