Data pipelines
Jobs where a quiet error is worse than a loud failure.
A pipeline that crashes gets fixed the same day. A pipeline that silently produces slightly wrong numbers gets believed, gets built on, and gets discovered a quarter later when a decision made on those numbers turns out to be wrong. Data work fails quietly, which is what makes it dangerous.
The same loop, applied to this problem.
Say what correct means
The intent captures the invariants — row counts that must reconcile, totals that must balance, records that must never be dropped. Those become explicit criteria in the plan instead of tribal knowledge.
Graded before it touches real data
The independent reviewer evaluates the job against those stated invariants, so a disagreement between what you asked for and what was built surfaces as a verdict rather than as a number in a report.
Approved runs only
You hold the gate on promotion, which is the last point at which a wrong transformation is still cheap.
Things teams actually ask for.
Written the way a request arrives, not the way a feature list describes it.
- A nightly ingest that reconciles source and destination counts
- A denormalization job that refuses to run on a partial extract
- A reporting rollup with an explicit definition of every metric
- A backfill that is idempotent and safe to re-run after a failure
Common questions.
- How does Fidelio know my data is correct?
- It does not know inherently — you state the invariants in the intent. The value is that those invariants become a written, reviewed part of the plan rather than an assumption living in one engineer's head.
- Can it run against production data?
- Promotion is gated on human approval, so running against production is a decision someone makes deliberately with the review verdict available.
Put an intent in and see what comes back.
Every run produces a plan, a build, an independent review, and a gate you control.