Healthcare data workflows
Convert 834 enrollment files to CSV, one clean row per member.
The monthly 834 arrives, and someone spends a day turning INS, REF, DTP, and HD segments into a spreadsheet the eligibility team can reconcile. Describe the member rows you need instead. Data Shepherd writes transparent scripts that flatten enrollment activity into adds, terminations, and changes, and the sealed script does it the same way every month.
Free plan available. No card required.
INS*Y*18*030*XN*A*E**FT~ REF*0F*MBR000777~ NM1*IL*1*SMITH*PAT~ DTP*356*D8*20260701~
member_id,name,action,relationship,eff_date MBR000777,SMITH PAT,add,subscriber,2026-07-01
How it works
From a messy file to a tested pipeline, in minutes.
Describe it and bring your spec
Plain English, plus a sample, a spec, or a data dictionary. Cannot share data at all? Build from the spec alone.
AI writes transparent scripts
You get reviewable, versioned Python, never a black box. Inspect every line and preview the output on a sample.
Run it anywhere, with no AI
Trigger by API, schedule, or click, or pull from SFTP, S3, or Azure Blob. The sealed script does the work; nothing goes to a model.
The AI builds it. A sealed script runs it.
Most AI data tools push records through a language model on every run. We keep the model in the build phase only, so production never sends your data to a model.
Full data-flow table and sub-processor list in our Trust Center →
Common questions
EDI 834 to CSV, answered.
Can it compare the 834 against our current roster?
Yes. Provide the roster as a second input and describe the reconciliation: the generated script can flag adds, terms, and changes that do not match. You review the script and preview the output before running the full files.
We cannot share member data. Can we still build this?
Yes. Build from the 834 implementation guide or a layout spec alone, with no sample at all, or use a synthetic sample. The model sees structure, never production records, and run-time sends nothing to a model either way.
How do we run this every month without thinking about it?
Save the transformation once, then schedule a pickup from the SFTP or S3 location where the file lands. The sealed script runs automatically and a webhook or email tells you it finished.
Working with claims or clinical data? See the dedicated healthcare page →
Describe it once. Run it forever.
Build the transformation on a sample in minutes, review the scripts line by line, and let the sealed runs handle every file after that.