Production-grade AI systems for eligibility, claims, prior auth, documentation, patient communication, and denial management - built into the systems your team already runs on.
The specifics differ. The same operational friction keeps appearing wherever this work crosses people, records, and decisions.
Front-desk and intake teams re-verify coverage by phone or payer portal, one patient at a time - and gaps still slip through to become denied claims or surprise bills.
First point of frictionCoding mismatches, missing modifiers, and payer-specific formatting rules get caught after submission instead of before, and reconciliation against remittances is a manual, spreadsheet-driven chase.
Recurring operating costAssembling clinical documentation, filling payer-specific forms, and chasing peer-to-peer reviews stretches turnaround from days to weeks, and every hour of delay is an hour a patient waits or a procedure gets pushed.
Recurring operating costPhysicians finish the day's appointments and then spend hours writing notes into the EHR - time that comes out of evenings, not clinic capacity.
Recurring operating costNo-shows, confusing bills, and unclear care-plan follow-up all trace back to the same root cause: nobody has time to reach patients before a problem becomes a phone call.
Recurring operating costBilling teams appeal claims individually without ever aggregating why they're being denied - so the same coding gap, missing documentation, or payer policy keeps costing money month after month.
Recurring operating costBefore we propose a system, we map the recurring surfaces of the operation and the work that moves through them.
Confirm eligibility, benefits, and the operational context before work reaches the care team.
Assemble claim, authorization, or documentation work from the records already available.
Track responses, denials, and missing inputs across payer and internal systems.
Send clinical or financial exceptions to the right person with the relevant source context.
The operation keeps its judgment. AI takes on the repeatable, system-to-system work that makes good people spend their time on administration.
AI handles
Your team handles
The system can draft, organize, and surface the work. An authorized person decides and acts.
Operational systems that connect the tools you already use. Not chatbots sitting next to the work.
Manual eligibility verification means a staff member checking coverage per patient, per payer, often by phone - and it still doesn't catch every gap, which is how denied claims and unexpected patient bills happen. The Eligibility & Benefits Engine runs real-time verification against payer systems as part of intake and scheduling, flags coverage gaps and authorization requirements before the visit happens, and surfaces exceptions to staff instead of forcing them to check everything by default. The result is fewer denials traceable to eligibility, and front-desk time freed up for patients instead of phone hold music.
Claims errors are expensive precisely because they're invisible until a denial comes back - by which point the service was already delivered and the revenue is stuck in appeal. This engine validates claims against payer-specific rules before submission, catching coding and formatting issues at the point they're cheapest to fix, then reconciles submitted claims against remittances automatically and flags mismatches for review instead of leaving reconciliation as a manual, end-of-month scramble. Claims move faster, and the exceptions that do need a human get to one sooner.
Every prior auth request means gathering clinical documentation, matching it to a payer's specific form and criteria, submitting, and then following up - often by phone - until a decision comes back. That process, multiplied across a full caseload, is where care gets delayed and staff time gets consumed. The Prior Authorization Engine assembles PA packets directly from chart data, tracks payer-specific requirements so nothing gets submitted incomplete, and follows up on outstanding requests automatically, giving staff a live status view instead of a stack of pending faxes. Turnaround shrinks from a staffing problem to a monitored process.
Physicians didn't go into medicine to spend their evenings typing notes into an EHR, but that's where a meaningful share of the day goes. The Ambient Documentation Assistant listens to the clinical encounter with patient consent, drafts a structured note in EHR-ready format aligned to your existing documentation standards, and hands it to the physician to review and sign off - not to submit unreviewed. The physician stays the clinical author of record; the system removes the transcription and formatting labor sitting between the visit and a finished chart.
Most patient-facing friction - missed appointments, confusion over a bill, a care plan nobody followed up on - comes down to the same gap: no one had the bandwidth to reach out before it became a problem. The Patient Communication Engine handles appointment reminders, plain-language explanations of bills and coverage, and care-plan follow-up across the channels patients actually use, and escalates to staff automatically when a conversation needs a human. It's not a replacement for your care team - it's what keeps routine communication from consuming their day.
Denial management usually happens one claim at a time: a biller opens a denial, fights it, moves to the next one, and the underlying cause - a coding gap, a documentation shortfall, a payer policy change - never gets fixed upstream. The Denial Root-Cause System aggregates denial data across payers and claim types, identifies the patterns driving them, and feeds specific, actionable fixes back to coding, documentation, or intake - turning denial management from a permanent fire drill into a shrinking problem.
One representative example of an operating system shipped around a real workflow.
Healthcare · Case study
We design around the systems of record. Integration scope comes from the real workflow, access rules, and decision boundaries - not a platform replacement plan.
The principles we use when we design, ship, and improve a system alongside the people who run it.
The work still belongs to your operation. We make the repetitive path reliable and visible.
Each system needs a measurable baseline, source traceability, and a clear way to improve when it is wrong.
The best system sits inside the tools and habits your team already depends on.
The system can prepare, route, and remember. Your team keeps the decisions that carry consequence.
How we apply the same operating discipline in other contexts.
AI systems for customer support, retention, order exceptions, and revenue recovery as volume grows.
Explore →AI for Financial servicesAI systems for KYC, onboarding, fund administration, reporting, and controlled compliance operations.
Explore →AI for InsuranceAI systems for claims, policy servicing, underwriting submissions, and document intelligence across the policy lifecycle.
Explore →The things teams ask before the work begins.
Start where the work is frequent, visible, and costly when it goes wrong. The first mapping session identifies the people, systems, data, and controls around that workflow.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
Start with the work
Pick a time for a working session or send a short brief. Either way, we will come prepared to understand where the work gets stuck.
Talk through the work
Bring the workflow that feels slow or fragile. We will determine whether it is a sensible candidate for an AI system.
Bartosz LuderaFounder, HarnessloopSend a workflow brief
Tell us where work waits, repeats, or falls through the cracks.
