Decagon and Sierra are both strong enterprise AI agent platforms. For a healthtech team, the question is not which is better in the abstract, but whether either fits the way a HIPAA-regulated support team has to handle protected health information, sign a business associate agreement, and produce an audit trail a privacy officer will accept.
This is a decision guide, not a ranking. Decagon and Sierra are two of the most credible AI customer service agent platforms in the market, and most teams evaluating one evaluate the other. They overlap heavily on capability and differ most on pricing philosophy, deployment model, and industry focus. But healthtech adds a layer that generic enterprise CX buying guides skip: handling protected health information (PHI), getting a signed business associate agreement (BAA), and proving the agent's behavior to a privacy and security team before launch. Below we walk through the criteria that decide a healthtech purchase, explain how the main alternatives fit them, and close with the conditions under which Lorikeet is the strongest match.
Healthtech support handles PHI - eligibility, coverage, claims, scheduling, prior authorization, billing - which means a signed BAA and PHI-grade handling are gating requirements, not nice-to-haves.
Decagon and Sierra are both broad enterprise platforms; the key questions for a healthtech buyer are whether the vendor signs a BAA, how it handles PHI, and how deep the audit trail goes.
The deciding criterion in regulated healthcare support is usually whether the agent's behavior is provable before go-live and replayable after, not the headline resolution rate.
Other alternatives - Fin by Intercom, Ada, Forethought, Salesforce Agentforce - each fit a different profile, from drop-in helpdesk AI to broad enterprise suites.
When the deciding factors are PHI handling, a BAA, audit you can replay, and the hardest tickets (eligibility, claims, prior auth), it is worth running Lorikeet alongside any shortlist.
Last updated: June 2026
Most comparison pages try to declare a winner. That framing fails for healthtech support, because the right answer depends on what you are buying for and what your privacy and security teams will sign off on. A consumer health app handling appointment reminders has a different ideal than a platform that touches claims, coverage, and prior authorization. This guide is structured so you can match the criterion that matters most to your business against how each alternative behaves, then decide. We aim to be fair to every platform named, and we say plainly where a healthtech buyer should keep looking.
Why healthtech teams evaluate beyond Decagon and Sierra
Decagon and Sierra are genuinely strong. Sierra was founded by Bret Taylor and Clay Bavor and scaled quickly, reaching $100M ARR in 21 months per TechCrunch, with a signature outcome-based pricing model. Decagon is the high-end enterprise AI agent platform with a white-glove deployment model and, per industry data, a median total contract value near $400,000 per year. Both ship voice, chat, and email, both serve large enterprises, and both negotiate custom pricing rather than publishing rates.
The reason a healthtech team often looks past both is not capability. It is the set of requirements specific to handling protected health information:
Business associate agreement (BAA). Under HIPAA, any vendor that creates, receives, maintains, or transmits PHI on your behalf is a business associate and needs a signed BAA. If a vendor will not sign one, it cannot touch PHI, which rules it out of most healthtech support workflows. The first question is whether the vendor signs a BAA, and the second is what its subprocessors and model providers do with the data.
PHI handling and redaction. Eligibility checks, coverage questions, claim status, and billing all surface PHI. The platform needs PII and PHI redaction, role-based access, and clear data-residency and retention controls so that minimum-necessary principles are supported.
Audit your privacy officer will accept. A transcript is not an audit trail. Healthcare governance expects a replayable record of what the agent did and why, which matters during internal audits and any regulator or partner review.
The hardest tickets are the regulated ones. Appointment scheduling is easy. Eligibility, prior authorization, claim disputes, and coverage determinations are where correctness and provability matter, and where a generalist platform optimized for high-volume consumer CX may be weakest.
A platform can be excellent at enterprise CX and still be a poor fit for healthtech if it does not sign a BAA, handle PHI to a defensible standard, or produce audit artifacts your security team trusts. That is the gap this guide is about. None of this is a knock on Decagon or Sierra as platforms; it is a reminder that healthtech procurement filters on different criteria than generic CX.
The criteria that decide a healthtech purchase
Generic CX buying guides start with deflection rate, response time, and CSAT. In a HIPAA-regulated business those are downstream of correctness, privacy, and provability. The lenses below are the ones a healthtech team should weigh, in roughly the order they tend to gate a deal.
1. BAA and compliance posture
Start here, because it is the fastest disqualifier. Ask whether the vendor will sign a BAA, and read it for scope: which workflows, which data, which subprocessors. Then ask what the underlying model providers do with the data - contractual no-train commitments matter when prompts may contain PHI. Confirm SOC 2, data-residency options, and retention controls. Compliance features should be understood as supporting your HIPAA obligations, not as certifying compliance on your behalf - the covered entity remains responsible, and the vendor is a business associate that supports that responsibility.
2. PHI handling and access controls
Once a BAA is in place, the question is how the platform handles PHI in practice. Look for PII and PHI redaction in logs and prompts, role-based access control so only the right people and systems see the right data, and support for minimum-necessary handling. Ask how PHI flows through the agent, where it is stored, how long it is retained, and who can see it. The goal is a configuration your privacy officer can map to your existing HIPAA policies.
3. Audit and quality assurance
An audit trail in healthtech support is a timestamped, replayable record of every tool call, prompt, and reasoning step the agent made on a ticket. It is the artifact your compliance and privacy teams use during internal audits and partner reviews. A transcript is not enough. The standard to hold is replayability at the reasoning-step level: when a coverage determination or eligibility check goes wrong, you need to point at the exact step that failed. Continuous quality assurance on every ticket, rather than a sampled review, is what separates a platform you can defend from one you hope is behaving.
4. Multi-step workflows on regulated tickets
Most healthtech tickets are sequences, not single questions: verify identity, check eligibility, look up a claim, explain a coverage decision, and escalate if a threshold is crossed. The platform has to chain several tool calls in the right order, keep state, and recover when a system errors mid-chain. Ask what happens when an eligibility or claims system returns a 5xx error partway through, and whether the agent retries, escalates, or rolls back. A platform that escalates on the first hard step is a chatbot, not an agent.
5. Channels and patient experience
Healthtech support spans channels: members call about coverage, message about claims, and email about billing. The agent should be the same agent across chat, email, voice, and SMS, with shared context, so a member who started in chat does not repeat themselves on a call. Probe whether voice runs on the same workflow engine as the other channels or on a separate stack joined by a transcript handoff, and what the voice latency is, since long pauses degrade a sensitive health conversation.
6. Ownership after launch
Healthcare rules and payer policies change. After the launch period ends, can your team change workflows, guardrails, and integrations independently, or does every material change require a vendor services engagement? High-touch deployment shortens time to first production tickets, but post-launch self-sufficiency is what lets you respond quickly to a policy or regulatory change. Settle ownership before signing.
How the main alternatives fit
Here is how the platforms a healthtech team typically evaluates map to those criteria. Treat published figures as directional, since most vendors do not publish rates and contract values vary by company size and volume. Always confirm BAA availability and current compliance scope directly under NDA.
Decagon
Decagon is a top-of-market enterprise AI agent platform with proven scale and a white-glove deployment model, offering per-conversation or per-resolution pricing that the customer selects plus an annual platform fee. For a large healthtech enterprise with the budget for a premium total contract value and limited internal AI engineering, the embedded deployment can be a real benefit. The healthtech-specific questions to settle are whether Decagon will sign a BAA for your workflows, how PHI is handled and redacted, and whether your privacy team can replay a full reasoning-step audit trail rather than a sampled summary. Its strength is breadth, scale, and a selectable pricing model.
Sierra
Sierra's signature is outcome-based pricing - you pay only when the AI fully resolves a case, and escalations cost nothing - with enterprise contracts reportedly in the $50,000 to $200,000 per year range. The appeal is incentive alignment and budget predictability, and its branded-persona deployment suits consumer-facing brands. For healthtech, weigh two things. First, confirm BAA availability and PHI handling, since outcome billing does not change the compliance requirements. Second, consider that any model paid only on full resolution can create a quiet bias toward easy tickets, and in healthtech the hard ones - eligibility edge cases, claim disputes, prior authorization - are the ones that matter. Ask how Sierra handles partial resolutions and the hard cases that escalate.
Fin by Intercom
Fin by Intercom is an AI agent layered on Intercom's messenger and helpdesk, with among the lowest published per-resolution rates in the category and a fast trial-to-deployment path. For a healthtech team already on Intercom that needs to automate simpler, lower-risk ticket types, it can be a quick win. The constraint for regulated healthtech is depth: drop-in helpdesk AI is strongest on retrieval-and-reply and lighter on multi-step regulated workflows. Confirm BAA availability and PHI handling on the Intercom platform before routing anything that touches protected health information through it.
Ada
Ada is an established AI support vendor that expanded from chat into voice and email and pitches a high autonomous resolution rate, with a median contract reportedly around $70,000 per year. It does breadth well and has mature integrations. The consideration for healthtech is that vendors which grew from a chatbot architecture tend to be strongest on breadth and lighter on the deepest multi-step action chains and reasoning-step audit logging - the capabilities hardest to retrofit. Confirm BAA, PHI handling, and audit depth against your specific eligibility and claims workflows.
Forethought and Salesforce Agentforce
Forethought offers a multi-agent stack covering resolution, triage, assist, discovery, and QA and was acquired by Zendesk in 2026, so signing now means signing into Zendesk's roadmap. Salesforce Agentforce is the agent layer for teams standardized on Salesforce, attractive when your systems of record already live there. Both are credible for broad enterprise support. For healthtech, the same gating questions apply: BAA scope, PHI handling, and whether the audit trail meets the replayable, reasoning-step standard your privacy team needs.
When Lorikeet is the strongest match
Lorikeet is an AI customer support platform built specifically for complex and regulated businesses - fintech, financial services, healthcare and healthtech, insurance, and gaming. It builds AI concierges that resolve issues end-to-end rather than deflection chatbots. For a healthtech team whose deciding factors are the criteria above rather than general enterprise CX, Lorikeet is the strongest match under these conditions:
A BAA and PHI handling are gating requirements. Lorikeet is BAA-ready for HIPAA, holds SOC 2, is GDPR-aligned, and provides PII redaction, role-based access control, and data residency in the US, AU, and UK. It maintains contractual no-train agreements with its model providers (OpenAI, Anthropic, Gemini), which matters when prompts may contain PHI. These features are designed to support your HIPAA obligations, not to certify compliance on your behalf - your organization remains the covered entity and Lorikeet operates as a business associate that supports that responsibility.
Guardrails must be provable before go-live. Lorikeet's approach is defence in depth: pre-launch adversarial simulations and red-teaming, inbound message checks, outbound guardrails, and 100% post-facto QA through its Coach agent. The intent is that your privacy and security teams can sign off before launch rather than review after an incident.
Audit and QA are continuous, not sampled. Coach runs automated quality assurance on 100% of tickets with root-cause analysis and resolution verification - the AI evaluating the AI - producing the replayable, reasoning-step record a privacy officer expects. Coach is also deployable standalone at roughly $0.25–$0.30 per ticket.
The hardest tickets are regulated. Lorikeet is built for multi-step resolution on the tickets that matter - eligibility, coverage questions, claim status, billing, and the equivalents of KYC and disputes in adjacent regulated industries - combining natural-language workflows with deterministic structured workflows in a single interaction, all configurable in plain English so your team can change behavior after launch.
Channels include low-latency voice on one engine. Lorikeet runs chat, email, voice (sub-1-second latency), SMS, and WhatsApp on a single engine with shared context, plus outbound re-engagement with compliance controls such as do-not-call and call-hour rules.
You want outcome-anchored pricing without an outcome-only bias. Lorikeet charges roughly $0.80–$0.95 per chat, email, or SMS resolution and roughly $1.20–$1.50 per voice resolution, the customer defines what counts as a resolution, and escalations are not charged. Against a human baseline of roughly $1.25 to $4 per handled ticket, the model is designed to price the hard tickets honestly rather than steer toward the easy ones.
Lorikeet is the narrower, deeper choice. It is purpose-built for regulated industries rather than being a general-purpose enterprise agent, and roughly 80% of its customers are US financial institutions and fintechs, with healthtech as a core adjacent vertical. A team buying primarily for high-volume, low-risk consumer support may find a generalist platform a closer match. The fair caveat: Lorikeet does not chase the broadest set of consumer-CX use cases, and it is a younger company than the largest incumbents in the broader category, which some procurement teams weigh. Where it earns its place on a healthtech shortlist is depth on regulated workflows, PHI handling, provable guardrails, and continuous audit.
Where each can fall short
A fair decision guide names the friction alongside the fit. None of these are disqualifiers; they are trade-offs to price in.
Decagon. The premium total contract value puts it out of reach for smaller healthtech teams, and the white-glove model can leave your team dependent on the vendor for configuration changes past launch. Confirm BAA scope and exactly how much you can change without a services engagement.
Sierra. Outcome-only pricing carries a structural incentive that matters in regulated support: a vendor paid only on full resolution is rewarded for the tickets that resolve cleanly, while the cases that decide your compliance risk are the messy ones. The branded-persona deployment is also more consumer-brand oriented than compliance-first.
Fin, Ada, Forethought, Agentforce. These are credible for broad enterprise support, but for healthtech the common questions are BAA availability, PHI handling depth, and whether the audit trail is replayable at the reasoning-step level. Drop-in and chatbot-origin platforms tend to be strongest on breadth and lighter on the deepest regulated workflows.
Lorikeet. It is deliberately specialized for regulated industries, so its breadth across general consumer CX is narrower than the generalists, and it is younger than the largest incumbents. Its depth on regulated workflows, PHI handling, audit, and QA is the reason it earns a place on a healthtech shortlist despite the narrower surface area.
How to run the decision
Demos are built to look good. Make the decision on the same hard cases across every vendor. Bring your hardest tickets - a failed eligibility check, a disputed claim, a coverage determination that needs a threshold check - and ask each vendor to run them in your stack against your guardrails. Then compare on the criteria in order of what matters most to your business.
Ask whether the vendor will sign a BAA, and read it for scope and subprocessors before anything else.
Ask how PHI is redacted, stored, and retained, and who can see it.
Ask for an end-to-end audit trail of a real decision the agent made last week, with every tool call and the reasoning between them.
Ask what happens when an eligibility or claims system returns a 5xx error mid-chain.
Ask who owns and can change the workflows after the launch period ends.
Model your real ticket mix against each pricing structure, paying attention to cost on the hard 20% of tickets that do not fully resolve.
Decagon and Sierra are both strong, and for many enterprises either is the right answer. For a healthtech buyer whose toughest stakeholders are the privacy and security teams, the questions that separate the alternatives are the same questions that make it worth running Lorikeet alongside them.
