Stop fixing one failure at a time
An on-call engineer sees one broken pipeline. FlowDog reads your whole estate as one connected network, so the same failure tells you what is about to break next — and which flows are fine.
One at a time
All at once
Four capabilities, and we will tell you which are real.
Most vendors describe a roadmap in the present tense. Each capability below carries the status it actually has today.
Your estate as one connected graph
Every pipeline, its lineage, its failure history and the code it is built from, captured continuously as a single object. Not a lineage diagram you maintain — an observed graph, rebuilt as your estate changes.
Dependencies, run history and code, across Airflow, Databricks, Snowflake and GitHub.
Every failure classified and kept
Each incident is resolved to a cause in a fixed taxonomy and stored against the pipeline that produced it. That history survives the rotation handoff, which is the thing a runbook never manages to do.
Every decision and outcome is kept, so the next investigation starts with context.
Failure prediction with real lead time
A model that reads the graph and the history together and tells you which pipelines are about to fail, and roughly when. A prototype today; we are validating it on larger estates before it is generally available.
We will publish its lead time and precision only after independent validation.
Blast radius and preventive action
Scoring one failure across every connected flow, then acting before the damage spreads: holding a doomed run, warning the consumer of a table that is about to go quietly wrong, opening preventive work on a pipeline that has not broken yet. Designed and in build.
Not shipped. We will not bill for it until it is.
The graph is useful on day one.
Prediction needs your own history before it earns your trust. We would rather say that than switch on a model that guesses.
We connect and watch
Read-only to start, through the APIs you already have for Airflow, Databricks, Snowflake and GitHub. Within days your estate is a graph, and the graph keeps itself current.
Your failures become history
Every incident from that point is resolved to a cause and kept. This is the asset the prediction runs on, and it is why the service gets better the longer it runs.
Prediction turns on
Once there is enough of your own history, predictions start arriving with the evidence behind them. You decide what they are allowed to do, action class by action class.
Now onboarding customers.
Pipeline Intelligence is sold as part of FlowDog, not as a separate product. Prediction is offered as a preview to early customers while we prove the lead time generalises.
Early customer
Prediction, as it ships
- Everything available today, from day one
- Failure prediction as it comes online
- Direct line to the team building it
- Influence over what ships first
- A say in the action boundary before anything runs
What we ask in return: a real estate to learn from, and honest feedback about what is wrong.
Book an onboarding callStandard
The available capabilities
- Your estate captured as one connected graph
- Every failure classified and kept
- Deadline forecasting across the estate
- Detection, diagnosis and governed fixes
- Prediction available when it leaves preview
Priced with the rest of the platform. See pricing.
Book an onboarding callThe questions we get asked
Is this available today, or is it a roadmap?
Both, and the page says which is which. The estate graph and the classified failure history are available now to onboarding customers. Failure prediction is a prototype in preview. Blast-radius propagation and preventive action are in build and are not sold.
What does an early customer actually get?
The available capabilities from day one, prediction as it comes online, direct access to the team building it, and influence over what ships first. In return we ask for a real estate to learn from and honest feedback about what is wrong.
How long before predictions are useful on our estate?
The graph is useful immediately. Prediction needs your own failure history before it says anything trustworthy, which in practice means weeks rather than days. We would rather tell you that than switch on a model that guesses.
How do you prove prevention actually prevented something?
This is the honest hard part. A failure that does not happen cannot be counted, so we run a randomised holdout: a small fraction of high-risk predictions are deliberately left unactioned so we can measure the difference. It is the only way to give you a number that means anything.
Does it need write access to production?
Not to deliver the intelligence. Prediction and blast radius are read-only. Acting on a prediction needs whatever you grant, one action class at a time, and everything outside that boundary is proposed and waits for a person.
Bring us a failure that spread.
Pick an incident where one upstream break took out several teams. We will walk through what FlowDog would have seen, which flows it would have flagged, and how much earlier.
- Read-only to start
- No pipeline migration
- Your security review sets the pace