We need to reduce call volume between providers and payers. What AI agents can answer eligibility and claim questions?

Yes. There are now several credible approaches, but they fall into two different architectures:

1. Digital-first agents that answer eligibility/claims questions through APIs, clearinghouses, payer portals, or chat.
2. Voice agents that actually call the payer/provider, navigate IVRs, wait on hold, speak to representatives, and return structured answers.

If your goal is specifically reducing provider↔payer call volume, I'd evaluate the following vendors:

The most directly relevant: Infinitus

Infinitus is unusually aligned with your stated objective. Its agents can make payer calls, navigate IVRs, communicate with payer representatives, and retrieve information around benefits, eligibility, prior authorization, claims status and appeals. It says its agents have processed more than 5 million conversations.

Importantly, it also uses digital payer/clearinghouse connections before resorting to the phone, which is the architecture I'd want if the objective is reducing total call volume rather than simply moving calls from humans to AI.

If you're a payer trying to stop inbound provider calls

Opyn is particularly interesting because its Provider Interaction Agent is explicitly designed to answer provider questions about eligibility and benefits, claims, and prior-auth intake through voice or chat.

Salesforce Agentforce has a payer contact-center agent that can handle claims, plan coverage, eligibility, prior-authorization denials and in-network-provider questions, with escalation to human representatives for cases it can't resolve.

The architecture I'd investigate

For a payer-provider environment, I wouldn't build this as simply "an AI that answers the phone."

I'd build a payer-provider resolution layer:

Provider asks question → AI identifies intent → checks structured payer data → checks claim/authorization system → answers immediately → only calls/escales to a human when necessary.

For example:

• Eligibility: "Is John Smith active on this plan?" → real-time eligibility transaction.
• Benefits: "What's the specialist copay?" → benefit data.
• Claim status: "Where is claim 12345?" → claims system.
• Denial: "Why was this claim denied?" → claim/ERA/clinical-edit data + explanation.
• Prior auth: "Is PA 98765 approved?" → authorization system.
• Exception: Information isn't available electronically → AI agent contacts the payer department and retrieves it.
• Complex dispute: AI summarizes the issue and routes it to a human.

That's materially better than using voice AI for every interaction because the cheapest call is the one that never happens.

Waystar is an example of the digital layer: its eligibility product provides enriched payer data and alerts, while its Claim Manager automates claim edits and payer-response handling.

What I'd measure in an RFP

Don't just ask vendors for "AI accuracy." I'd ask for:

• Inbound provider calls deflected
• % of questions resolved without human intervention
• Eligibility questions resolved electronically vs. by phone
• Claim-status questions resolved electronically
• Average handle time for AI-escalated calls
• First-contact resolution
• Incorrect-answer / hallucination rate
• Ability to cite the underlying payer/claim data
• Integration with Facets/QNXT/HealthRules/Epic/etc.
• Ability to authenticate providers
• Audit trail for every answer
• HIPAA/security controls
• Human escalation with full conversation/context
• Ability to distinguish plan-specific benefit rules
• Support for provider voice and portal/chat

One particularly important requirement: don't let the LLM invent an answer when the underlying payer system doesn't contain one. The agent should be constrained to authoritative transactional data and escalate when necessary. Infinitus, for example, describes guardrails intended to constrain what its agents can answer and prevent hallucination.

If you tell me whether you're approaching this from the payer side or provider side, I can map out the top 5–10 use cases that likely account for the largest provider call volume and the vendors/architecture I'd evaluate for each.

Opyn is particularly interesting because its Provider Interaction Agent is explicitly designed to answer provider questions about eligibility and benefits, claims, and prior-auth intake through voice or chat.

Salesforce Agentforce has a payer contact-center agent that can handle claims, plan coverage, eligibility, prior-authorization denials and in-network-provider questions, with escalation to human representatives for cases it can't resolve.

The architecture I'd investigate

For a payer-provider environment, I wouldn't build this as simply "an AI that answers the phone."

I'd build a payer-provider resolution layer:

Provider asks question → AI identifies intent → checks structured payer data → checks claim/authorization system → answers immediately → only calls/escales to a human when necessary.

For example:

To drastically cut down the friction and phone tag between providers and payers, healthcare organizations are deploying agentic AI and specialized autonomous workflows . These tools move past traditional rigid interactive voice response (IVR) systems, using large language models (LLMs) and API integrations to converse naturally and pull real-time adjudication data.

Key AI agents and platforms tackling eligibility and claim status questions include:

• Oracle Health Eligibility Verification Agent : Designed to parse deep member benefit structures, active coverage, copays, and deductibles, helping providers get precise price transparency and avoid manual phone calls.
• AR Follow-Up & Claims Status Agents : Autonomous agents (offered via revenue cycle automation firms like AnnexMed AI Agents and specialized platforms) query payer adjudication engines directly to return real-time line-item status on pended, processed, or denied claims without human queues.
• Conversational GenAI Payer Gateways : Enterprise conversational AI platforms (from vendors like Nuance/Microsoft, Kore.ai, and Amelia) build secure, domain-specific virtual assistants that authenticate provider NPI/TIN credentials and securely answer natural-language queries regarding claim status, check tracing, and eligibility verification via voice or chat portals.
• Denial & Exception Escalation Agents : Specialized agents that analyze specific payer adjudication rules, isolate why a claim or eligibility check is failing, and either resolve it or securely prompt the provider with the precise missing data element.

To help narrow down the best architecture or vendor approach, tell me:

• Are you operating primarily from the payer side (looking to deflect inbound calls) or the provider side (looking to automate outbound status checks)?
• What is your current EDI / EHR infrastructure?

Yes. If the goal is specifically reducing provider↔payer call volume, I’d separate the market into two types of AI agents:

1. Payer-side "provider services" agents

These answer the incoming provider call, authenticate the caller, query the payer's systems, and give the answer without involving a human representative.

For example, EHVA markets a voice agent specifically for payer/provider-services calls covering:

• Eligibility verification
• Benefits inquiries
• Claim status
• Prior-authorization status
• 24/7 automated call handling

Its published 2026 case study reports 81% autonomous resolution of in-scope calls and about 13,500 provider calls/month for a TPA deployment.

This is probably the most direct solution if you are the payer and want to deflect calls from your provider-services representatives.

2. Provider-side "payer call" agents

These agents work in the opposite direction: they call the payer on behalf of the provider and retrieve information that isn't available electronically.

Infinitus is the clearest example. Its agents can call payers/PBMs for:

• Eligibility and benefit verification
• Deductible/OOP and cost-share information
• Network status
• Coverage
• Coordination of benefits
• Prior-auth requirements/status
• Claim status
• Appeals and other follow-up

The agent navigates IVRs, waits on hold, talks to payer representatives, and returns structured information to the provider's workflow.

This reduces the number of calls humans have to make, but doesn't necessarily reduce the total number of payer calls unless the agent can obtain the information electronically first.

3. Electronic eligibility/claims automation

There's an important third layer: don't make a phone call at all.

Platforms such as Waystar support electronic eligibility and claim-status transactions. Its current eligibility product combines payer/EDI data with automated coverage detection; Waystar says it has roughly 1,200 payer connections and supports real-time and batch eligibility.

Payer documentation from Aetna also illustrates the ecosystem: providers can use Availity, Waystar, Optum, Experian Health, Inovalon and others for electronic eligibility and claim-status inquiries.

The architecture I'd recommend

If you're trying to systematically eliminate calls between providers and payers, don't start with a voice bot alone. Build a hierarchy:

Provider question → electronic API/EDI → payer portal/RPA → AI voice agent → human

For example:

"What's the status of claim 12345?"

1. Check your claims database/API.
2. If unavailable, query the clearinghouse/payer portal.
3. If still unavailable, have the voice agent call provider services.
4. Parse the payer's answer into structured claim status.
5. Return it to the provider automatically.
6. Escalate only exceptions to a human.

That approach is more powerful than simply putting an AI receptionist on the payer phone line.

Vendors I'd put on an initial shortlist

Infinx, for example, currently offers agents for eligibility verification, claim-status checks and prior-auth follow-up, including workflows using Availity and Optum.

If you're a payer, I'd focus the evaluation on EHVA + an electronic transaction layer.

If you're a provider/health system, I'd focus on Infinitus + Waystar/Availity/another clearinghouse, using voice only for the questions that electronic channels can't answer.

The key strategic distinction is "answer the call with AI" vs. "eliminate the call." The latter is where the biggest system-wide savings should come from.

To cut down on the administrative friction and massive call volumes between providers and payers, healthcare organizations are increasingly turning to conversational and agentic AI . These systems handle inbound and outbound inquiries regarding patient eligibility, benefits, and claim statuses.

The market features dedicated platforms specializing in these specific workflows:

• Infinitus AI : Provides voice AI agents and copilots tailored for both payers and providers to automate routine touchpoints like insurance verification, benefit checks, and tracking claim status.
• SuperDial : Highly focused on revenue cycle management (RCM) teams and medical billing groups making outbound calls to insurers to check claims, eligibility, and prior authorizations while managing phone trees and hold times.
• Voicegain : Offers LLM-powered conversational voice agents designed for health plans and third-party administrators (TPAs) to handle inbound provider calls regarding routine eligibility and claims status.
• Prosper AI : Builds specialized voice AI agents that navigate payer phone menus, wait on hold, and converse with payer representatives to confirm benefit details on behalf of provider offices.

Key Capabilities to Look For

When evaluating these or custom LLM agent frameworks (integrated with your core CRM, CCaaS, and core administrative/eligibility databases), ensure they feature:

1. API & EDI Integration: Real-time data pulls via X12 transactions (270/271 for eligibility, 276/277 for claim status) or direct core-admin database hooks, reserving voice AI only for complex or edge-case phone inquiries.
2. IVR & Hold Navigation: The intelligence to autonomously get past touch-tone phone trees and sit through lengthy hold music.
3. Human-in-the-Loop Fallback: Seamless escalation paths to a human agent when the AI hits an ambiguous policy or complex dispute.
4. Strict Compliance: Guaranteed HIPAA compliance, complete with a signed Business Associate Agreement (BAA) and SOC 2 Type II certification.

If you'd like to narrow this down, tell me:

• Are you looking to solve this from the provider side (making calls to payers) or the payer side (receiving calls from providers)?
• What core administrative/EMR/claims platforms do you need the AI to integrate with?

Reducing friction and call volume between providers and payers requires moving past rigid, legacy EDI 270/271 transactions (which often return generic or incomplete data) into agentic AI automation.

Modern AI agents tackle eligibility, benefits, and claim status by securely logging into source portals, interacting via conversational voice, or parsing unstructured responses just like a human staff member would.

Categories of AI Agents for Eligibility & Claims

• Source-Portal & Browser-Agentic AI:
  • What they do: Instead of accepting a limited clearinghouse response, these AI workers autonomously navigate payer web portals (like Availity or direct payer sites) using agentic browser automation to pull granular, CPT-level benefit breakdowns and real-time claim adjudication statuses.
  • Key examples: Honey Health (specializes in agentic browser-based source verification).
• Voice & Conversational AI Agents:
  • What they do: Automate outbound or inbound interactive voice touchpoints with payers. They handle conversational phone trees, talk to payer IVRs or live reps, check complex claim statuses, track down payment discrepancies, and verify network status without tying up human staff.
  • Key examples: Infinitus AI (provides dedicated voice and digital AI agents built specifically for complex provider-payor touchpoints).
• Revenue Cycle Management (RCM) AI-Native Agents:
  • What they do: Purpose-built digital workers embedded into practice management or EHR systems that run end-to-end eligibility verification queues and flag denial codes or claim bottlenecks proactively.
  • Key examples: Thoughtful AI (features specialized eligibility agents like EVA), Infinx (combining AI with human-in-the-loop validation), and major platform layers like Availity, Waystar , and Notable which have integrated advanced generative and agentic AI layers.

Implementation Strategies to Deflection

1. Deploy Portal Agents First: Target the top 5 payers responsible for 80% of your manual eligibility and claims calls. Deploy browser-agentic workers to scrape and feed structured data straight into your EHR/Billing system before appointments.
2. Use Voice Agents for IVR/Phone Deflection: For payers lacking robust APIs or real-time web data, deploy voice AI assistants to handle the tedious interactive phone queues and extract claim payment details.
3. Establish Human-in-the-Loop Thresholds: Set confidence scores for the AI; if an eligibility or claim detail is ambiguous or flags a potential denial, route it smoothly to a human specialist with the AI's pre-compiled research attached.

If you'd like to narrow this down, let me know:

• Are you looking for outbound voice agents to call payers or portal-scraping/API agents for front-end workflows?
• What EHR or practice management system are you currently using?

Yes. If the goal is specifically to reduce provider↔payer call volume, I’d separate the market into two types of agents:

1. Agents that answer providers’ questions directly — payer-side AI/voice agents.
2. Agents that eliminate the need for the provider to call — provider-side eligibility/claims agents that retrieve the answer automatically.

The strongest categories

The interesting part is that this is already becoming a fairly mature vendor category. For example, EHVA specifically markets an AI voice agent for payer-provider calls covering claims status, eligibility and benefits, reporting 81% autonomous resolution for its in-scope calls.

On the provider/RCM side, Infinitus, SuperDial, Prosper AI, Tahoma AI, QuickIntell and Operator Labs are examples of companies attacking the payer-call problem. Tahoma, for example, has separate eligibility, claims, denial, A/R and payment agents.

If you're a payer, I'd prioritize these

1. Provider Eligibility Agent — highest-volume opportunity

A provider calls:

“Can you verify coverage for member 12345 for DOS 9/15?” The agent authenticates the provider, retrieves the member/benefit information, and answers conversationally. Ideally it also pushes the result into the provider's portal/API so the call never happens.

2. Claim Status Agent

This is probably the next big one:

“What's the status of claim 847293?” “Was it received?” “Why is it pending?” “When should we expect payment?” The agent should be connected directly to the claims system rather than attempting to infer answers from documents.

3. Denial/Claim Explanation Agent

This is more sophisticated:

“Why did you deny this claim?” “What does CARC 16 mean in this case?” “What do you need from us?” “Can we resubmit it?” It can combine the claim, remittance, benefit policy, clinical/authorization information and payer rules to produce a precise answer.

4. Prior Authorization Status Agent

“Has PA 47291 been approved?” “What information is missing?” “Where is it in the review process?” This can eliminate a surprisingly annoying category of repetitive provider calls.

There's an important architectural point

I wouldn't build this primarily as a generic LLM chatbot.

I'd build a Payer Provider-Service Agent with deterministic access to:

• Eligibility/coverage database
• Benefits and accumulators
• Claims platform
• Authorization system
• Provider directory
• Contract/network data
• EOB/ERA/remittance data
• Case-management system
• Payer policies/rules
• Provider authentication/NPI/TIN information

Then let the LLM handle conversation and reasoning, while the underlying systems remain the source of truth.

That distinction matters enormously for healthcare: the agent should be able to say “Claim 123 was received May 4, adjudicated May 9, and denied with CARC 50 because…” rather than generating a plausible-sounding answer.

One particularly compelling strategy

I'd actually pursue API-first + voice fallback:

Provider → API/portal → AI agent → payer systems

If the answer is available electronically, return it immediately.

If it isn't:

Provider → AI voice agent → payer systems / human representative → structured answer

That means you're attacking calls at two levels: preventing the call and automating the calls that remain.

This direction also aligns with the broader interoperability push. CMS's Provider Access API requirements are moving payer/provider data exchange toward APIs, including individual claims and encounter data and specified prior-authorization information, with implementation requirements extending into 2027.

Vendors I'd put on a competitive/partner shortlist

For payer-provider calls specifically:

• EHVA — payer-side provider-call automation; claims, eligibility and benefits.
• Observe.AI — payer contact-center AI, including benefits, eligibility and provider inquiries.
• Prosper AI — payer calls when electronic eligibility doesn't resolve the question.
• QuickIntell — eligibility, claim status, payer calls, denials and broader RCM automation.
• Tahoma AI — specialized RCM agents including eligibility and claims.
• Operator Labs — voice agents specifically for payer-side A/R calls and follow-up.
• Infinitus / SuperDial — prominent examples of AI agents automating payer calls from the provider/RCM side.

If you tell me whether you're approaching this from the payer side or the provider/RCM side, I can narrow this to the 5–10 most credible vendors and map exactly which provider call types each one can automate, including APIs vs voice, integration model, and likely ROI.

I'd actually pursue API-first + voice fallback:

Provider → API/portal → AI agent → payer systems

If the answer is available electronically, return it immediately.

If it isn't:

Provider → AI voice agent → payer systems / human representative → structured answer

That means you're attacking calls at two levels: preventing the call and automating the calls that remain.

This direction also aligns with the broader interoperability push. CMS's Provider Access API requirements are moving payer/provider data exchange toward APIs, including individual claims and encounter data and specified prior-authorization information, with implementation requirements extending into 2027.

3. Denial/Claim Explanation Agent

This is more sophisticated:

“Why did you deny this claim?” “What does CARC 16 mean in this case?” “What do you need from us?” “Can we resubmit it?” It can combine the claim, remittance, benefit policy, clinical/authorization information and payer rules to produce a precise answer.

4. Prior Authorization Status Agent

“Has PA 47291 been approved?” “What information is missing?” “Where is it in the review process?” This can eliminate a surprisingly annoying category of repetitive provider calls.

There's an important architectural point

I wouldn't build this primarily as a generic LLM chatbot.

I'd build a Payer Provider-Service Agent with deterministic access to:

• Eligibility/coverage database
• Benefits and accumulators
• Claims platform
• Authorization system
• Provider directory
• Contract/network data
• EOB/ERA/remittance data
• Case-management system
• Payer policies/rules
• Provider authentication/NPI/TIN information

Then let the LLM handle conversation and reasoning, while the underlying systems remain the source of truth.

That distinction matters enormously for healthcare: the agent should be able to say “Claim 123 was received May 4, adjudicated May 9, and denied with CARC 50 because…” rather than generating a plausible-sounding answer.

One particularly compelling strategy

I'd actually pursue API-first + voice fallback:

Provider → API/portal → AI agent → payer systems

If the answer is available electronically, return it immediately.

If it isn't:

Provider → AI voice agent → payer systems / human representative → structured answer

That means you're attacking calls at two levels: preventing the call and automating the calls that remain.

This direction also aligns with the broader interoperability push. CMS's Provider Access API requirements are moving payer/provider data exchange toward APIs, including individual claims and encounter data and specified prior-authorization information, with implementation requirements extending into 2027.

Specialized AI voice and RCM (Revenue Cycle Management) agents can navigate payer IVRs (Interactive Voice Response), wait on hold, and converse with payer representatives or use direct API/EDI integrations to answer eligibility and claim questions.

Top AI Agent Solutions for Provider-Payer Workflows

• Prosper AI : Voice AI agents that handle RCM workflows like claims status tracking, EOB (Explanation of Benefits) retrieval, and benefits verification by calling payers directly or connecting via clearinghouses.
• Stedi : Automates data interchange and powers outbound voice AI agent workflows with rapid eligibility and insurance discovery checks.
• OmniMD AI Front Desk & RCM: Features payer-calling agents designed to automate benefits verification, prior authorizations, and claim status checks before patient visits.
• Assort Health : Specializes in real-time insurance eligibility verification, billing Q&A, and routing complex inquiries.

Key Capabilities Comparison

If you'd like, let me know:

• Are you looking for tools to automate outbound calls to payers or inbound provider portals?
• Which EHR or clearinghouse systems are you currently using?

I can narrow down the best platform fit for your organization.

To reduce provider–payer call volume, the highest-value AI agents are usually provider service agents and revenue cycle agents that can answer repetitive questions, retrieve data, and complete follow-up tasks without human intervention.

AI agents that can handle eligibility questions

1. Eligibility & Benefits Verification Agent

Handles:

• Is the patient covered on the date of service?
• Active/inactive coverage
• Plan name and network status
• Deductible remaining
• Copay/coinsurance
• Out-of-pocket maximum
• Benefit limitations
• Coordination of benefits indicators

Typical integrations:

• Payer eligibility APIs (270/271)
• Clearinghouses
• Payer member systems
• Provider portals
• EHR/practice management systems

Example vendors offering eligibility-focused agents include Tahoma AI and SuperDial.

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AI agents that can handle claim questions

2. Claims Status Agent

Handles:

• “Where is my claim?”
• Claim received/accepted status
• Pending vs. processed status
• Payment date
• Allowed amount
• Denial reason
• Remittance details
• Next action required

These agents can retrieve information from:

• Claims platforms
• EDI transactions (e.g., 276/277)
• Payer portals
• Clearinghouses
• Call-center systems

Voice-based payer interaction agents can also navigate payer phone systems when APIs or electronic data are unavailable.

---

Other agents that reduce related payer/provider calls

3. Prior Authorization Status Agent

Handles:

• Auth submitted?
• Approval/denial status
• Missing documentation
• Expiration dates
• Next steps

EHVA.ai### 4. Denial Management Agent

Handles:

• Why was the claim denied?
• Is the denial correct?
• What documentation is needed?
• Draft appeal workflows
• Track appeal deadlines

GetMax Healthcare Solutions QuickIntell### 5. Provider Service Voice Agent

A front door for provider calls:

• Answers natural-language questions
• Authenticates provider identity
• Retrieves eligibility/claims information
• Escalates exceptions with a full transcript and context

Opyn Health EHVA.ai

A practical target architecture

A payer trying to reduce call volume would typically deploy:

The biggest impact usually comes from starting with the top 20–30 provider call reasons, connecting the agents to authoritative payer data sources, and routing only exceptions to human representatives.

For a payer/provider environment, I would prioritize:

1. Eligibility + benefits
2. Claim status
3. Prior authorization status
4. Denial explanations
5. Provider voice self-service

Those workflows are high-volume, structured, and have clear answers—making them strong candidates for automation.

The biggest impact usually comes from starting with the top 20–30 provider call reasons, connecting the agents to authoritative payer data sources, and routing only exceptions to human representatives.

For a payer/provider environment, I would prioritize:

1. Eligibility + benefits
2. Claim status
3. Prior authorization status
4. Denial explanations
5. Provider voice self-service

Those workflows are high-volume, structured, and have clear answers—making them strong candidates for automation.

Yes. If the goal is specifically to reduce provider↔payer phone calls, I’d separate the market into two types of AI agents:

1. Provider-side agents that answer eligibility/claims questions without the provider calling the payer.
2. Payer-side provider-services agents that actually answer the inbound calls, using live member/claims data.

The second category is the more direct solution if you are the payer.

The strongest agent use cases

There are already vendors doing substantial pieces of this. For example, EHVA markets a payer-side voice agent specifically for provider calls involving eligibility, claims status, benefits and authorization status. It reports an 81% autonomous-resolution rate for a particular TPA deployment, so I'd treat that figure as a vendor-reported case study rather than a market benchmark.

SuperDial takes the complementary approach: agents work across payer phone calls, portals, APIs and EDI to retrieve eligibility, benefits, authorization and claim information for revenue-cycle teams.

On the provider side, Tahoma AI has separate agents for eligibility, claims, denials, payments and A/R, including portal interaction.

What I'd build for a payer

If you're trying to take 50–80%+ of repetitive provider-service calls off the phone, I'd prioritize:

1. Claim Status Agent

This is probably the highest-volume opportunity.

Provider says:

“I'm calling about claim 847392. Has it been processed?” Agent authenticates the provider, retrieves the claim, and responds:

• Received: June 12
• Status: Processed
• Allowed: $142
• Paid: $118
• Patient responsibility: $24
• Payment date: June 24
• EFT trace: XXXXX
• Explanation of benefits available electronically

No human agent needed.

2. Eligibility & Benefits Agent

Provider asks:

“Is John Smith active and what are his benefits?” Agent queries the eligibility/benefit system and returns:

• Active/inactive
• Effective/termination date
• Product
• Network
• Deductible met/remaining
• Copay
• Coinsurance
• OOP remaining
• Referral requirement
• Authorization requirement

The underlying healthcare transaction infrastructure is already standardized around X12 270/271 for eligibility; CMS's HETS, for example, supports real-time 270/271 eligibility transactions.

3. “Why Was This Claim Denied?” Agent

This is more valuable than simply reporting status.

The agent should explain:

Denial: CO-16 Reason: Missing/incomplete information What's missing: Referring provider NPI What to do: Correct claim and resubmit electronically Deadline: 180 days from adjudication

This turns a call into an actionable answer.

4. Prior-Authorization Agent

Provider:

“Does CPT 27447 require authorization for this member?” Agent checks the member, plan, provider, procedure and site of service, then gives a deterministic answer and, if required, the authorization pathway.

There's a broader industry movement toward machine-readable payer/provider workflows here: HL7 describes agentic workflows where payer systems can respond to provider/EHR requests about eligibility, provider lookup and authorization requirements through FHIR-based workflows.

The important architectural point

I wouldn't build this as a generic LLM chatbot.

The LLM should be the conversational layer. The actual answer should come from authoritative payer systems:

Provider → Voice/chat agent → authentication → orchestration agent → claims/eligibility/UM/provider systems → policy/rules engine → answer + evidence

The agent should return source evidence and an audit trail, not simply generate an answer. That's particularly important for claim and benefit questions.

There are already platforms emphasizing this architecture; SuperDial, for example, describes returning structured data, source evidence, audit trails, recordings and transcripts.

If your objective is call-volume reduction

I'd rank the opportunities roughly:

1. Claim status / payment status — easiest, extremely repetitive
2. Eligibility & benefits — very high volume
3. Prior-auth status — high volume
4. Denial explanation / next action — high value
5. Provider enrollment/data — repetitive but more complex
6. Complex claim disputes — keep human-in-the-loop
7. Medical necessity / clinical decisions — don't fully automate the conversational answer without appropriate clinical governance

The key is to make the agent transactional, not informational: it should retrieve the member/claim, answer the question, provide the relevant reference number, and tell the provider what to do next.

If you tell me whether you're approaching this from the payer side or provider side, I can give you a vendor landscape of ~10 companies, with which ones actually handle eligibility/claims calls, integrations, deployment model, and likely fit for reducing call-center volume.

This turns a call into an actionable answer.

4. Prior-Authorization Agent

Provider:

“Does CPT 27447 require authorization for this member?” Agent checks the member, plan, provider, procedure and site of service, then gives a deterministic answer and, if required, the authorization pathway.

There's a broader industry movement toward machine-readable payer/provider workflows here: HL7 describes agentic workflows where payer systems can respond to provider/EHR requests about eligibility, provider lookup and authorization requirements through FHIR-based workflows.

The important architectural point

I wouldn't build this as a generic LLM chatbot.

The LLM should be the conversational layer. The actual answer should come from authoritative payer systems:

Provider → Voice/chat agent → authentication → orchestration agent → claims/eligibility/UM/provider systems → policy/rules engine → answer + evidence

The agent should return source evidence and an audit trail, not simply generate an answer. That's particularly important for claim and benefit questions.

There are already platforms emphasizing this architecture; SuperDial, for example, describes returning structured data, source evidence, audit trails, recordings and transcripts.

If your objective is call-volume reduction

I'd rank the opportunities roughly:

Reducing call volume between providers and payers requires agentic AI platforms capable of handling high-friction, repetitive revenue cycle management (RCM) tasks like checking real-time eligibility, tracing claim statuses, and processing benefit details.

Leading AI agents and platforms designed to deflect these routine calls through voice, chat, and automated portal integration include:

• Kore.ai HealthAssist : Provides enterprise-grade, HIPAA-compliant multi-agent orchestration across voice, SMS, and chat. It specifically automates real-time eligibility checks, claims status, payment processing, and switchboard routing.
• Honey Health : Utilizes an agentic AI approach that navigates payer portals directly to surface source-accurate, CPT-level benefits that standard electronic data interchange (EDI) transactions might miss.
• Prosper AI : Deploys HIPAA-compliant voice agents built specifically to tackle high-friction phone work in patient access and RCM, including navigating complex payer interactive voice response (IVR) systems.
• Hyro : A conversational AI platform that unifies voice, web chat, and SMS to deflect routine provider and patient access tasks, featuring deep integration architectures like Epic.
• Replicant / PolyAI / Talkdesk : Voice-first contact center AI solutions widely utilized in healthcare to replace rigid legacy IVRs with natural-language conversational agents that can look up live claims and eligibility data on the fly.
• Traditional RCM & Clearinghouse Giants (Availity, Waystar, Experian Health) : These established ecosystems have layered advanced AI-native eligibility and claims assistants directly onto their massive existing clearinghouse and 270/271 data rails.

To help narrow down the right solution, tell me:

• Are you looking at this from the payer side (deflecting inbound provider calls) or the provider side (calling/checking payers)?
• Do you primarily need voice/telephony agents or portal/API automation?