One platform.
Three automated workflows.
A national physician association had no shortage of data. It had a shortage of time. SunnyData automated three manual workflows on their Databricks platform, taking a weekly review from 30 hours to about 1.
Client challenge.
Three manual jobs, with no path to scale.
The association represents physicians across the US. It maintains physician credentialing records and runs an online medical learning platform. The data wasn't the problem. The workflows were.
Sanctions, read one by one.
When a state medical board sanctions a physician, a PDF arrives and one analyst reads, categorizes and records it by hand. That's 50 to 170 documents a week.
Articles with no common tags.
The learning platform's courses and articles had no consistent classification. Search was limited, governance was manual and recommendations had nowhere to start.
Licensing files, routed manually.
Staff had to retrieve and route files from 28 state licensing boards manually.
The solution.
Three jobs, one governed platform.
SunnyData built each workflow on the association's existing Databricks environment, to production standards from day one.
From a week of work to an hour.
A pipeline reads every new sanction PDF daily, identifies the sanction type using an AI model, and emails the team the result with a plain-language explanation. It skips files it has already processed and keeps a complete history for audits.
Plain-language explanation
A classification layer that scales.
A model summarizes each article and sorts it into the association's own and common medical categories. It works live, so new content is tagged as it comes in. The result is a searchable, managed library and a base for future recommendations.
28 states, every day, untouched.
The system looks for new files every day, brings them into a controlled space, sends each to the correct place by state and month, and sends a daily summary to stakeholders. Nothing is missed, nothing is handled twice, and every transfer is recorded.
Illustrative representation. Not client data.
Not a proof of concept.Not a pilot.Running in production today.
SunnyData also set shared standards early, with governed releases and consistent promotion from test to production, so every solution met the same quality bar.
Key benefits achieved.
Business results, before and after.
What they can do now.
The hard work of getting the platform right is done.
Review, not data entry.
Analysts now review and act on AI outputs instead of producing them by hand.
Build recommendations.
The learning platform finally has a reliable classification layer to build on.
Add the next use case.
New workflows join the same environment, with their data governed from day one.
What comes next gets easier.
