Build to compound
See what production is doing, and be allowed to act
A Databricks job fails overnight or the bill doubles, and nobody can say whether it matters or who's allowed to stop it. We add a layer over what Databricks already reports that ranks alerts by rules you write, traces spend to the jobs behind it, and lets the person who notices a problem act on it.
The alert fired. Then what.
Monitoring answers whether something broke. The expensive questions are the three it does not.
Nobody knows if it matters
A job failed. Whether that breach is urgent, and to whom, is worked out by whoever happens to know what that job feeds.
The person watching cannot act
They can see it and not stop it. The person who can stop it is not looking. The gap between those two is where the money goes.
The bill has no cause
Spend doubled last quarter. Which jobs did it, and whether any of them produced anything anybody used, is a forensic exercise.
Observation was never the hard part. Deciding what a signal means and who is allowed to respond to it is, and that is a governance problem wearing an operations hat.
The shift
Three ways to watch a live estate
Only one lets somebody do something about what they see.
Platform monitoring
It ran, or it did not
A dashboard somebody built
Accurate, and unread
Governed mission control
Scored, and actionable
What gets stood up
The signals, the judgement and the authority, as one system rather than three tools.
Workload signals
Job state, latency, cost and data quality collected as events rather than scraped from a dashboard.
Severity from policy
Whether a breach matters is evaluated against rules somebody wrote, not inferred by whoever is on call at the time.
Interventions with authority
Stopping a workload is an action with a policy in front of it, so the person who sees the problem can be the person who ends it.
Cost attributed
Spend traced to the jobs and teams that caused it, so a growing bill has a name rather than a mystery.
Budgets that act
A budget that stops a workload, rather than one that emails about it after the quarter closed.
The response history
What fired, who acted, under which policy. Reading the estate's past is a query rather than an archaeology project.
Policy
Decides severity, rather than whoever is on call
Built for estates we were answerable for
Radar came out of running Databricks workloads where somebody was going to ask us what happened and why. It is free and open source and it runs in your workspace.
Clients we have done this for
Health & Wellness
Athlinks
Athlinks had more than 400 million athlete records from sources that never agreed on who an athlete was, and a matching run that died halfway through couldn't just start again. Our engineers took the project from prototype to production as a serverless pipeline on AWS with Temporal holding the state. Failed steps now retry on their own, failures reach Slack as they happen, and the matching rules are tuned for each tier.
B2B ecommerce
AmTab
AmTab, a table manufacturer, had orders moving between departments on paper while demand climbed. Senior engineers from TechFabric worked through those manual steps with AmTab's own people first, then connected the amtab.com store, the CRM and Business Central, so an order placed online reaches the ERP that invoices it without anyone retyping it.
FAQ
Production visibility: common questions
We have monitoring already. What does this add?
The two things monitoring does not do: decide whether a breach matters, and let the person who saw it act. Those are policy questions rather than metric questions, which is why a monitoring tool does not answer them.
Will it actually stop a runaway job?
If you give it the authority to. The design deliberately makes that your decision rather than the tool's, because a system that can stop production without anybody agreeing to it is its own incident.
Does it work on estates we did not build?
Yes, and that is most of them. It reads what Databricks already emits rather than requiring the workloads to be instrumented for it.
If your problem is shaped differently
Build an agentic software factory
Launch a product in a regulated vertical
Take the AI demo to production
Move off the warehouse without a Friday night
Make the estate answer for itself
Modernise the estate that outgrew itself
Keep people in charge of the agents you already run
Leave your team able to do it without us