Skip to content

AI for Healthcare

Clinicians treating patients. Not charting at midnight.

We engineer AI for health systems and provider groups: ambient clinical documentation, prior-authorization automation, EHR-grounded knowledge search, and intake triage.

Built on your real workflows, your data, and the oversight your clinicians will trust.

Discuss your clinical workflowSee how the system works

The operational reality

Clinicians chart after dark. Prior auths pile up. Records live in six systems.

Every click eats a clinical minute. Every workaround eats a patient.

The paperwork wins. The patient waits.

Felt by physicians, APPs, nurses, and the practice administrators who pick up what documentation debt leaves behind.

  1. 01

    Intake and verification

    Front desk collects history, verifies insurance eligibility, and surfaces prior-authorization requirements before the clinician enters the room.

    Friction · Mismatched benefits data creates reschedules; eligibility rules change weekly.

  2. 02

    Chart review

    Clinician scans problem list, meds, allergies, recent imaging, labs, specialist notes, and any outside records in the HIE.

    Friction · The relevant note is in an outside system, a PDF, or a scanned fax.

  3. 03

    Visit documentation

    The encounter note is drafted during or after the visit: HPI, exam, assessment and plan, patient instructions.

    Friction · Charting runs well past clinic hours; the EHR has become a second patient in the room.

  4. 04

    Orders, coding, and prior auth

    Imaging, labs, prescriptions, referrals go in; E/M and procedure codes are assigned; prior auth requests are drafted and sent.

    Friction · Prior auth packets rebuild the same clinical justification each time.

  5. 05

    Scheduling and follow-up

    Patients are routed to the right follow-up, specialist, or care manager; reminders go out and no-show risk is managed.

    Friction · No-shows and bounces cascade into longer wait lists and revenue leakage.

Swipe the workflow

Where the work breaks

The friction is in the places nobody budgets for.

  • 01

    After-hours charting pulls clinicians past clinic close.

    How we fix it · A system we build catches this before it costs you.

    Impact · Burnout, slower throughput the next day, and senior clinicians who scale back their panel.

  • 02

    Prior auth packets duplicate clinical reasoning that already exists in the chart.

    How we fix it · A system we build catches this before it costs you.

    Impact · Care decisions wait days; staff spend their shift on faxes instead of patients.

  • 03

    The right prior note sits in an outside PDF, a scanned fax, or a specialist letter.

    How we fix it · A system we build catches this before it costs you.

    Impact · Clinicians make decisions on partial context, or spend ten minutes searching before an eight-minute visit.

  • 04

    Call-in questions bottleneck the front desk.

    How we fix it · A system we build catches this before it costs you.

    Impact · Patients wait on hold; simple requests crowd out the complex calls that need staff attention.

  • 05

    No-show and reschedule patterns are visible only in retrospect.

    How we fix it · A system we build catches this before it costs you.

    Impact · Chair time is lost; the waitlist grows while clinicians sit idle.

What we would build

Systems, not features. Each with its own job.

  • 01

    Ambient clinical documentation

    Clinicians still chart after the last patient leaves.

    Who benefits · Physicians, APPs, and nurses in ambulatory, specialty, and inpatient settings.

    Inputs
    Consented ambient audio from the visit, structured encounter metadata from the EHR, and the clinicians own templates and macros.
    What the system does
    A specialty-aware model drafts the note in the clinicians structure (HPI, exam, A/P, patient instructions) and queues orders, coding suggestions, and problem-list updates for one-click review.
    Output
    A draft note, draft orders, and draft codes staged inside the EHR for sign-off.
    Benefit
    Documentation shifts to during the visit, not after clinic. The second patient in the room goes away.
    Human stays here
    Clinician reviews, edits, and signs every note. Orders and codes are suggested, never submitted automatically.
  • 02

    Prior authorization that drafts itself

    Prior auth consumes hours each week that should sit with patients.

    Who benefits · Clinicians, prior-auth specialists, and revenue-cycle leaders.

    Inputs
    The patients chart, the proposed order, the payers current clinical criteria, and the practices own historical approvals.
    What the system does
    The system matches the proposed order to payer policy, assembles the required clinical justification from the chart, drafts the submission, and tracks status through the payer portal.
    Output
    A drafted PA submission ready for a specialist to review, with every clinical claim linked to the source note or report.
    Benefit
    Approvals come back faster. Staff shift from faxing to appealing the cases that matter.
    Human stays here
    A prior-auth specialist reviews every submission before it is sent. Appeals and medical-necessity narratives are always clinician-authored.
  • 03

    EHR-grounded knowledge search

    The answer is in an outside PDF, a scanned fax, a specialist letter, or a three-year-old discharge summary.

    Who benefits · Clinicians at the point of care, and nurses and MAs preparing for the visit.

    Inputs
    The patients EHR record, uploaded documents, HIE pulls, and the practices own clinical protocols.
    What the system does
    Documents are indexed into a retrieval system that respects the patient record boundary. A question returns the exact passage, with the source document and date.
    Output
    A direct answer, with citations the clinician can open before they act on it.
    Benefit
    Chart review that took ten minutes drops to under a minute. Nothing surfaces without a source.
    Human stays here
    Every answer cites its source document. The system refuses to answer when retrieval confidence is low.
  • 04

    Patient intake and front-desk triage

    Routine calls bottleneck the front desk; patients leave voicemails for simple questions.

    Who benefits · Front-office staff, nurses, and care-coordination teams.

    Inputs
    The practice own scheduling rules, triage protocols, and clinical guidance; the patient existing record where consented.
    What the system does
    Voice and chat agents handle intake, scheduling, prescription refill requests, and routine clinical questions against the practice protocols. Complex calls are warm-routed to the right person with a prepared summary.
    Output
    Appointments booked, refills queued for prescriber sign-off, routine questions answered, complex calls handed off with context.
    Benefit
    Phones stop queueing. Staff attention shifts to the calls where a human voice matters.
    Human stays here
    Clinical questions beyond the protocol escalate to a nurse. The agent discloses that it is an assistant.
  • 05

    Scheduling, no-show, and panel operations

    Chair time is lost to no-shows and reschedules that were predictable in advance.

    Who benefits · Practice managers, schedulers, and care coordinators.

    Inputs
    Historical no-show patterns by patient, visit type, time of day, and weather; outreach channel preferences.
    What the system does
    The system scores no-show risk for each upcoming appointment and sequences reminders through the channel the patient actually responds to. When a slot is at risk, it offers an earlier appointment to a patient on the waitlist.
    Output
    Fewer empty chairs, shorter waitlists, and reminders that read like the practice, not a vendor template.
    Benefit
    Capacity utilisation rises without adding clinic hours.
    Human stays here
    Patients remain in control: opt-outs are honoured, and clinically urgent cases are not reshuffled.

Show the system

What it could look like in your hands.

A design concept, not a shipped product. Interfaces like this get drawn out before any code lands, so the system reads back the way the people using it work.

encounter / post-visit review

J. Morales

MRN 004-23-91 · 54 F

Follow-up, hypertension and type 2 diabetes

Draft encounter note

HPI
Patient returns 12 weeks after medication titration. Reports improved morning readings; occasional lightheadedness in evenings. Denies chest pain, syncope, or peripheral edema.
Exam
BP 128/82 in-office. HR 76. Lungs clear. No LE edema. Point-of-care glucose 142.
Assessment and plan
1) HTN controlled on current regimen; continue, add home BP log review at next visit. 2) T2DM A1c trending down; continue metformin, reinforce diet counseling. 3) Address evening lightheadedness: review timing of antihypertensive, consider dose split.

Citations behind the draft

  • “A1c trending down.”Source · Lab result, A1c 7.4 (ordered 2026-07-14) vs 8.2 (2026-04-02).
  • “Evening lightheadedness.”Source · Patient quote at 00:03:42 of ambient recording.
Synthetic data. This is a design concept of how a draft note, draft orders, and the citations behind them could look in a clinician hands.

End-to-end workflow

From the trigger to the outcome.

A representative encounter path, from the room to a signed note. Automated stages run the moment the data lands; human stages are explicit and never skipped.

  1. 01

    Visit begins

    Patient consents to ambient capture at check-in. The device records only inside the exam room, encrypted end-to-end.

    Automated
  2. 02

    EHR context pulled

    Problem list, meds, allergies, recent results, and any pending orders are retrieved via the EHR SMART on FHIR interface.

    Integration
  3. 03

    Note drafted during visit

    The model drafts HPI, exam, and A/P in the clinician own template structure, with orders and coding staged alongside.

    Automated
  4. 04

    Low-confidence passages flagged

    Passages where retrieval confidence is low, or where the audio was unclear, are marked for clinician attention rather than guessed.

    Guardrail
  5. 05

    Clinician reviews and signs

    Draft note, orders, coding, and PA packets are reviewed in the EHR. The clinician signs; nothing is submitted without them.

    Human in loop
  6. 06

    Downstream actions fire

    Signed orders route to the lab or pharmacy. Codes flow to billing. PA packets go to the payer. All logged with the signing clinician ID.

    Integration
  7. 07

    Follow-up queued

    Patient reminders, no-show-risk-aware scheduling, and the next visit prep brief are set up for the care team.

    Automated

How exceptions are handled

  • If audio capture failed, the system returns an empty draft rather than a hallucinated one.
  • If a required piece of chart context is missing, the note flags it for the clinician instead of silently filling in.
  • If payer policy changed overnight, prior-auth drafts refuse to send until the policy is re-matched.
  • If a patient revokes consent, their ambient audio and derived drafts are purged.

How it is engineered

Built on your stack, not ours.

Design choices that make this run in a real clinic, not a demo. We build to your EHR, your identity provider, and your compliance posture, not to our stack.

01

EHR integration

SMART on FHIR for reads and writes against Epic, Cerner (Oracle Health), athenahealth, and others. HL7v2 and CCDA for legacy interfaces. We write back only through the surfaces the EHR approves.

02

Identity and access

Single sign-on through your existing identity provider. Clinician actions are attributable; agents never act under a shared service account.

03

Data boundary

PHI stays in your tenant or ours under a signed BAA, encrypted at rest and in transit. Model inference runs with no retention and no training on your data.

04

Retrieval

A per-patient retrieval index with hard tenant and record boundaries. Documents are OCRd on ingest; every answer carries its source.

05

Model choice

Specialty-aware foundation models for drafting; deterministic rules for coding checks and policy matching. We pick models for cost, latency, and accuracy on your actual notes, not benchmarks.

06

Observability and evaluation

Every draft and every retrieval is logged with the model version, the inputs, and the clinician edits. An evaluation harness watches drift and flags accuracy regressions.

What the business will measure

Numbers the operator already watches.

We agree on metrics and baselines before building. These are the numbers a healthcare operator usually watches when deciding whether a system is working.

  • 01

    After-clinic documentation time per clinician

    Baseline today

    Measured today in EHR audit logs: average minutes charting after the last patient visit end, by clinician and day.

    How it moves

    Ambient draft is staged at visit close; the sign step shrinks to minutes rather than hours.

  • 02

    Prior-auth turnaround time (median)

    Baseline today

    Time from order placed to decision received, by payer and service line.

    How it moves

    Drafts are ready when the order is placed; appeal-worthy denials are prioritised for a specialist attention.

  • 03

    Chart-review time per encounter

    Baseline today

    Audit log: EHR time from chart open to encounter start, by clinician and visit type.

    How it moves

    A one-question search surfaces the relevant prior note; a visit prep brief is pre-staged.

  • 04

    Call abandonment rate at the front desk

    Baseline today

    Phone-system export: calls abandoned per hour and by time of day.

    How it moves

    Routine intake handled by agents; complex calls reach staff with context already prepared.

  • 05

    Scheduling capacity utilisation

    Baseline today

    Scheduled vs completed appointments by clinic, day, and visit type.

    How it moves

    No-show risk is scored; reminders use channels the patient responds to; at-risk slots are offered to the waitlist.

Human in the loop

Oversight, safety, and the data boundary

Where the humans stay in control.

The system is designed around what clinicians, compliance officers, and patients already expect. Nothing is autonomous that should not be.

  • 01

    HIPAA posture

    We operate under a signed Business Associate Agreement. PHI is encrypted at rest and in transit. Access is minimum-necessary and logged.

  • 02

    Patient consent

    Ambient capture requires explicit consent at check-in. Patients can revoke; revocation purges audio and derived drafts.

  • 03

    Clinician sign-off

    No clinical note, order, code, or prior-auth submission is final without the responsible clinician signing it in the EHR.

  • 04

    Attribution and audit

    Every automated draft logs the model version, the inputs, and the clinician edits. Audits can reconstruct how a note was composed.

  • 05

    Low-confidence refusal

    When retrieval or extraction confidence is below threshold, the system returns "not found" rather than guessing.

  • 06

    No training on your data

    Your clinical data is not used to train base models. Any fine-tuning happens in your tenant, with your approval, on your data only.

Proof

Work we have shipped, and the shape of what we build.

Real engagements where we can speak to them, and representative use cases where we cannot.

Case studies

Shipped in Healthcare.

How the build runs

Scoped tightly, shipped in phases.

Fixed price per phase, approved before the phase starts. You own the code, the models, the data, and the deployment.

  1. 01

    Discovery

    Two weeks. We shadow clinic days, map the real workflow against your EHR, and agree on the metrics we will move and how we will baseline them.

  2. 02

    Prototype on real data

    Four to six weeks. One service line, one clinic, one proof of mechanism. Clinicians use it in parallel to real work; we measure against the baseline.

  3. 03

    Production rollout

    Eight to twelve weeks per service line. EHR integrations, SSO, BAA, audit trails, evaluation harness, and the oversight workflow your compliance team signed off on.

  4. 04

    Handover

    You get the full source, the runbooks, the evaluation suite, and a two-week walkthrough with your engineering and clinical informatics teams.

  5. 05

    Optional ongoing support

    A fixed-term support agreement for model-drift monitoring, EHR interface changes, and payer-policy updates. Never a retainer dependency.

Common questions

Answers before the first call.

01Which EHRs do you integrate with?
We build against SMART on FHIR, which covers Epic, Cerner (Oracle Health), athenahealth, Meditech, and others. For legacy connections we handle HL7v2 and CCDA. We write back only through the EHR own approved surfaces.
02Where does PHI live?
In your tenant where the EHR already sits, or in a dedicated environment we operate for you under a signed BAA. Encrypted at rest and in transit, minimum-necessary access, no retention at inference time, and no training on your data.
03Do clinicians actually use ambient documentation, or revert to typing?
Adoption is a workflow problem, not a model problem. We co-design with the clinicians who will use it: template structure, how edits land, where orders surface, how PA packets stage. If a clinician cannot sign a draft note in under two minutes, we have not shipped yet.
04What happens when the model is wrong?
Every draft is a draft until a clinician signs it. Low-confidence passages are flagged, retrieval refuses when it is not sure, and every automated action logs the model version and inputs so an audit can reconstruct it. We treat errors as a product metric, not an afterthought.
05Can you work alongside our existing scribe or documentation vendor?
Yes. We often start by replacing one piece (prior auth, or chart search) while an incumbent scribe stays in place, and expand only where the measured outcome justifies it.
See the other details on the FAQ page

Next step

A thirty-minute conversation. Bring one workflow that costs you clinician time today, and we will walk through what the first phase would look like, where the measurements come from, and what has to be true for your compliance team to approve it.

Discuss your clinical workflow

Helpful to have on the call

Helpful to have on the call: the EHR you run, one service line with a documentation or PA pain point, and the person who owns the metric you would like to move.

Explore

A different problem in every sector, one engineering standard underneath.

Featured · AI for banking

Banking

The fraud that hurts looks exactly like a real transaction.

Your fraud team catches the obvious cases. The expensive ones are the transactions that look normal until they are not.

See AI for banking
stallwart / banking

Live run

Banking

Running
as of nowhealthy
See every industry we build for

An AI-First Engineering Company

Get a price in one call.

One 30-minute call. You leave with an architecture, a fixed price, and a start date. No charge, and no slide deck.

Booking now for December 2026

Book a callSee how it works