Healthcare automation · in production since 2018
Your team should not be the workaround for a broken process.
We automate the healthcare workflows that stopped scaling: prior authorization, eligibility, denials and appeals. Your team absorbs growth without tripling headcount.
Ten years in healthcare automation. 150,000 transactions a month running in production today.
Measured at a national cancer diagnostics laboratory, 2018 to 2026, against the client’s pre-automation manual workflow. See how.
Where to start
Start Where It Hurts
Four doors, one team behind them. Pick the closest match, and if none of them fit, that is fine too. Most engagements start with a problem nobody had a name for.
Automation
A process only works because people are absorbing it
Manual re-keying between systems, queues that never clear, staff acting as the integration layer. Eligibility, intake, credentialing, claims, enrollment: the pattern repeats wherever it shows up.
Automation · worked example
Revenue cycle cannot keep pace with growth
Prior authorization, eligibility, claims, denials and appeals. Our deepest proof sits here, a 300% capacity gain and 40% fewer denials, measured over eight years.
Clinical operations
Scheduling and assignment depend on one person’s judgment
Case assignment, subspecialty routing, coverage planning. Work that has to respect availability and clinical judgment, and currently lives in somebody’s head and a spreadsheet.
AI
You are being asked what your AI plan is
We help teams work out which problems a model actually helps with, which are better solved without one, and what has to be true about your data before either can start.
Who this is for
Who we work with
Operations, finance and technology leaders at provider organizations and payers: provider groups, diagnostics and pathology laboratories, health plans, and the companies that serve them. Usually mid-sized, usually growing faster than hiring can keep up.
We can help when:
- Volume is rising and the answer on the table is “hire more people”
- Work quality depends on who happens to be doing it
- An AI initiative has stalled on the question of patient data
- A previous automation attempt broke and nobody trusts the next one
Proof
Our Case Studies
Case Study 01 · Revenue cycle
Transforming revenue cycle management
A growing cancer diagnostics organization needed three times the billing staff to keep pace. We automated the work instead: 300% more capacity per person and 40% fewer denials, eight years and counting.
Read the case study →Case Study 02 · Clinical operations
Automating pathology case assignment
Scheduling work that has to respect availability, subspecialty and human judgment. Proof the method holds outside billing, the same approach, a clinical workflow.
Read the case study →Case Study 03 · Appeals
From manual appeals to a repeatable framework
How a productized appeal framework turned individual wins into a scalable process, and lifted appeal success rates by 70%.
Read the case study →Artificial intelligence
Where AI Fits
AI is useful in healthcare operations in narrower places than the market suggests, and in those places it is genuinely useful, mostly in making unstructured documents usable, and in drafting work a person then approves. Plenty of what teams want AI for is better solved by plain automation.
Wherever a model is involved, the harder question is what happens to patient data while it works. Where a workflow can run on de-identified data, we build it that way, identifiers stripped before anything reaches a model.
Our own products are where that work is most visible, what we build sets out the engineering behind it.
Method
How We Work
Four steps, in this order, every time. We do not start building until step two produces something you recognise as your own process.
01
Sit with the work
We watch the actual queue, with the people who run it. Documented process and real process are never the same document.
02
Map and cost it
A workflow map with hours attached to each step, so the decision about what to automate first is arithmetic rather than opinion.
03
Build the narrow thing
One workflow, in production, in weeks. The people who built it stay on the call. No twelve-month implementation.
04
Hand over the controls
Documentation, logging and escalation paths your team owns. If we disappeared, the system would keep running.
Security & data handling
Security In A Healthcare Environment
Automation and AI in healthcare mean touching systems that hold patient data. If you run a laboratory or any organization under audit, the question worth asking a vendor is not whether they use AI. It is where your data goes when they do, and who can answer for it afterward.
Patient data stays inside your systems
Automation runs against your environment under your access controls. Where a workflow can run on de-identified or tokenized data, we build it that way by default.
Every automated action is logged
Automated steps are attributable, auditable and reversible. Your compliance team should be able to answer what happened to a specific claim, and when, without asking us.
Clear boundaries around AI vendors
Where a workflow uses a model, we document which service processes the data, under what agreement, whether outputs are retained, and whether de-identified data would serve the same purpose. Your security team gets that in writing, not in a demo.
We work inside the controls you already have
Change control, access review, inspection readiness. Laboratories and regulated providers carry obligations we do not get to sidestep, so we plan around them from the first workflow map instead of asking for exceptions later.
Your reviewer will want more than this. The full security and compliance page sets out our position control by control, in the language your questionnaire uses.
The offer
Book A Teardown
Thirty minutes on the workflow that stopped scaling: a claims backlog, a prior authorization queue, case assignment, an intake process nobody owns. Afterwards you get a one-page automation opportunity map, whether or not we work together.
Bring the process, roughly how much volume it carries, and where it breaks. That is enough to have a useful conversation.
Book a teardownWhat happens on the call
You walk us through the workflow. We ask where it breaks and who absorbs it when it does.
We map it back to you out loud, with hours attached, and mark what is automatable today versus what is not.
The one-page map arrives within two business days.
Want it minute by minute? The full page sets out the agenda and handles the usual hesitations.
Not ready for a call? The eight-question readiness check takes two minutes and gives you the same map in outline.