Healthcare · Revenue Cycle Management

Fix the claim before it leaves the building.

Most RCM AI goes to work after the chart closes, reconstructing accuracy that was lost in the exam room. We put three digital workers upstream of the problem — and we run them for you.

Book a working session Size your denial leakage

The usual sequence

Encounter happens → note written from memory → coder queries the physician days later → claim goes out incomplete → payer denies → someone appeals it.

What we deploy instead

Encounter is captured as it happens → codes surface at the point of care → the claim is validated against payer rules before submission → the denial never occurs.

Building healthcare systems since 2015Ambient Scribing · EMR Copilot · DocuFindr

Trusted by leading enterprises and healthcare teams

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AI in the revenue cycle, in plain terms

The revenue cycle is everything between a patient walking in and the money arriving. Most of it is administrative work that AI can now do — but where you apply it decides whether you get a productivity gain or a financial one.

Where the money actually leaks

Denials are the symptom, not the disease. The common causes trace back much earlier — documentation that missed clinical specificity, a code that did not reflect the visit, eligibility never verified, authorisation never obtained. By the time a payer rejects the claim, you have paid for the work twice: once to do it, once to chase it.

Why most RCM AI underdelivers

The majority of tools in this market work retrospectively. They cluster denials, draft appeals and chase A/R — valuable, but downstream of the error. They also arrive as platforms your team must configure and operate, which is why so many pilots stall on integration effort and unproven ROI rather than on model quality.

What changes when you move upstream

Capture the encounter accurately, code it while the clinician is still in context, and check the claim against payer rules before it is submitted — and a large share of denials simply never happen. That is a smaller AI problem than appeal automation, and a far bigger financial one.

The three workers at a glance

One chain, three points of intervention

Each can be deployed on its own. Together they cover the encounter through to a validated claim.

Sits at · the encounter

Ambient Scribing

Listens to the visit and writes the note.

Does — turns clinician–patient conversation into a structured visit note
Fixes — documentation written from memory hours later
Helps — clinicians, and every coder downstream

Read the detail →Sits at · coding

EMR Copilot

Codes the visit while the context is still live.

Does — drafts SOAP notes and suggests ICD-10 codes in real time
Fixes — inconsistent coding and retrospective query cycles
Helps — coding & CDI teams, and physicians

Read the detail →Sits at · before submission

DocuFindr

Checks the claim against payer rules before it goes out.

Does — checks treatment plans and records against payer rules
Fixes — manual bottlenecks that slow authorisation and cost revenue
Helps — revenue cycle leadership and finance

Read the detail →

Who this is built for

Mid-market providers, not academic medical centres

The enterprise RCM platforms are priced, scoped and staffed for health systems with an internal AI team. If you have a revenue cycle problem and no one to build against it, that is the gap we exist to close.

Organisation

Provider groups, specialty practices, community hospitals and ambulatory networks

Scale

Roughly 3–50 sites, or $25M–$500M net patient revenue

Settings

Ambulatory, dental and specialty clinics; lab and diagnostic operations

The trigger

Rising denial rate, coder backlog, or physician documentation burnout you cannot hire your way out of

Not a fit if you want a platform licence and intend to configure and operate it with your own team. We build, deploy and run the workers. If that is not the model you want, the enterprise vendors will serve you better.

The revenue cycle, and where AI actually pays

Four stages. Three of them decided before the claim is submitted.

Denial recovery is the most crowded part of the market and the least valuable place to intervene. By the time a claim is denied, the cost has already been incurred twice.

Stage 01 · Encounter

Capture

The clinical detail that drives accurate coding either enters the record here, or it is reconstructed later from memory.

Ambient Scribing

Stage 02 · Coding

Code at the point of care

ICD-10, CPT and E/M levelling surfaced while the clinician is still in context, not raised as a query three days on.

EMR Copilot

Stage 03 · Pre-submission

Validate against payer rules

The gate almost nobody owns. Incomplete visits, missing authorisation, plan-versus-actual mismatches and absent audit trail, caught before submission.

DocuFindr

Stage 04 · Post-submission

Denial recovery

Root-cause clustering and structured follow-up for what still gets denied — feeding corrections back upstream into stages 02 and 03.

Orchestrated via CheiAI

Three digital workers

Three workers. One chain. No new workflow to learn.

Each runs inside the systems your teams already use. None of them asks a clinician to change how they work, and none of them requires you to stand up an AI team.

Worker 01 · At the encounter

Ambient Scribing

A passive listener that turns the clinician–patient conversation into a structured visit note — findings, procedure notes, prescriptions and the next-visit plan pre-filled. Built chairside for in-clinic use, with dental workflows including tooth-by-tooth findings.

Capture is end-to-end encrypted, and nothing is drafted that a clinician has not signed.

Specificity at source. Laterality, acuity and aetiology captured in the room, where they are still known — rather than inferred later by a coder who was not there.
Note drafted in real time. The clinician reviews and signs rather than writes.
Structured output, not a transcript. Sections map to what downstream coding and validation actually need.

Worker 02 · At coding

EMR Copilot

A side-by-side assistant that drafts SOAP notes from the live encounter, summarises patient history at the point of care, and suggests ICD-10 codes in real time — flagging the entries that carry high denial risk before they are committed.

Works alongside your existing EMR with bi-directional sync and no duplicate entry.

Coding while the context is live. Suggestions surface during the encounter, not as a retrospective query after the physician has moved on.
Denial patterns built in. The coding layer is trained on payer denial behaviour, so high-risk entries are flagged at the moment they are entered.
History surfaced at the point of care. Prior visits, medications and diagnoses pulled forward instead of scattered across EMR screens.

Worker 03 · Before submission

DocuFindr — pre-denial validation

The gate the rest of the category leaves open. An AI reviewer that checks treatment plans, prescriptions and visit records against payer rules before the claim is submitted — flagging incomplete visits, drug-interaction risk, plan-versus-actual mismatches and missing audit trail.

HIPAA-compliant, deployable in your private cloud or entirely on-premise.

Payer-rule based, not generic scrubbing. Validation runs against the specific rules of the payer the claim is going to.
Every flag cites its rule. Reviewers see why something was raised, which is what makes the output usable and the decision auditable.
Removes the manual bottleneck. The authorisation delays that hold up care and cost revenue every day are resolved before submission rather than after denial.

How we deliver

We don't hand you a platform. We hand you working digital workers.

Every major RCM vendor sells a configuration canvas and a login. Someone on your side still has to design the workflows, integrate the systems, tune the models and keep them running. That someone is us.

Platform model

What you buy elsewhere

A licence, a login and an implementation partner
Workflow design and integration owned by your team
Model tuning and exception handling on your headcount
Ongoing performance is your problem after go-live
Value realised when your team gets to it

Delivered & operated

What you get from us

Three workers built against your payer mix and specialties
Integration into your EMR, PM system and clearinghouse by our engineers
Human-in-the-loop review designed in, and staffed during ramp
We operate the workers after go-live and report on their output
200+ engineers, four delivery centres, ISO 27001 and ISO 9001 certified

Weeks 1–2DiscoveryDenial root-cause read on your last 12 months. We tell you which of the three workers pays back first.

Weeks 3–6Data foundationEMR, PM system and clearinghouse integration; payer rule library assembled for your specific plan mix.

Weeks 7–10Shadow runWorkers run alongside your team without touching live claims. We publish the accuracy delta before going live.

Week 11 onwardOperateProduction, with our team running the workers, holding the exception queue and reporting monthly against baseline.

What we have actually shipped

Delivered results, with the source attached

Every figure below comes from a delivered engagement, and names where it came from.

42%

Increase in denial resolution rate on a denial-flagger platform built for a healthcare RCM client.

Source: Qodex denial flagger engagement

18%

Increase in clean claim rate, moving the organisation toward MGMA-recommended benchmarks.

Source: Qodex engagement

14,000+

Providers running on a Medicaid billing and prior-authorisation platform we built and modernised.

Source: Parthenon Medicaid modernisation

50%

Reduction in medication errors after we rebuilt closed-loop medication management inside an EHR.

Source: Harris Healthcare medication management

Metric provenance: Figures are verified from delivered client projects. We do not publish modelled or illustrative numbers as results. Ask us for the engagement detail behind any figure on this page.

AI safety & audit defensibility

The real risk isn't a wrong note. It's an audit you cannot defend.

Coding suggested by a model is a compliance exposure unless you can show how it was produced, who reviewed it, and what evidence supports it. Every worker we deploy is built to answer that question on demand.

01GenerateThe worker produces a note, code or validation flag from the source encounter.

02GroundEvery output is linked back to the specific evidence and payer rule that produced it.

03EscalateLow-confidence and high-risk items route to a human queue instead of proceeding.

04ReviewA qualified reviewer accepts, edits or rejects. Nothing reaches a payer unreviewed during ramp.

05RecordThe full chain is written to an immutable audit trail, exportable for payer audit.

Our security and compliance posture

ISO 27001 (information security) and ISO 9001 certified
HIPAA-aligned handling across all three workers
Private-cloud or fully on-premise deployment — your PHI does not have to leave your estate
Zero-trust architecture and role-based access on every worker
Named data-processing terms in the engagement contract, not just on this page

Known failure modes we publish

Ambient capture degrades in high-noise, multi-speaker rooms; we measure this per site before go-live
Coding suggestions are weakest in rare specialty presentations with thin training precedent
Payer rule libraries drift; ours are versioned and re-validated on a fixed cadence
No worker is deployed fully autonomously in its first production quarter

Integration reality

We work with the stack you already have.

You have an EMR you cannot rip out and a billing vendor under contract. Nothing here asks you to change either one.

EMR / EHR

Bi-directional sync with your existing EMR, with no duplicate entry. Deep EHR and EMR integration is core delivery work we have done since 2015.

Claims & clearinghouse

Clearing house integration, eligibility, prior authorisation and denial management workflows — built as part of our standing healthcare RCM practice.

Interoperability

HL7 and FHIR, labs and LIMS, pharmacy, RIS and VNA, IoMT and wearable data. Interoperability is a named capability, not an afterthought.

Cloud & platform

Azure, AWS and Google Cloud, with Microsoft-stack delivery including Power Platform where your estate already runs there.

Named integrations are listed only where we have production experience. Send us your stack and we will tell you plainly which parts we have done before and which would be new work.

Fix the claim before it leaves the building.

AI in the revenue cycle, in plain terms

Where the money actually leaks

Why most RCM AI underdelivers

What changes when you move upstream

One chain, three points of intervention

Ambient Scribing

EMR Copilot

DocuFindr

Mid-market providers, not academic medical centres

Four stages. Three of them decided before the claim is submitted.

Capture

Code at the point of care

Validate against payer rules

Denial recovery

Three workers. One chain. No new workflow to learn.

Ambient Scribing

EMR Copilot

DocuFindr — pre-denial validation

We don't hand you a platform. We hand you working digital workers.

What you buy elsewhere

What you get from us

Delivered results, with the source attached

The real risk isn't a wrong note. It's an audit you cannot defend.

Our security and compliance posture

Known failure modes we publish

We work with the stack you already have.

Start with a thirty-minute conversation.

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