Health Plans

Your Payer workflows. Your model. Your intelligence.

ACTAVA helps payers put AI agents to work across complex operations, with potential starting points in provider credentialing, member onboarding, benefits configuration, or claims operations. We connect enterprise systems, policies, and expert judgment to reduce manual work and improve turnaround times—while building operational intelligence your organization owns and controls.

Half of health plans already run AI. The tail is still manual.

A payer runs thousands of distinct workflows, and vendors build for the few dozen with the biggest transaction volumes. Everything below that line stays with your staff: each remaining workflow is too small to justify a platform purchase and too steady to staff away, so the tail persists year after year while the RFPs go to the head.

What the tail is worth
$21B

Still recoverable through full automation of manual transactions.

50%+

Health plans already using AI tools in administrative workflows.

$150–300M

Administrative savings AI can unlock per $10B of payer revenue.

The 2025 CAQH Index counted $258 billion in administrative cost already avoided through automation, with $21 billion still on the table in the manual and partially manual work that remains. More than half of health plans run AI tools in administrative workflows, yet the savings sit untouched because they hide in hundreds of small workflows rather than one big one. McKinsey sizes the administrative prize at $150 to $300 million for every $10 billion of payer revenue, before any medical cost effect. Read the 2025 CAQH Index →

Workflow volume, illustrative
Point solutionsYour teams, by hand

Vendors build for the head of the curve. The tail is where your teams spend their weeks.

General-purpose models can't carry this work alone.

On χ-BENCH, frontier models fail 72% of complex U.S. healthcare workflows, and fewer than 8% of agents stay successful when a task repeats. What you build around the model decides whether an agent is deployable.

Read the χ-BENCH study
Payerverse· The payer enterprise map

Explore where agents go to work across the payer enterprise, department by department.

Twelve departments, two platforms, one map — the tail made concrete. Every department answers the same questions: the jobs to be done, the workflow bucket its agents live in, the library agents ready to deploy, the tail still run by hand, and the human who holds the decision. Hover a department for its intel; open one to see what would run there.

Try the workbenchBrowse the payer agent library →
The Payerverse map · Payer enterprise view12 departments
Operations coreThe back office that runs the plan
Member experience ringEvery function that touches a member

Hover a department for intel · Click to open it below · Plum = featured

Department· 4 of 12

Appeals & Grievances

Where denials get a second look, under regulatory clocks and CMS scrutiny. Agents re-read the evidence, catch the error, and draft the resolution while a credentialed human holds every determination.

2 library agents2 bucketsFeatured

Position in the value stream

  1. Shop
  2. Quote
  3. Enroll
  4. Access
  5. Authorize
  6. Adjudicate
  7. Pay
  8. Appeal
  9. Renew

Jobs to be done

  • Triage incoming appeals
  • Re-review denials against policy
  • Resolve grievances
  • Meet regulatory clocks
  • Assemble external-review packets

Library agents, ready to deploy

Each card opens the agent in the Workflow Library.
Utilization ManagementCompliance
Behavioral Health Prior Authorization AgentRuns the authorization lifecycle through concurrent review and compliant denial review with peer-to-peer coordination — the same re-review an appeal needs.payer-5a · View agent
Claims & Denial ManagementCompliance
Post-Denial Management AgentAssesses appeal merit with human oversight, generates the packet, tracks resolution, and closes the loop to prevention.vbc-7b · View agent

The tail — built on KORA

Workflows no vendor's roadmap reaches
  • Appeal intake classificationType, urgency, and line of business at arrival; expedited cases surfaced instantly.
  • Grievance root-cause miningCluster narratives to find the systemic issue weeks before the complaint rate does.
  • Determination-letter QARequired language, reading level, appeal-rights text — checked before release.
  • External-review packet assemblyThe complete, indexed, deadline-compliant packet for the IRE.
  • Denial-reason clusteringTrace overturns back to the intake or configuration error that caused them.

Each of these is too small to justify a platform purchase and too steady to staff away. Your team builds it on KORA in days, with the same gates, audit log, and evaluation the library agents ship with. How KORA builds the tail →

The ACTAVA platform

One platform runs the whole tail. Build the agent, govern it, and own the model underneath.

Build any workflow with KORA

When a workflow is too niche for any vendor's roadmap, your operations team builds the agent on ACTAVA KORA in days. Agents work across chat, voice, and background processes with HIPAA controls and audit logs on every action. Each one tests against your rubrics and χ-BENCH-grade simulations before it touches production, then improves from reviewed work.

Explore KORA

Govern every agent with KORA

A hundred small agents create a hundred audit surfaces, and ACTAVA KORA governs all of them from one place. It writes your AI policies, trains your teams on them, and tests every agent against NIST AI RMF, HIPAA, and CMS requirements. When a regulator asks how an agent made a decision, the evidence is already assembled.

Explore compliance

Own the intelligence with CURA

Long-tail economics fail when every agent rents tokens from a frontier lab. CURA, ACTAVA's trillion-parameter healthcare model, comes tuned on payer rules and parity policy, runs in your own cloud environment at a fraction of frontier cost, and stays yours, under your control. Operate CURA, frontier models, or both without rewriting a workflow.

Explore CURA
See the work change· Operator's seat

Agent-assisted appeal resolution. You hold the decision gate.

You are the appeals nurse reviewer. An agent has pre-worked a member appeal — read the documents, matched the policy, and found something. The decision is still yours.

Illustrative prototype · sample tenantAI pattern · document intelligence + agentic workflowHuman-in-the-loop · required
Appeal Resolution AgentSample tenant · illustrative
Agent is working A human must act

New appeal in your queue

Standard (pre-service) appeal · received via member portal

APL-2026-08417
Member
M. Alvarez · Medicare Advantage
Service denied
Outpatient knee arthroscopy
Denial reason
“Site of care not medically necessary”
Regulatory clock
30 days · 26 remaining

The appeal packet holds four documents: the member's letter, the original denial, the surgeon's clinical notes, and the plan's medical policy. In the old workflow you would now open five systems and start reading. Here, the agent goes first.

Full workbench transcript

You are the appeals nurse reviewer. An agent has pre-worked a member appeal — read the documents, matched the policy, and found something. The decision is still yours.

APL-2026-08417. Standard (pre-service) appeal · received via member portal. Member: M. Alvarez · Medicare Advantage. Service denied: Outpatient knee arthroscopy. Denial reason: “Site of care not medically necessary”. Regulatory clock: 30 days · 26 remaining.

  1. Ingested the appeal packet (0.8s): 4 documents parsed · member letter, denial, clinical notes, policy MP-217
  2. Extracted the clinical facts (1.4s): Diagnosis M23.51 · failed 8 weeks of conservative therapy · BMI 27 · ASA class II
  3. Matched the policy criteria (2.1s): MP-217 site-of-care criteria: 6 of 6 clinical criteria met for the ASC setting
  4. Found a discrepancy (2.9s): The denial evaluated the request against hospital outpatient criteria, but the request was for an ambulatory surgery center. Wrong site code applied at intake.
  5. Drafted the recommendation and the member letter (3.6s): Recommendation: overturn. Draft letter queued for your review.

Agent recommendation: overturn the denial. The denial appears to rest on a site-of-care coding error at intake, not on clinical grounds. All six policy criteria for the requested setting are met.

The agent cannot overturn or uphold anything. Adverse and favorable determinations both require a credentialed human. Choose how to resolve APL-2026-08417.

  • Approve the overturn: Accept the agent's finding. Authorization issued for the ASC setting; member and provider notified today. Outcome: Denial overturned · authorization AUTH-77213 issued · resolved in 11 minutes, day 4 of 30.
  • Escalate to the Medical Director: Agree a discrepancy exists, but route the clinical judgment to the MD queue with the agent's packet attached. Outcome: Escalated to the Medical Director with the full agent packet · clock paused at day 4 · MD SLA 48 hours.
  • Uphold the denial: Disagree with the agent. Requires a documented rationale; the case auto-flags for QA sampling and the disagreement feeds model monitoring. Outcome: Denial upheld · your rationale recorded · case flagged for QA sampling and model feedback.

The click-through workflow: 13 steps · 5 systems, ~3–4 days. The agent-embedded workflow: 5 steps · 1 surface · 2 human, ~11 minutes.

Illustrative prototype. The member, plan, policy, and timings are fictional sample-tenant data built to show the shape of the workflow; nothing here is customer telemetry or a performance claim.

Built on KORA from the library's denial-review pattern — see the Behavioral Health Prior Authorization Agent and the Post-Denial Management Agent in Appeals & Grievances above. Open the library agent →

90-day value realization

Start quickly.

Plan your agent roadmap and digital workforce budget.

Build agents immediately with the ACTAVA team beside you.

Integrate your systems of record and launch from the prebuilt library.

Run safely.

Establish your AI policies, frameworks, and evaluation rubrics.

Evaluate agent performance, inspect behavior, and improve it.

Govern every agent and orchestrate them across your systems.

Grow intelligently.

Control model costs with real-time consumption monitoring.

Distribute agent resources as functions and demand shift.

Measure ROI for every agent on a configurable dashboard.

Next step

Payer agents you can govern, prove, and own.

Every agent tests before it deploys, every action lands in the audit log, and the intelligence stays in your cloud, under your control. Name the workflow your team still runs by hand and we'll show you the first agent working against it.