Recover months of claim denial revenue in days, automatically
Our specialized healthcare AI agent force finds the denials worth fighting, pulls the matching chart evidence and drafts the appeal with the payer's own policy cited - then puts it in front of your team to approve. Just like that.
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HealthIntellix is a specialized healthcare agentic force,
developed by GoML
We've worked inside your exact workflows, thoroughly
"How much did we write off this year without even fighting for it?"
A massive problem you face is that upto 60% of denials never get resubmitted - because it's manual effort for your team. Stats say that re-working denials the right way have a 70% chance of being overturned. That's revenue unlocked for you.
Every denial gets worked - scored, appealed, and followed through, including the ones your team never had time to touch.
"Why are we paying people to re-litigate money that should've been paid the first time itself?"
Another huge issue faced in healthcare is that an estimated $18 BILLION/year is spent litigating claims. Your team is further burdened with manually pulling charts/spreadsheets, verifying them against ever-changing policies and then drafting appeals from scratch every time.
You enjoy auto-generated appeal packets (claim, chart, payer policy, contracts) assembled with payable clause citations. Then your specialist reviews it and it's good to go.
"How long did that payer get away with it before we noticed?"
Another pain point you're dealing with is in the last few quarters, Medicare Advantage post Pre-Auth denials jumped from 3.2% to 10.4% as payer adjudication patterns changed faster than your denial team could verify them.
Even payer behavior is monitored continuously. New denial patterns surface in week one - so you can flag it instantly rather than when the month ends.
The ROI of Autonomous Recovery Can Be Your Next Growth Lever
Months of denial scavenging. Weeks to file the same appeals.
Claims team
- Time to build one appeal1.5–4 hours
- 835 in hand → appeal filed5–40 days
- Payer pays the appeal30–90 days
- PrioritizationAge, queue capacity
- Evidence assemblyManual chart, policy, contract hunt
- Sign-offOften informal
- CoverageWhat the team has time to touch
Autonomous recovery
- Time to build one appeal20–45 minutes
- 835 in hand → appeal filed1–5 days
- Payer pays the appealUnchanged.
- PrioritizationRecovery probability × dollar value
- Evidence assemblyAssembled and cited automatically
- Sign-offExplicit human approval
- CoverageEvery denial scored
HealthIntellix makes it economically possible to pursue the recoverable pool your team cannot get to today.
Work the highest-value denials first.
Let agentic AI decide which ones.
The claims denial agentic force is one function under HealthIntellix - which has four specialized systems, wired into one closed feedback loop.
With HealthIntellix by your side, your claims team can close the loop on all aspects of the claims process - score, act, follow through and learn.
Payer-Behavior Intelligence
Data
Continuous ingestion of remittance and adjudication data - 835s, appeal outcomes, denial codes - aggregated across a dataset spanning many health systems.
Mechanism
Statistical drift detection on payer-level adjudication behavior, flagging shifts the moment they emerge.
Engineering loop
Cross-tenant aggregation produces population-level signal - a scale of pattern-detection a single system's own claims history can't generate alone.
Grounded Appeal Generation
Data
Five source types reconciled per claim - claim data, chart/EHR documentation, payer policy text, coding guidelines, and contract to the.
Mechanism
Structured retrieval with citation. Every assertion in the appeal traces to a retrieved, sourced document.
Engineering loop
Each appeal ships as an evidentiary brief, with every claim backed by a citable source.
Payer Interface Automation
Data
Live interaction across IVR trees, hold queues, and payer portals.
Mechanism
An agentic execution layer engineered for resilience against UI and workflow changes on the payer side.
Engineering loop
Built and maintained as core infrastructure, engineered to hold up as payers update their systems.
Closed-Loop Worklist Prioritization
Data
Recovery probability × dollar value, scored per denial on arrival. Outcomes - paid, denied again, partial recovery - captured continuously.
Mechanism
A live feedback loop: Score → Act → Outcome → Learn → Re-prioritize.
Engineering loop
Continuous online learning - the model sharpens payer-specific tactics with every resolved case, in real time.
The orchestration layer is the differentiator.
What the system learns from one payer's denials sharpens how it fights every other payer's next.
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"GoML is our strategic partner in building the layer of Generative AI that helps our teams analyze and interact with the broad set of clinical data we collect on every member of our preventative health practice. Further, it is essential with such a rapidly evolving landscape to ensure that we get the right thought leadership to immediately implement new architectural or design patterns in generative AI, and GoML has delivered on this promise as well."
Why leave revenue you deserve on the table? Get started with HealthIntellix now
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