Back office ops · Production

Petvisor scales data platform and achieves more with a smaller team using Airbyte

The problem

Petvisor's data infrastructure, built on Stitch and custom-coded solutions, lacked pipeline visibility and connector customization, making it difficult to manage and troubleshoot data flows across thousands of veterinary locations. A major vendor price increase on contract renewal forced a platform re-evaluation.

First attempt

The prior Stitch-based setup had limited configurability and no pipeline visibility; Petvisor could not customize connectors or diagnose failures when they occurred.

Workflow diagram · grounded in source
1
Enterprise customer requests new data
trigger
“when one of these enterprise customers or their executive teams comes to us, indicating 'we want to see this additional information,' and if we don't yet have it in our data warehouse”
2
Check Airbyte connector availability
routing
“we ask, does Airbyte have a connector for that? If so, yes, let's add it and start pulling that in”
3
Add connector and ingest data
integration
“let's add it and start pulling that in”
4
AI-assisted log analysis
ai_action
“The new AI-assisted audit feature that scans through logs and explains it seems to be pretty effective. Before, logs were very cryptic—this feature makes it much easier to troubleshoot and debug.”
5
New data insights delivered
output
“Being able to iterate and adjust very quickly, and just having the confidence in our ability to obtain new data is worth something, especially at our increasing scale.”
Reported outcome

Petvisor now confidently manages 20–25 data sources feeding into Snowflake, reduced time to integrate new data sources from weeks or months to days, and achieved operational efficiency equivalent to at least one FTE data engineer.

Reported metrics
Operational efficiency vs. headcountequivalent to at least one FTE data engineer
Time to integrate new data sourcesfrom weeks or months to just days
Data sources managed20-25
Pipeline visibilityEliminated configuration blindness with full visibility into data pipelines
Show all 5 reported metrics
operational efficiency vs. headcountequivalent to at least one FTE data engineer
time to integrate new data sourcesfrom weeks or months to just days
data sources managed20-25
pipeline visibilityEliminated configuration blindness with full visibility into data pipelines
headcount avoidedwould definitely require at least another data engineer
Reported stack
AirbyteMicrosoft SQL ServerPostgreSQLMySQLSalesforceGoogle AnalyticsSnowflakedbt CoreTableauStitchTalend
Source
https://airbyte.com/success-stories/petvisor
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Petvisor now confidently manages 20–25 data sources feeding into Snowflake, reduced time to integrate new data sources from weeks or months to days, and achieved operational efficiency equivalent to at least one FTE d…

What tools did this team use?

Airbyte, Microsoft SQL Server, PostgreSQL, MySQL, Salesforce, Google Analytics, Snowflake, dbt Core, Tableau, Stitch.

What results were reported?

Operational efficiency vs. headcount: equivalent to at least one FTE data engineer; Time to integrate new data sources: from weeks or months to just days; Data sources managed: 20-25; Pipeline visibility: Eliminated configuration blindness with full visibility into data pipelines (source-reported, not independently verified).

What failed first in this deployment?

The prior Stitch-based setup had limited configurability and no pipeline visibility; Petvisor could not customize connectors or diagnose failures when they occurred.

How is this back office ops AI workflow structured?

Enterprise customer requests new data → Check Airbyte connector availability → Add connector and ingest data → AI-assisted log analysis → New data insights delivered.

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