AI for the practice. Less paperwork. Clear control of patient data.
Practices, clinics, and wellness businesses: places where the front desk carries the whole operation and the paperwork grows faster than the patient list.
Where does AI actually pay in healthcare?
Three administrative candidates to investigate. These are illustrative opportunities, not measured results from healthcare clients. None of them touch clinical judgment, and all of them start with the question every practice should ask first: where does the data go? We answer that plainly in Where Your Data Goes.
Give the front desk a prepared scheduling queue.
Phone tag, no-shows, and gaps the front desk discovers too late to fill. An automation drafts confirmations and reminders, offers open slots when someone cancels, and flags openings for the front desk. The front desk approves what goes out.
The paperwork is done before the patient sits down.
An intake agent collects forms and history ahead of the visit, chases what is missing, and hands staff a clean, chart-ready summary. Your clinicians spend the visit on the patient, and every clinical call stays theirs.
The claim is drafted from the documentation, not from memory.
Coding and claims work piles up at the end of the day and the denials pile up after that. An agent drafts the codes from the documentation and flags the uncertain ones for a person to review, so the biller stays accountable for every claim that goes out.
If the opportunity needs investigation, start with the AI Readiness Audit: two to four weeks mapping where the hours actually go, and where patient data can and cannot go, before anyone builds anything. Pricing is published, in plain numbers: audits run $3,500–$8,500; sprints $18,000–$60,000, fixed quote in writing. The cloud-versus-on-prem decision, in plain English: where your AI data actually goes.
Already know the handoff? Bring one example from your practice, the systems it touches, and the person who reviews the result. A defined workflow can go straight to sprint scoping. Use sample records without sensitive details until data handling is agreed.
Start with the data flow. Then choose the system.
Agree where patient information may be processed, who can access it, and which actions need review before connecting a workflow.
Local, cloud, and hybrid designs have different responsibilities. The written scope should identify what stays in the practice’s systems, what may reach a provider, and how access, retention, and backups work. A local server alone is not the answer to every privacy question.
A privacy filter can detect supported identifiers and remove them before permitted processing. It can miss sensitive information. Filtering must be tested against the documents in scope and used alongside access controls, data minimization, and the agreed deployment boundary.
Our published MARCUS case uses encryption and an append-only, hash-chained log. Verification supports detecting changes to recorded entries; investigation and access controls still matter. The security page explains those limits. MARCUS is a lender deployment, not a clinical case study.
Settle data handling before access. HHS explains when cloud processing requires a BAA and other HIPAA safeguards. Bring the responsible reviewer into scoping and establish the required agreements before sharing PHI.
We do not claim a HIPAA certification or promise that a particular architecture establishes compliance. The examples below are illustrative administrative workflows that require practice-specific review.
Two days a practice already knows. Drawn before and after.
Illustrative vignettes, not client claims. In both, the machine prepares and the person who knows the patient decides.
The new-patient packet is complete before anyone sits down.
Before. Monday brings eleven new patients. Each arrives with a packet filled in on a clipboard, half of it illegible, a third of it blank. The front desk re-keys it into the practice management system between phone calls, chases three of them for an insurance card, and finds at 4pm that two histories are missing the medication list the provider needed at 9am. Nobody did anything wrong. The paper simply moves slower than the schedule.
After. The packet arrives digitally before the visit. An intake agent reads it, flags what is missing against the fields your providers actually need, and sends one plain-language follow-up asking for exactly those. It reconciles the insurance details against what is already on file and drafts the summary the provider reads before walking in. The agreed data-handling controls apply throughout the workflow. A person reviews every packet before it posts.
The win: the front desk stops re-keying and starts handling exceptions.The backlog gets prepared overnight, not worked down on Fridays.
Before. Encounters pile up waiting on documentation. The biller works the queue oldest-first, pulling charts to find the one line that decides the code, and the ones that need a provider question sit until Friday. Claims go out late; a share come back denied for something a second read would have caught; the appeal costs more than the visit earned.
After. A coding agent reads each encounter overnight and stages it: the supporting documentation pulled and cited, the likely code proposed with the text it rests on, and the specific question flagged where the note does not support the level. The biller opens a prepared queue instead of a pile. Nothing is submitted by the machine. A certified coder approves, edits, or sends it back — every time, on every claim.
The intended win: less chart preparation, with coding decisions and submissions still reviewed by a qualified person.Illustrative builds, not client claims. What we would actually scope is whatever your free assessment shows is worth automating first. The one build we can name is MARCUS, a private AI back office for a regulated lender — a source of design lessons, not evidence of outcomes in this industry.
Nothing clinical. Not now, not later.
The line is bright on purpose, and it is the same line we hold for a lender: the machine prepares the work, a licensed person decides it.
We do not build systems that diagnose, triage, interpret an image or a lab result, adjust a dose, or decide medical necessity. Not as a launch limitation to be relaxed later — as a design rule. In the lender build the equivalent rule is that MARCUS never decides eligibility, credit, or price; those numbers come from systems of record, never from a model’s memory. In a practice, clinical judgment is the thing that is not ours to automate.
What is left is the paperwork around the care: intake, eligibility and benefits checks, prior-authorization packets assembled from documents you already hold, recalls and reminders, referral letters drafted from the chart, coding prepared for a certified coder to approve. Administrative work, carried. Clinical and billing decisions stay with the responsible professionals. Scope routine reminders separately from actions that require individual approval, and define the records of those approvals.
Local We work with healthcare & wellness in person from two hubs — AI consulting in Denver and AI consulting in Phoenix — and remotely across the US, at the same published prices.
From the catalog Explore relevant build components: administrative system connectors, data privacy filter, private AI server. These are scoped within an engagement; suitability and inclusions depend on the workflow.
AI for medical and dental practices.
Want the numbers?
The full price list is published — audits, sprints, managed services, all on one page.
Read the price list →01What can AI do for a healthcare practice?+
The administrative work around the care: intake packets completed and checked before the visit, eligibility and benefits verified, prior-authorization packets assembled from documents you already hold, recalls drafted, and coding prepared for a certified coder to approve. Never the clinical decision.
02Where does our patient data go?+
The agreed deployment determines where records are processed. Local models can run on hardware you control; external services require an explicit data-boundary review. Automated privacy filters can miss identifiers and do not replace access controls, required agreements, or a healthcare compliance review.
03What about HIPAA and a business associate agreement?+
When a vendor is acting as a business associate, the required agreement and safeguards must be in place before protected health information is shared. Review the proposed data routes with your compliance adviser. We do not claim HIPAA certification; a local model or privacy filter alone does not establish compliance.
04Will it touch clinical decisions?+
No. We do not build systems that diagnose, triage, interpret an image or a lab result, adjust a dose, or decide medical necessity. That is a design rule, not a launch limitation. Nothing sends, files, posts, or bills until a licensed person approves it.
05How much does it cost for a practice?+
The AI Readiness Audit runs $3,500–$8,500; an AI Implementation Sprint runs $18,000–$60,000, quoted fixed in writing before work begins. If a scoped workflow is not live within 90 days, we keep building at no charge until it is. Intake or coding preparation may be candidates; the audit tests their value, data requirements, and review workload.
06How long does it take?+
Plan on 2 to 4 weeks for the audit and 4 to 12 for the first build, scheduled around clinic hours rather than against them. The guarantee holds either way: live within 90 days, or we keep building at no charge until it is.
07Do you work remotely or on-site?+
Practices near Denver, Colorado or Phoenix, Arizona can have us on-site; elsewhere in the US we work remotely. Hardware and installation are agreed only if the approved deployment requires them.
See the wins in healthcare.
Bring one scheduling, intake, and coding workflow to a free 30-minute assessment. We will discuss the bottleneck, the constraints, and a sensible next step.
We reply within 24 hours. A fixed quote follows an agreed scope, before paid work begins.
— Christopher Myers, Founder