Decagon and Lorikeet are both strong AI agent platforms, but a healthtech buyer is not choosing between two general tools. The question is which one handles protected health information, a signed BAA, an audit trail, and clinical-adjacent workflows in a way your privacy officer can approve before launch. This is a head-to-head on exactly those terms.
Decagon vs Lorikeet is a comparison between two AI customer support platforms evaluated specifically for healthtech: digital health, telehealth, pharmacy, benefits, and care-navigation companies that handle protected health information (PHI) and operate under HIPAA. Decagon is a top-of-market enterprise AI agent platform with deep production deployments and white-glove implementation. Lorikeet is an AI concierge platform built for complex and regulated industries, including healthtech, where workflow depth, defence-in-depth guardrails, and an audit trail compliance teams can replay matter as much as raw resolution volume. This is a fair head-to-head on HIPAA and BAA posture, PHI handling, audit trails, multi-step healthtech workflows, channels, pricing, and deployment, written to help you shortlist rather than to crown a universal winner.
Decagon's strength is enterprise scale and polish: production deployments processing millions of interactions, a strong procurement story, and white-glove implementation with embedded engineering during launch.
Lorikeet's strength is depth on regulated, multi-step tickets: deterministic and natural-language workflows in one interaction, defence-in-depth guardrails, omnichannel including sub-1-second voice, and 100% automated QA via its Coach agent. Roughly 80% of its customers are US financial institutions and fintechs, and it is built for the same regulated bar in healthtech.
On compliance posture, both platforms are SOC 2 and serve enterprise buyers. Lorikeet is BAA-ready for HIPAA, GDPR-aligned, supports PII and PHI redaction and RBAC, and offers US, UK, and AU data residency. For any healthtech evaluation, confirm a signed BAA and current SOC 2 scope with each vendor directly under NDA.
Pricing models differ. Decagon does not publish rates; industry data suggests a platform fee plus per-conversation or per-resolution fees, with a median total contract value near $400,000 per year. Lorikeet prices per resolution: roughly $0.80–$0.95 per chat, email, or SMS resolution and about $1.20–$1.50 per voice, escalations not charged, and the customer defines what counts as a resolution.
Choose Decagon for a top-of-market enterprise platform with the budget and engineering to support a months-long, white-glove deployment. Choose Lorikeet when your hardest healthtech tickets are multi-step, PHI-sensitive, and need behavior your privacy and compliance team can approve before go-live.
Last updated: June 2026
Most AI support comparisons score platforms on a single resolution number. In healthtech that number is the wrong headline. A patient asking "why was my prescription denied" or "can you reschedule my telehealth visit and update my insurance on file" is not a knowledge-base lookup, and a wrong answer is not a refund problem. It can be a PHI disclosure to the wrong person, a missed clinical-escalation path, or a HIPAA exposure your privacy officer has to report. Decagon and Lorikeet both clear easy tickets well. The useful question for a healthtech buyer is what happens on the hard, PHI-touching tickets, and whether the platform's behavior on those is provable before launch. This comparison gives Decagon real credit for enterprise scale and implementation depth, and positions Lorikeet where it genuinely leads, on regulated workflow depth and compliance-approvable behavior.
Decagon vs Lorikeet at a Glance for Healthtech
Decagon · Best for: Large healthtech and enterprise teams with significant support budgets and engineering capacity for a white-glove deployment · Key strength: Enterprise scale, production maturity, and embedded-engineering implementation · Channels: Chat, email, voice · Pricing: Not published; industry data suggests platform fee plus per-conversation or per-resolution, median around $400,000 per year · Compliance: SOC 2; confirm BAA and HIPAA posture directly
Lorikeet · Best for: Complex and regulated healthtech (telehealth, pharmacy, benefits, care navigation) needing end-to-end resolution on PHI-sensitive workflows with audit trails · Key strength: Resolution depth on multi-step regulated tickets; defence-in-depth guardrails; 100% automated QA · Channels: Chat, email, voice (sub-1-second latency), SMS, WhatsApp, plus outbound re-engagement · Pricing: Per resolution (~$0.80–$0.95 chat/email/SMS, ~$1.20–$1.50 voice; escalations not charged) · Compliance: SOC 2, BAA-ready (HIPAA), GDPR-aligned, PII/PHI redaction, RBAC, US/UK/AU data residency
What Each Platform Is Built For
Decagon is a top-of-market enterprise AI agent platform with named customers across high-end consumer and financial brands and production deployments processing millions of customer interactions. It operates on per-conversation or per-resolution pricing with white-glove implementation, including embedded engineering during the launch period. Decagon does enterprise scale well: it is built for large organizations that want a premium, heavily supported deployment and have the procurement appetite and internal resources to match. For a healthtech company at that scale, that polish and production track record are real advantages worth weighing.
Lorikeet is an AI concierge platform built specifically for complex and regulated industries, including fintech, financial services, healthcare and healthtech, insurance, and gaming. Rather than a chatbot or a deflection tool, Lorikeet builds concierges that resolve issues end-to-end across channels, and a second agent, Coach, that runs analytics and 100% automated QA. The design center is the hard ticket: the kind that needs several actions executed in the right order against live systems, with a record a compliance team can replay. In healthtech that means handling PHI-touching workflows, eligibility and benefits questions, prescription and pharmacy coordination, and appointment changes, while keeping a provable behavioral boundary around what the agent may and may not do. Where Decagon optimizes for enterprise scale and a high-touch managed rollout, Lorikeet optimizes for depth and provable behavior on regulated work.
HIPAA, BAA, and Compliance Posture
For a healthtech buyer this is the section that gates everything else. An AI agent that touches PHI is a business associate under HIPAA, which means you need a signed business associate agreement (BAA) with the vendor before the agent processes a single real patient interaction. No feature list substitutes for that document.
Lorikeet is SOC 2 compliant and BAA-ready for HIPAA, GDPR-aligned, and supports PII and PHI redaction, role-based access control, and US, UK, and AU data residency. It also operates under contractual no-train agreements with its model providers, so PHI passed to the underlying models is not used to train them. These features support your HIPAA and privacy obligations; they do not by themselves certify compliance, and any vendor that promises certification or guarantees compliance is overstating it. The work of HIPAA compliance is shared between you and your vendor, and the right posture is one that gives your privacy officer the artifacts to do their half.
Decagon is SOC 2 and serves enterprise customers in regulated-adjacent sectors, and many enterprise AI vendors at its tier will sign a BAA and support HIPAA-aligned deployments. Because Decagon does not publish the specifics, the honest instruction for a healthtech buyer is the same one you would apply to any vendor: ask directly, under NDA, for a signed BAA, the current SOC 2 Type II report and its scope, the data-residency options, and the model-provider data-handling terms. Treat the answers, not the marketing, as the input to your decision. This is not a knock on Decagon; it is the diligence any healthtech procurement team should run on every platform on the shortlist, Lorikeet included.
PHI Handling and Data Boundaries
Beyond the paperwork, the operational question is how PHI moves through the system at runtime. A patient ticket can contain a diagnosis, a medication, a member ID, and a date of birth in a single message. The agent needs to use that information to resolve the ticket without surfacing it to the wrong party, logging it where it should not be logged, or carrying it into a context where it does not belong.
Lorikeet's approach is to make PHI handling part of the configured behavior rather than an afterthought. PII and PHI redaction, role-based access control, and least-privilege scoped tools mean the agent reaches only the systems and fields a given workflow requires. Inbound message checks screen what comes in, outbound guardrails constrain what goes out, and the customer can define disclosure rules, such as verifying identity to a defined standard before discussing any clinical detail. The practical effect is that the data boundary is something you specify and then test, not something you hope holds. Decagon, as an enterprise platform, provides its own controls and redaction capabilities; the depth and configurability of those controls for a specific PHI workflow are exactly the kind of thing to probe in a technical deep-dive during evaluation.
Audit Trails and Examinations
In regulated work the question is not "does it work most of the time" but "can we show what it did, and why, on any given ticket." Healthtech audits, internal privacy reviews, and patient complaints all turn on the ability to reconstruct a specific interaction after the fact.
Lorikeet treats the audit trail as a first-class artifact: a replayable record of the tool calls, prompts, and reasoning steps the agent took on a ticket, in order, with timestamps. That record is built to be read by a compliance or privacy reviewer, beyond what a developer needs to debug a session. Combined with the Coach agent, which runs 100% automated QA on every ticket, a regression in behavior shows up in your own QA before it shows up in a complaint or an audit finding. Decagon maintains logging appropriate to an enterprise platform; for a healthtech buyer the specific test is whether you can replay the full reasoning-plus-tool-call chain for an arbitrary ticket from several weeks ago and hand that record to a reviewer. Ask both vendors to demonstrate exactly that on a real ticket during the evaluation, and compare what they show you.
Multi-Step Healthtech Workflows
The clearest functional difference between the two platforms shows up on a multi-step ticket. "Reschedule my telehealth appointment, confirm my insurance is still active, and tell me what my copay will be" requires verifying identity, reading and writing across a scheduling system and a benefits or eligibility source, applying policy, taking actions in the right order, and recovering gracefully if one step errors, all while keeping PHI inside its boundary.
Lorikeet is built around multi-step resolution as the default case. It combines deterministic structured workflows with natural-language workflows, and the two can run in a single interaction, so a flow that must follow an exact regulated or clinical-safety script can hand off to flexible reasoning and back without leaving the conversation. The Team of Agents capability dispatches sub-agents to call third parties and coordinate, for example contacting a pharmacy on a prescription question or a payer on an eligibility check. The honest tradeoff is that this depth is more configuration than a drop-in chatbot, which is why Lorikeet ships with forward-deployed implementation help rather than expecting you to self-serve the hardest flows alone. Decagon is a capable agent platform that handles complex flows for large enterprises and backs deployment with embedded engineering, which is its way of getting the same hard workflows built correctly. The difference is less whether each can do multi-step work and more how the work gets configured and validated, and at what cost and control profile, which the pricing and deployment sections below make concrete.
Channels
Decagon covers chat, email, and voice, which fits the majority of healthtech support operations and is a real strength of an enterprise platform.
Lorikeet covers chat, email, voice, SMS, and WhatsApp on the inbound side, plus outbound re-engagement over voice, SMS, and email for use cases like appointment reminders and care follow-ups, with compliance controls for do-not-call lists, call-hour rules, and consent. Its voice agent runs at sub-1-second latency with natural conversation and automatic language switching. The architectural point that matters for healthtech is that voice runs on the same workflow engine as chat and email, so the agent carries shared context across channels and can take actions on a call rather than routing to a human. A patient who starts in chat and moves to a phone call does not repeat their member ID, and the agent can reschedule a visit or check eligibility live. For teams whose patients reach support by phone as often as by chat, that single-engine omnichannel design is the practical differentiator.
Pricing
The platforms use different commercial models, and the right one depends on your volume, ticket mix, and internal resources.
Decagon does not publish rates. Industry data suggests an annual platform fee plus per-conversation or per-resolution fees, with a median total contract value near $400,000 per year. Vendors at that tier typically sell embedded engineering as part of the package; the honest read is that the price reflects a premium, heavily supported deployment, which is the right fit for a large healthtech with the budget and the appetite for white-glove service.
Lorikeet prices per resolution and lets the customer define what counts as a resolution. Chat, email, and SMS resolutions run about $0.80–$0.95 each, voice resolutions about $1.20–$1.50, and the Coach QA agent runs about $0.25–$0.30 per ticket and can be deployed standalone. Escalations to a human are not charged, which removes the incentive for the vendor to claim a resolution it did not earn. Set against a human baseline of roughly $1.25 to $4 per handled ticket, per-resolution pricing is designed to track value rather than seats.
The practical question for a buyer is which model maps to your profile. If you are a large healthtech with high, predictable volume and you value a deeply managed rollout, Decagon's enterprise model and embedded engineering can be the right call and is straightforward to plan against once contracted. If your volume is variable, or if you want commercial terms that only bill when the agent does the job you agreed counts, per-resolution pricing with no charge on escalation and a customer-defined resolution maps more closely to value delivered.
Deployment
Decagon's white-glove implementation with embedded engineering is its deployment hallmark. For a large healthtech that wants the vendor to carry much of the build and validation, that high-touch model shortens internal lift and brings experienced hands to a complex rollout. It is a genuine strength for teams that prefer a managed deployment over owning the configuration themselves.
Lorikeet pairs a plain-English configuration model, where workflows, guardrails, and tools are defined in natural language, with a forward-deployed product manager and engineer during implementation. A working sandbox is typically standing in 20 to 30 minutes, with a production deployment operational in about a month. The tradeoff is honest: Lorikeet asks for partnership during implementation because the workflows it targets are complex, but the plain-English model is designed so your team can own and change the workflows after launch rather than depending on the vendor for every adjustment. For a healthtech team that wants to keep clinical-safety and disclosure logic under its own control, that ownership model is worth weighing against a fully managed one.
Where Decagon Is the Stronger Choice
It would be unfair to frame this comparison as if regulated depth were the only thing that matters. For a meaningful set of healthtech buyers, Decagon is the better fit, and for honest reasons.
If you are a large healthtech with a substantial support budget and you want a top-of-market platform with a long production track record at enterprise scale, Decagon's maturity and the fact that it processes millions of interactions across demanding deployments de-risk the decision. If you prefer a white-glove model where embedded engineering carries much of the build, Decagon is designed around exactly that and will feel less like a project you have to staff internally. And if your priority is a premium, heavily supported rollout with a vendor whose enterprise procurement story is well established, that is squarely Decagon's strength. The honest summary is that Decagon wins on enterprise scale, production maturity, and managed-deployment depth, and for large healthtechs with the budget and the preference for high-touch service, those are the right criteria.
Feature-by-Feature Summary for Healthtech
HIPAA and BAA posture: Lorikeet is SOC 2 and BAA-ready for HIPAA with PII/PHI redaction, RBAC, and US/UK/AU data residency; Decagon is SOC 2 and serves enterprise buyers, with BAA and HIPAA specifics to confirm directly. Both should be diligenced under NDA.
PHI handling: Lorikeet makes redaction, least-privilege tools, and disclosure rules configurable and testable; Decagon provides enterprise-grade controls whose depth for a specific PHI workflow is worth a technical deep-dive.
Audit trails: Lorikeet provides a replayable tool-call-plus-reasoning record plus 100% automated QA via Coach; ask both vendors to replay a real ticket end to end during evaluation.
Multi-step workflows: Lorikeet combines deterministic and natural-language workflows in one interaction and dispatches sub-agents; Decagon handles complex flows backed by embedded engineering.
Channels: Decagon covers chat, email, and voice; Lorikeet adds SMS, WhatsApp, and outbound re-engagement, with voice on the same engine as chat and email at sub-1-second latency.
Pricing: Decagon is unpublished, with a median near $400,000 per year; Lorikeet prices per resolution (~$0.80–$0.95 chat/email/SMS, ~$1.20–$1.50 voice) with escalations not charged.
Deployment: Decagon is white-glove with embedded engineering; Lorikeet is plain-English configuration with forward-deployed help, designed for your team to own the workflows after launch.
How to Choose Between Decagon and Lorikeet for Healthtech
The decision comes down to scale, control, and what your hardest PHI-touching tickets look like.
Choose Decagon if you are a large healthtech with the budget for a premium, white-glove deployment, you value a long enterprise production track record, and you prefer the vendor's embedded engineering to carry much of the build. Decagon's scale, maturity, and managed-deployment depth are real, and for that class of buyer they are exactly what the job needs.
Choose Lorikeet if your important healthtech tickets are multi-step and PHI-sensitive, your privacy and compliance team needs to approve the agent's behavior before launch, and you want to own and adjust the workflows yourself afterward. Lorikeet leads on resolution depth, defence-in-depth guardrails, deterministic plus natural-language workflows, single-engine omnichannel including sub-1-second voice, simulation-based validation, audit trails, and 100% automated QA, with a BAA-ready HIPAA posture. The cost of that depth is a more involved implementation, which is why Lorikeet provides forward-deployed help. Whichever way you lean, confirm a signed BAA and current SOC 2 scope with the vendor before any real PHI flows through the system.
If your hardest healthtech tickets are PHI-sensitive and multi-step, book a Lorikeet demo and bring your toughest 10 tickets. We will run them in your stack against your guardrails before you sign.
Key Takeaways
For healthtech, the choice between Decagon and Lorikeet is not about a single resolution number; it is about PHI handling, BAA and HIPAA posture, audit trails, and multi-step workflows your privacy team can approve before launch.
Decagon's strength is enterprise scale, production maturity, and white-glove implementation with embedded engineering; it is the right fit for large healthtechs with the budget and a preference for a managed rollout.
Lorikeet's strength is regulated workflow depth: deterministic plus natural-language workflows in one interaction, defence-in-depth guardrails, PII/PHI redaction, replayable audit trails, and 100% automated QA, with a BAA-ready HIPAA posture.
Pricing differs in kind: Decagon is unpublished with a median near $400,000 per year and embedded engineering; Lorikeet prices per resolution (~$0.80–$0.95 chat/email/SMS, ~$1.20–$1.50 voice) with escalations not charged and a customer-defined resolution.
Whichever you pick, confirm a signed BAA, current SOC 2 scope, data residency, and model-provider data terms directly under NDA. These features support your HIPAA obligations rather than certifying compliance on their own.
