Real sources, controlled access
The PostgreSQL connector scans the source in a read-only transaction. Scope, row budget, method, and duration are reported; a partial scan is never presented as a full scan.
A NEW LAYER OF DATA RELIABILITY 01 / 06
The pipeline ran.
But is the data right?
PipeSentinel makes silent data failures visible. It detects anomalies, maps the affected pipeline, explains likely root causes with evidence, and tests proposed fixes on isolated data before a person decides what to do.
Working prototype, clearly defined scope.
PostgreSQL source analysis and evidence-based RCA are available.
✳ PipeSentinel SYSTEM LIVE
DATA RELIABILITY / INCIDENT DETAIL
Silent failures. Clear signals.
orders.total_amount · latest scan
AFFECTED ASSETorders
SIGNALDistribution + range
STATUS Review needed
✳
Possible unit / scale errorEvidence: amounts rose 100×; the contract range was exceeded.
↗Fix validationIsolated snapshot · human approval
PRODUCT PREVIEW ↗ SYNTHETIC SCENARIO F05
DATA QUALITY✳ROOT CAUSE ANALYSIS✳LINEAGE✳SAFE REMEDIATION✳HUMAN APPROVAL
01 / THE PROBLEM
A SUCCESSFUL JOB DOES NOT GUARANTEE TRUSTWORTHY DATA.
The costliest data failures
go unnoticed.
A pipeline can finish without an error while a column disappears, amounts arrive in the wrong units, or the same rows load twice. The dashboard is green, but the data behind a decision may be wrong.
Explore a real test scenario ↗01
Signals are fragmented
↗Schema, completeness, distribution, volume, and freshness appear in separate tools.
02
Causes are unclear
↗An alert alone cannot show which source affects which report.
03
Fixes carry risk
↗Changing production data is hard without evidence and a before-and-after comparison.
02 / PLATFORM
ONE WORKFLOW, FROM DETECTION TO DECISION.
See the issue. Trace its impact.
Make an informed decision.
PipeSentinel connects quality signals with technical and business context. Every step produces evidence that can be reviewed.
SIX STEPS BELOW ↓
01 / 06
Understand the source
✳A read-only PostgreSQL connection starts a scoped analysis of the selected table and partition. The connection secret stays in an environment variable, outside metadata.
03 / PRODUCT EXPERIENCE
AN EXPLAINABLE INCIDENT, NOT AN ABSTRACT ALERT.
CONTROLLED FAILURE CATALOG · F01—F06Follow an anomaly
Follow an anomaly
from signal to decision.
Six synthetic scenarios reproduce failures ranging from schema loss to delayed freshness. Each is evaluated against its expected signal and root-cause hypothesis.
INCIDENT REVIEWF05 / 06
DISTRIBUTION + RANGE
Lira → subunit scale error
orders.total_amount values rise to roughly 100 times the expected level.
01 / DETECTION
A contract range violation and baseline distribution shift appear together.
02 / ROOT CAUSE
The rules engine presents a possible unit / scale change as an evidence-backed hypothesis.
03 / DECISION
A limited scale template is tested on an isolated snapshot; the result goes to human review.
04 / TECHNICAL DEPTH
AN AUDITABLE ARCHITECTURE.
AI sits on top of evidence.
People make the decision.
Anomaly detection uses deterministic contract rules and statistical comparisons. The core workflow works with the LLM disabled; when enabled, it turns existing evidence into a clearer explanation.
WORKING STACKPython
FastAPI
PostgreSQLStatic HTML / CSS / JS interface
FastAPI
PostgreSQLStatic HTML / CSS / JS interface
INPUTPostgreSQL
+ dbt / OpenLineageSource and dependencies
→+ dbt / OpenLineageSource and dependencies
QUALITY ENGINEProfile + contract
+ baselineSignal and risk score
→+ baselineSignal and risk score
ANALYSISLineage + RCA
+ business impactEvidence-backed hypothesis
→+ business impactEvidence-backed hypothesis
CONTROLIsolated validation
+ approval recordNo automatic production writes
+ approval recordNo automatic production writes
A trustworthy baseline
Empty or anomalous scans cannot become baselines. A clean candidate needs operator approval, and seasonal comparisons require enough approved history.
Visibility into downstream impact
dbt manifests and OpenLineage events feed the dependency graph. Related incidents are grouped, while timing alone is never presented as proof of a shared cause.
Bounded fix validation
Scale correction and exact-row deduplication templates run on isolated snapshots. Before-and-after profiles, contracts, and control groups are compared.
05 / TRUST BOUNDARY
Controlled automation.
Clear accountability.
PipeSentinel does not write fixes to production data on its own. An approval record does not execute SQL. Snapshot validation shows whether a proposal meets the contract; it does not guarantee business correctness or every downstream outcome.
Read the scope limits06 / INVESTOR VIEW
PRODUCT HYPOTHESIS AND NEXT MILESTONE.
Turn data trust into
a measurable workflow.
The initial customer hypothesis is small and midsize data teams using PostgreSQL and dbt. The value proposition brings early detection, evidence-backed diagnosis, and controlled fix evaluation into one interface.
PRODUCT THESIS
“Shorten the path from alert to action without losing the evidence.”
Technical base for pilots
Live PostgreSQL source analysis, YAML contracts, quality-approved baselines, dbt/OpenLineage ingestion, RCA, isolated fix validation, dashboard, and API.
Customer value
Pilots will measure false alert rate, time to detect and diagnose, human effort per incident, query cost, and the share of fixes validated in real pipelines.
Productization
Pilot feedback will guide priorities for access control, deployment, broader data sources, and safeguards for production remediation.
TRANSPARENT STATUS
F01–F06 results come from synthetic regression scenarios. They do not claim production accuracy, revenue, customer count, or market share.
APPENDIX / FAQ
FOR TECHNICAL EVALUATION.
Clear questions.
Clear answers.
Which data sources does PipeSentinel support today?+
A PostgreSQL connector supports real source analysis. dbt manifests and run results, plus OpenLineage RunEvents, add dependency and change context. Support for other databases is not claimed.
Does AI make decisions on its own?+
No. Detection uses contracts and statistical rules. Rule-based root-cause hypotheses link back to evidence; the LLM is an optional explanation layer. Actions are presented for human review.
Does fix validation change production data?+
No. Supported templates are tested on isolated snapshots. An approval record does not run SQL or dbt in production. Automatic production application, rollback, and backfill are outside this version’s scope.
What evidence supports performance and accuracy?+
The F01–F06 synthetic failure catalog has deterministic regression evaluations and integration tests. Real-world false alert and detection rates still need to be measured in pilots.
See the truth
behind your data.
Review the technical architecture, current scope, and pilot hypothesis together.