AI Agents for Healthcare
We build multi-agent systems for the administrative side of care: prior authorization, claims and documentation, with a human in the loop.
Book a free consultationTrusted by enterprises across Europe and the US.
What is a multi-agent system in healthcare?
A multi-agent system in healthcare is a set of specialised AI agents for intake, coding, payer rules and documentation that read and write through FHIR and HL7 interfaces, coordinate under an orchestrator, and require clinician or reviewer approval at every point where a decision affects care or reimbursement.
The problems worth automating first
Prior authorization burns days per case
Incomplete submissions bounce, payer rules change quarterly, and the people assembling the paperwork are the same ones patients are waiting for.
Denials are fought by hand, or not at all
Many denied claims are appealable with the documentation you already hold. Assembling the appeal takes hours nobody has, so revenue leaks quietly.
Documentation eats the visit
Notes written after hours, coding queries days later, and a record that's complete for billing but late for the next clinician who needs it.
An example agent team: who does what
A multi-agent system works like a team with clear roles. Here's an example lineup for a healthcare operation:
Case event
Orchestrator
Specialised agents
Intake
Payer rules
Coding
Documentation
Appeals
Clinician / reviewer approval gate
Clinical systems
EHR (FHIR/HL7) · RCM · payer APIs
Audit trail: every decision, input and rationale is logged
Intake Agent
Assembles case documentation and flags what's missing before submission: the difference between a clean prior auth and a bounce.
Payer Rules Agent
Maintains current payer requirements and validates every submission against them, attachment by attachment.
Coding Agent
Proposes medical coding (ICD-10, CPT) with its reasoning and the source passage from the record, for a coder to accept or correct.
Documentation Agent
Drafts clinical notes with sources cited. Nothing enters the record without clinician sign-off. No exceptions.
Appeal Agent
On denial, assembles the appeal case from payer rules and the existing record.
Orchestrator
Routes the work, keeps the order of operations, and writes the audit trail for every decision: inputs, rationale, approver, timestamp.
Not every deployment needs the full lineup. Discovery tells you which two or three earn their keep first.
Orchestration, approval gates and audit trails work the same way in every system we ship.
See how multi-agent systems work
What this looks like in your operation
What the agents do
Prior authorization
Assemble documentation → validate against payer rules → submit → track status
Claims & appeals
Analyse denial → assemble justification → draft appeal
Clinical documentation
Draft note with sources cited → clinician approves → write
Revenue cycle
Detect billing gaps → propose correction → escalate
Clinical trial matching
Screen inclusion criteria → candidate list with reasoning
Systems touched
Prior authorization
EHR (FHIR), payer API, RCM
Claims & appeals
RCM, payer API, EHR
Clinical documentation
EHR (FHIR), dictation
Revenue cycle
RCM, ERP, EHR
Clinical trial matching
EHR, CTMS
What we measure
Prior authorization
Time to decision; denial rate for missing info; FTE hours per request
Claims & appeals
Denial rate; appeal success rate; days in A/R
Clinical documentation
Documentation time per visit; note completeness
Revenue cycle
Days in A/R; clean claim rate
Clinical trial matching
Screening time; qualified candidates per cohort
What the agents do
Systems touched
What we measure
Prior authorization
Assemble documentation → validate against payer rules → submit → track status
EHR (FHIR), payer API, RCM
Time to decision; denial rate for missing info; FTE hours per request
Claims & appeals
Analyse denial → assemble justification → draft appeal
RCM, payer API, EHR
Denial rate; appeal success rate; days in A/R
Clinical documentation
Draft note with sources cited → clinician approves → write
EHR (FHIR), dictation
Documentation time per visit; note completeness
Revenue cycle
Detect billing gaps → propose correction → escalate
RCM, ERP, EHR
Days in A/R; clean claim rate
Clinical trial matching
Screen inclusion criteria → candidate list with reasoning
EHR, CTMS
Screening time; qualified candidates per cohort
From first call to production
01
Architecture Discovery (2 weeks)
We map your process, systems and constraints. You get a reference architecture for your case, a recommended autonomy level per step, and a prioritised roadmap ranked by business value, whether you build with us or not.
02
Pilot in shadow mode (6-8 weeks)
The system runs in parallel with your current process, on live data, writing nothing. Agents assemble and validate real cases alongside your team, nothing is submitted without review, and you measure completeness and turnaround side by side.
03
Production (8-12 weeks)
Integration with your systems through a controlled layer (MCP where possible), approval gates wired to your roles, audit trail switched on, security review passed.
04
AgentOps (ongoing)
Evaluation suites run on every change. We monitor accuracy, latency and cost per task, and re-evaluate the whole system when a model version changes, because a silent model update should never silently change a coding proposal.
01
Architecture Discovery (2 weeks)
We map your process, systems and constraints. You get a reference architecture for your case, a recommended autonomy level per step, and a prioritised roadmap ranked by business value, whether you build with us or not.
02
Pilot in shadow mode (6-8 weeks)
The system runs in parallel with your current process, on live data, writing nothing. Agents assemble and validate real cases alongside your team, nothing is submitted without review, and you measure completeness and turnaround side by side.
03
Production (8-12 weeks)
Integration with your systems through a controlled layer (MCP where possible), approval gates wired to your roles, audit trail switched on, security review passed.
04
AgentOps (ongoing)
Evaluation suites run on every change. We monitor accuracy, latency and cost per task, and re-evaluate the whole system when a model version changes, because a silent model update should never silently change a coding proposal.
See how we've helped our clients
Embedded AI in a cybersecurity platform serving Fortune 500 clients: by embedding a conversational AI layer into the platform, we cut customer onboarding time by 95% and turned a quarterly reporting tool into a daily decision-support system.
Why us
Why enterprises choose us
We're a 50-person, cross-functional software development team based in Warsaw, Poland, building technology that delivers ROI, strong governance, and real adoption.
years delivering digital products
est. 2016
products shipped
web & mobile
experts on board
Product & UX designers, Software engineers, AI specialists, PMs
client NPS
Praised for communication, pace and quality
continents served
North America, South America, Europe, Asia, Africa
Frequently asked questions
Which workflow is costing you the most clinician hours?
Tell us how prior authorization, appeals and documentation run through your organisation today. We'll tell you which process agents should take first, which autonomy level is safe, and what the pilot would look like.
Work with a team trusted by Siemens, PwC, and Toyota.
