AI Healthcare Operating System
Shteg.ai replaces the patchwork of disconnected systems — EMR, clearinghouse, RCM, scheduling, and payments — with a single AI-native platform where every workflow runs on one shared data model.
One data model. One ledger. One copilot — across the whole practice.
Hundreds of moving parts, one system
Independent, continuously in motion — not a looping animation. Move your cursor through it.
The problem
Every practice assembles its own patchwork of vendors, portals, and spreadsheets. The seams between them are where time, money, and accuracy leak out.
A typical practice runs 6–10 vendors — EHR, practice management, clearinghouse, phones, portal, reporting. Each one is another login, contract, and silo.
Staff swivel-chair the same demographics, codes, and attachments between screens all day. Every re-keyed field is a chance for error.
Each system keeps its own version of the patient and the balance. Nothing reconciles, so no one fully trusts the numbers.
Weeks pass between encounter and cash — submission, rejection, resubmission, follow-up. Revenue arrives on the payer’s schedule, not yours.
Experienced people spend their days in payer portals and fax queues instead of with patients. Turnover is the predictable result.
Denials land in workqueues with little context and less urgency. Most are recoverable — few are actually worked.
The answer isn’t vendor number eleven. It’s one intelligent layer that connects them all.
The platform
Shteg sits in the middle of the encounter, turning clinical work into clean claims, scheduled visits, and settled payments — and it speaks every counterparty's language natively.
Patients connect to the Shteg AI layer, which handles documentation, coding, billing, scheduling, prior authorization, denials, messaging, and insights across six domains — clinical, revenue, operations, payments, interoperability, and analytics — and connects to payers, labs, hospitals, government, and banks.
Shteg AI Layer
Always-on agents for every workflow
Clinical
Encounters · Notes · Orders
Revenue
Claims · Remits · A/R
Operations
Scheduling · Tasks · Staffing
Payments
Cards · ACH · Financing
Interoperability
FHIR · HL7 · X12
Analytics
Dashboards · Forecasts · Benchmarks
Interoperability is built in, not bolted on — FHIR · HL7 v2 · X12 EDI · C-CDA · UDAP/TEFCA-aligned surfaces, with the connector for each counterparty enabled per practice.
Four stages, one deterministic gate. Every rail below ships flag-off and fail-closed — the build register says which parts are live and which are gated on a named human step.
Signed encounter → X12 837P assembly
The clinician signs the chart and charge-derive rules assemble an X12 837P claim from what was documented. The charge is a proposal: a human disposes of it before anything is submitted.
NCCI PTP/MUE edits + Da Vinci CRD / X12 278 prior auth
CMS NCCI PTP pairs and MUE unit limits run against the claim before it leaves, and coverage requirements are discovered at order time. Errors surface before submission, not after denial.
Six-condition gate → swappable rail executors
A settlement instruction clears all six deterministic gate conditions or the rail halts. Direct-bank, batch-wire and FedNow ISO 20022 message sets are implemented behind one executor interface — no live rail is enabled, and advance amounts stay pinned at zero.
WORM double-entry posting → 835 reconciliation waterfall
Every posting lands in a hash-chained, append-only double-entry ledger in integer cents, and the 835 remittance is allocated through a conserving waterfall. A variance raises a state and can halt origination; it is not reconciled away later.
Product ecosystem
Fourteen products on one data model — the chart, the money, the schedule, and the patient experience, designed together so nothing needs integrating.
Care gaps, screening due dates, and MIPS measures surfaced from the chart.
Learn moreAI everywhere
Not a copilot bolted onto the side — AI is woven through every workflow in the platform, from the exam room to the remittance.
The visit note drafts itself while you talk to the patient.
ICD-10 and CPT suggestions grounded in the documented encounter.
Charges captured as care happens — not reconstructed after.
Gaps in the day filled with the right visit type automatically.
Requirements checked at order time, not discovered at denial time.
Claims scrubbed against payer rules before they leave — not appealed after.
Routine questions answered and routed before the phone rings.
Patterns across your panels surfaced before they become problems.
AI output is always clinician-reviewable — nothing reaches a chart, claim, or patient without a human signing off.
The scribe records only with consent captured for that encounter, and only while the indicator is on — never continuously. It drafts the note, flags denial risk, and assembles the claim; a clinician disposes of every one of those before anything is signed or submitted.
Why Shteg.ai
Practices stitch together half a dozen vendors just to get through the day. Shteg.ai replaces the patchwork with a single AI-native operating system.
| Dimension | Traditional healthcare stack | Shteg.ai |
|---|---|---|
| Number of vendors | 6–10+ contracts to juggle | One |
| Manual work | Swivel-chair data entry | AI drafts, a human disposes |
| AI | Bolt-on add-ons | Native to every workflow |
| Integrations | Fragile point-to-point | FHIR / HL7 / X12 EDI built in |
| Data duplication | Every system keeps its own copy | One source of truth |
| Payment path | Submit, wait, chase, resubmit | Encounter-gated settlement, with real 276/277 payer status |
| Operational complexity | You manage the glue | We run the rails |
Product preview
Schedule, claims, revenue, and an AI copilot that watches all of it — the command center your front desk, billers, and clinicians share.
Today’s schedule
Claims pipeline
Collections — last 30 days
Sample data
Product mockup of the Shteg.ai command center showing a daily appointment schedule with check-in statuses, a claims pipeline with paid, scrubbed, and queued claims, a 30-day collections trend chart, AI copilot suggestions such as denial-risk warnings and prior authorization reminders, and a patient journey timeline from check-in to payment. All data shown is fictional sample data.
Target outcomes
A Shteg.ai deployment is scoped against a small set of operational targets — time saved at the keyboard, cash collected faster, denials avoided before they happen.
Illustrative targets, not measured Shteg.ai results. No figure here comes from a customer deployment or a published benchmark.
Security & compliance
The rails healthcare already trusts, wired into every workflow — not bolted on at audit time.
Data is encrypted everywhere — AES-256 at rest, TLS 1.3 in transit. Every money posting lands in a hash-chained, append-only double-entry ledger, record access is audit-logged, and every role runs on least-privilege access. Where a BAA is not yet in place, the integration fails closed rather than proceeding. Our SOC 2 audit has not been engaged yet, and we say so on the register instead of implying otherwise here.
Read the register — what is verified, what is gatedScheduling, charting, billing, and payments in one system — with AI quietly doing the busywork.