Built by AKOSBuilt on
FoundryMEDKONG is a set of deployable Agents for the critical workflows in healthcare revenue cycle operations (eligibility, prior authorization, charge capture, coding review, claim QA, denials, posting and AR), plus review Agents for Medicare Administrative Contractors. AKOS deploys each Agent into the stack you already run, against your systems of record. The Agent prepares the work; your people decide. You own it. It is not a tenant you rent.
Object types
6
Governed actions
4
Functions & automations
5
Applications
2
One MEDKONG deployment: each Agent ships as a versioned module. Sample data.
Built by AKOS
Built on FoundryAgents that work on ontology-backed objects, through governed actions, with full lineage: deployed into your environment, alongside the systems you already run.Added to your stackAudit trail per actionHuman in the loopEach MEDKONG Agent is delivered as a versioned module (ontology objects, governed actions, functions, automations and an operator workbench), built by AKOS on Palantir Foundry and deployed into your environment. It works alongside the EHR, PM system and clearinghouse you already run. Your organization runs it, governs it and changes it.
Each Agent lands in your environment as versioned infrastructure. Your data stays in your systems of record and lands once, with lineage. There is no MEDKONG tenant holding your records.
Patients, encounters, documentation, codes, authorizations, claims, payments and balances are objects every Agent reads and writes, not copies in Agent-specific silos.
Add an object type, a governed action or a work queue. Encode your payer policies and reviewer roles. The Agents are the starting point; the operating model is yours.
Deploy an Agent against the queue that hurts most, whether that is prior authorization, coding or claim QA, then add adjacent Agents against the same foundation without rebuilding it.
An Agent that prepares the work with its evidence. A workbench where an authorized person decides. Governance that sets who may act and what a rerun may touch. An audit record that keeps what happened.
Authorization determinations against the policy stack, ICD-10 suggestions validated against the index and tabular reference, clinical entities extracted from the transcript, the whole claim record checked before it leaves. Each result carries its evidence, its caveats and the exact inputs it still needs.
Suggested codes enter a review state and wait for a coder. Determinations are verified or overridden step by step. Coders query providers in a thread tied to the visit. Nothing becomes operational truth without a named person behind it.
“…present today for his new patient consult for a chronic history of low back pain…”
Sensitive edits run through authorized action types, never direct writes. Roles, permissions, thresholds and approval points are configured per organization. Once a person has reviewed a step, a machine refresh never overwrites it; a rerun resumes from its checkpoint, not from scratch.
Source documentation, evidence spans, policy citations, model reasoning, selected and rejected alternatives, reviewer identity, timestamps and revisions are structured records: reconstructable from the encounter, auditable by construction on Foundry.
Deployable independently, in any order. Each Agent answers one operational question and hands structured context to the next. The visit, documentation, codes, charges and payments stay linked to the same patient and encounter.
Is the patient’s coverage active, and what payer context governs the planned service?
Is authorization required, what policy and documentation apply, and is the packet ready?
Were all performed services captured from the encounter and translated into billable charges?
Do the diagnosis and procedure codes accurately represent the documented encounter?
Is the entire claim record internally consistent and ready for the clearinghouse and payer?
Why was the claim rejected or denied, what must change, and what is the next recovery action?
What was paid, by whom, by what method, and what balance remains?
Which balances need intervention, who owns them, and what follow-up should happen next?
Payer APIs, EDI transactions, clearinghouse acknowledgments, ERA posting and collection workflows are scoped and integrated per deployment, not assumed.
See the provider workbenches →
Foundry is the substrate every Agent deploys onto: the governed data foundation, the ontology, the action framework and the lineage that make an AI decision inside a revenue workflow traceable rather than plausible.
It is also why the Agents can be deployed rather than hosted. Each Agent ships as Foundry object types, actions, functions and applications, versioned and installed into your environment.
One governed data foundation
Source systems land once, with lineage and permissions carried through every downstream use.
A shared operational ontology
Patients, encounters, codes, authorizations, claims and accounts as objects every Agent reads and writes.
Governed actions, not direct writes
Approve, reject, query, edit billing, post payment: each is an authorized action type with identity and time recorded.
Auditability by construction
Every automated decision traceable to its inputs, its rule or model version, and its reviewer.
Your environment, your controls
Roles, permissions, retention and approval points are configured in the platform you already govern.
MEDKONG is not a hosted tenant. AKOS engineers add each Agent to your existing stack, one at a time, and hand it over as infrastructure your team operates.
Deployed into your Foundry environment, against your systems of record. Nothing is copied to a MEDKONG cloud.
Ontology object types, governed actions, functions, automations and operator applications, with a change log per release.
EHR, PM, clearinghouse and document stores land once, with lineage and permissions carried through every downstream use.
Payer channels, authorization programs, coding policies, thresholds and reviewer roles are configuration you own.
Extend object types, add actions, rewire queues. Human-reviewed decisions are protected across reruns and upgrades.
Diagnose the queue, deploy the first Agent, encode your rules, run supervised, hand over. Then the next Agent.
A 45-minute walkthrough: the workbenches, the ontology behind them, and a scoping of what the first Agent would take in your stack.
The same record, Agents and governance serve two very different desks: the provider organization preparing and defending a claim, and the Medicare Administrative Contractor reviewing a prior authorization request.
Eligibility, prior authorization, charge capture, coding review, claim QA, denials, payment posting and AR: one governed workflow in which each Agent hands structured context to the next. Start with the queue that hurts most.
A review Agent turns a submitted prior authorization request into a structured, evidence-backed review case: seven review gates, policy-aware findings, and a reviewer in control of every determination, through to the decision, the UTN and the provider letter.
A walkthrough runs 45 minutes: the workbenches running, the ontology behind them, and an honest scoping of what a first Agent would take in your stack.
The stack behind it
Built by AKOSIntegration, ontology, Agents, applications
Built on FoundryGoverned data, orchestration, full lineageDeployed Agent by Agent, into the systems you already run.