ETL consulting that ends in feeds running every night.
A new billing vendor, a new scheduler — and your reporting still runs on someone emailing Excel files over Dropbox. As your AI operating partner, we build the nightly feed that replaces the ritual: agreed tables in your reporting database, exported to secure SFTP, landing in your scorecard the same shape every night.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
Nobody looks for ETL services until the export ritual breaks.
From a working session with the finance team of a multi-site healthcare services company — mid-transition to a new billing vendor, a scheduling change next — whose reporting ran on manual extracts and emailed spreadsheets. AI was the goal; the plumbing was the blocker.
Reporting runs on emailed spreadsheets
Monthly transaction reports and timesheet data go out as Excel files over Dropbox; somebody downloads them, cleans them, and re-keys them before the numbers mean anything.
Payroll is exported by hand
Every two weeks a person pulls the payroll reports and moves them toward the budget system. Bi-weekly, without fail, a human being is the integration.
Every vendor swap resets the routine
A new RCM vendor went live; the scheduling vendor changes next. Each one sends different files, so the manual routine gets relearned — and the reporting gaps get made up with consistency and overtime.
The scorecard is only as fresh as the last extract
The performance scorecard leadership actually reads waits on whoever had time to run the extracts this month. The AI ambitions on the roadmap wait behind it.
Flat files over email make everyone nervous
Extracts moving through inboxes and personal cloud drives is how sensitive data ends up where it should never be — and why the replacement has to be de-identified by design.
Finance is doing a data-engineering job
Your finance team and their MSP did not sign up for extraction toil. Every hour spent pulling files is an hour not spent on the numbers.
Manual healthcare data integration has a standing price.
What the manual way looks like at one multi-site healthcare services company mid vendor-transition. Your numbers will differ — tracing one month of extracts puts figures on yours before anything gets built. Every AI initiative waits behind the plumbing.
From an anonymized engagement — a multi-site healthcare services company mid-transition to a new RCM vendor, scheduling change next
ETL automation is a build job: agree the tables, script the feed.
No platform for your team to learn, no PHI in flight. The extract your best analyst would run becomes the system — scripted, scheduled, and feeding the AI scorecard the same shape every night.
- 01
Agree the tables
With your finance team and your MSP, we define the summary tables your reporting actually needs — billing, payroll, budget, timesheets — in a de-identified reporting database. No PHI, ever.
- 02
Script the feed
SQL populates the agreed tables and exports every one to CSV flat files on a nightly schedule — SQL Server automation doing at 2 a.m. what a person used to do at month-end.
- 03
Deliver to secure SFTP
The files land in a secure SFTP container, IP-whitelisted to your cloud boundary, where the performance scorecard and its AI agents consume them. Phase one is read-only — no write-back to any system.
An ETL tool moves files. An AI operating partner owns the feed.
Integration platforms sell you connectors and leave the mapping to you — and when your new billing vendor changes the file layout, the connector shrugs and the ritual comes back. The tool was never the hard part; owning the translation between your systems is.
We advise, build, and run the whole machine: the tables, the scripts, the SFTP handoff, and the AI scorecard they feed — and when a vendor changes the files, the feed survives because the translation layer is ours to fix and yours to keep. One boundary, plainly: if you need the roadmap before the build, that is our data strategy consulting work. This page is the hands-on build.
“Somebody manually going in and extracting from Excel” was the whole pipeline. Now the feed lands the same way every night.
Led by Peter Enestrom.
SFTP automation, security, and other straight answers.
What finance and IT leaders ask before anything gets built.
Extract, transform, load: getting data out of the systems that produce it, into the shape your reports need, on a schedule nobody has to remember. ETL consulting done as a build job ends with the feeds running in your environment — the SQL, the scripts, the schedule, the documentation — not a recommendation to buy a platform. It is the plumbing under every AI initiative you have planned.
A tool gives you connectors; it does not own your mapping. When your new billing vendor changes the file layout, the tool waits for you to fix it. As an AI operating partner we own the translation layer — we advise on the tables, build the scripts, run the feed, and step back in when a vendor changes the files. You keep everything.
It is the best time, and the usual reason teams call. The feed is built against agreed summary tables, not against any one vendor’s file layout — so when the new RCM vendor or scheduler changes the files, the translation layer absorbs it and your scorecard keeps its shape. Vendor-proof is the design goal, not a lucky side effect.
By designing PHI out of the pipeline. The feed runs from a de-identified reporting database; only agreed summary tables move, as flat files to a secure SFTP container, IP-whitelisted against your cloud boundary — your Azure network, not ours. Phase one is read-only: no write-back to payroll, billing, or budgeting systems until you ask for it.
Yes — that is the common case. Where a system offers an API (Paylocity’s API, for example), we use it; where it does not, scripted exports automate the SFTP file transfer a person used to run by hand — so your team stops exporting payroll reports manually. We configure feeds out of budgeting systems like Axiom the same way: agreed tables out, same shape every night.
Everything: the database tables, the SQL, the export scripts, the schedule, and the documentation — running in your environment, inside your cloud boundary. We can stay on to run and extend the feeds as your AI roadmap grows, but dependency is not the model; your team gets the walkthrough and the keys.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.