A revenue cycle dashboard that is current every morning.
We build and run the AI-automated feeds behind your revenue cycle dashboard — claims billed, cash posted, payroll, and budget — so the scorecard reflects last night, not the last spreadsheet someone exported. Counts and dollars only, never patient data.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
The dashboard only moves when somebody exports a spreadsheet.
From finance teams we have sat with — including an anesthesia practice group whose billing runs by provider NPI and minutes: the numbers lived in the billing vendor’s reports, and the scorecard only refreshed when someone pulled the files by hand. AI was never the missing piece; the feeds were.
The scorecard waits on a person
Claims billed and cash posted sit in the billing vendor’s reports. The dashboard refreshes when someone exports the spreadsheets and cleans them — monthly, if the month is calm.
Your numbers live in the vendor’s format
Hours billed by provider NPI, minutes attached to each provider, cash posted against old claims — the facts of your practice exist in a report layout the billing vendor chose.
A vendor switch scrambles everything
New billing vendor live, old one still posting cash on old claims, files that look nothing alike — the manual reporting process breaks exactly when you need the trend line most.
Payroll and budget travel by email
Bi-weekly payroll and monthly budget figures move as Excel files over email and shared drives, cleaned by hand before the scorecard will accept them.
The meeting argues about the numbers
When the practice dashboard is a month stale, the discussion stops being about performance and starts being about whose extract is right.
Revenue cycle analytics is only as good as its feeds.
What the manual way looks like at an anesthesia practice group mid-transition between billing vendors. Your numbers will differ — tracing one month of the reporting ritual puts figures on yours before any AI gets built.
From an anonymized engagement — an anesthesia practice group, its finance team, and their MSP, mid-transition between billing vendors
We build the feeds behind your RCM analytics.
No new system for your finance team to learn, and no patient data anywhere in the pipe. We map the reports behind the scorecard, agree the summary tables — counts and dollars — and AI-automated jobs refresh them every night.
- 01
Trace the reporting ritual
We follow one cycle end to end: which reports the billing vendor provides, how payroll and budget data arrive, and what gets cleaned by hand before the scorecard will take it.
- 02
Agree the summary tables
Counts and dollars only — claims billed, cash posted, hours and minutes by provider NPI, payroll, budget — documented with your finance team, in a de-identified space you own. No patient data, ever.
- 03
AI jobs run the feeds nightly
Scheduled, scripted exports land the same shape every night; AI checks each load against expectations and flags the exceptions for a person. The dashboard is current every morning.
The chart was never the problem. The exports are.
Dashboard software is genuinely good at drawing the charts — pick your KPIs, pick your colors. What it cannot do is move the numbers: someone still exports the billing vendor’s reports, cleans the spreadsheets, and loads them before anything on screen changes.
We are an AI operating partner, not a tool vendor: we advise on the scorecard design, build the automated feeds, and run them in your cloud — so whichever dashboard you keep, the data behind it is current every morning and survives the next vendor switch.
Somebody manually going in and extracting from Excel — that was the reporting process. We will make up for the gaps with consistency.
Led by Peter Enestrom.
Asked by practice finance leaders.
The straight answers, before you book anything.
A revenue cycle dashboard is one screen that shows how a practice turns its work into cash: claims billed, cash posted, hours and minutes by provider, set against payroll and budget. The dashboard itself is the easy part — what makes it useful is whether the feeds behind it run on their own. Ours are AI jobs that refresh the numbers nightly, so the scorecard reflects last night instead of last month’s exports.
Your billing vendor’s RCM reporting answers their questions, in their format, on their schedule. We pull the same underlying counts and dollars into tables you own, combine them with payroll and budget data, and keep them current nightly — so your revenue cycle analytics survive the day you change vendors, because the feed, not the vendor’s report, is the system.
No — and this is a hard boundary, not a policy preference. The feeds carry counts and dollars only: claims billed, cash posted, hours and minutes by provider NPI, payroll totals, budget figures. No patient names, no records, no identifiers anywhere in the pipeline. The summary tables are de-identified by design, which keeps the AI automation simple and the compliance conversation short.
It is the best time. A manual reporting process breaks during a transition — the old vendor still posts cash on old claims while the new vendor’s files look nothing alike. An automated feed absorbs both shapes and keeps one continuous trend line, so the dashboard you open during the switch is the same one you open after it.
That is the normal case. The feed runs on scheduled, scripted exports — the same files a person would pull, produced every night and moved automatically to a secure drop we consume. AI jobs validate each load and flag the exceptions for a person. Legacy systems and vendor portals slow the work down; they do not block it.
Because the tool draws charts and the problem is the plumbing. A dashboard product gives you KPI templates; it does not extract the billing vendor’s reports, clean the payroll exports, or load anything. We advise on the scorecard, build the feeds, and run them — an AI operating partner, not another license. Whichever dashboard you keep, the numbers behind it are finally current.
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.