In regulated customer support, the platform that wins is not the one with the highest deflection rate. It is the one your compliance team will sign off on before launch and your auditor can replay after.
Sierra, Decagon, and Lorikeet are three of the most serious agentic AI customer support platforms a regulated buyer will shortlist in 2026. All three resolve real tickets end-to-end across multiple channels. All three are past the FAQ-chatbot stage. The differences that matter for a fintech, a lender, a healthtech, or an insurer are not in the demo. They show up in how each platform handles guardrails, audit trails, deterministic workflows, voice, pricing, and the deployment model your team has to live with.
This is a head-to-head, three-way comparison written to be fair to all three. Sierra and Decagon are strong platforms with real customers and real strengths. Lorikeet was built specifically for complex and regulated businesses, so on regulated depth it leads. Where a competitor is the better fit, this guide says so. Lorikeet builds and maintains this comparison, so weigh it accordingly and verify the claims against each vendor directly.
Sierra is the enterprise generalist with outcome-only pricing and strong brand pull. Decagon is the high-touch enterprise platform with embedded engineering and broad consumer deployments. Lorikeet is the regulated specialist with defence-in-depth guardrails, deterministic plus natural-language workflows, sub-1-second voice, and 100% automated QA.
For regulated buyers, the dominant evaluation criteria are now guardrail provability before go-live, replayable audit trails, and behavior you can test in simulation rather than discover in production.
Gartner predicts 80% of common customer service issues will be resolved autonomously by 2029, up from low double digits in 2024, which raises the stakes on getting the regulated tickets right, not just the easy ones.
Pricing models differ sharply: Sierra bills on outcomes only, Decagon negotiates per-conversation or per-resolution at enterprise contract sizes, and Lorikeet prices per resolution at roughly $0.80 for chat, email, and SMS and roughly $1.00 for voice, with escalations not charged.
Last updated: June 2026
Regulated support has a different failure mode than e-commerce or SaaS. A customer asking why their account is frozen is not a churn-risk ticket, it is a regulator-attention ticket. A wrong answer on a dispute, a KYC unlock, or a claim is not a refund, it is a complaint to the CFPB, a notice from AUSTRAC, or a HIPAA exposure. That changes what you should evaluate. Resolution rate alone is a vanity metric in a regulated business, because you can hit a high number by handling easy tickets and quietly mishandling the one that matters. The comparison below is organized around the lenses that actually decide a regulated procurement: guardrails, audit, workflows, voice, pricing, and deployment.
At-a-Glance Comparison
Sierra · Best for: Large enterprises that want outcome-only billing and a recognized brand · Key strength: Pure pay-on-resolution pricing and enterprise procurement story · Channels: Chat, email, voice · Pricing: Outcome-based, custom (reported enterprise contracts in the tens to low hundreds of thousands per year)
Decagon · Best for: Large consumer and enterprise teams that want a high-touch, white-glove deployment · Key strength: Embedded engineering and large-scale consumer deployments · Channels: Chat, email, voice · Pricing: Per-conversation or per-resolution, custom (reported median contract value near six figures)
Lorikeet · Best for: Fintechs, lenders, healthtechs, and insurers whose toughest stakeholder is compliance · Key strength: Defence-in-depth guardrails, deterministic plus natural-language workflows, sub-1-second voice, and 100% automated QA · Channels: Chat, email, voice, SMS, WhatsApp, plus outbound re-engagement · Pricing: Per resolution, roughly $0.80 chat/email/SMS and roughly $1.00 voice, escalations not charged
What These Three Platforms Have in Common
Before the differences, the shared baseline. Sierra, Decagon, and Lorikeet all build agentic AI that resolves tickets end-to-end rather than deflecting them to an article. All three take actions through integrations, not just answer questions. All three operate across more than one channel, hold SOC 2 at minimum, and have moved past the rules-based chatbot generation. If your requirement is simply "an AI that can resolve a meaningful share of inbound volume," any of the three can clear that bar in their target segment.
The reason to read further is that regulated support raises the bar past resolution. The question stops being "can it resolve" and becomes "can you prove what it did, control what it is allowed to do, and approve that behavior before a customer ever sees it." That is where the three diverge.
Guardrails and Safety
Guardrails are the controls that stop an AI agent from doing the wrong thing: leaking PII, skipping a required disclosure, acting above a dollar threshold, or answering a question it should escalate. For a regulated buyer this is the first lens, not the last, because a compliance team will not approve a system whose safety story is "trust us, it usually works."
Sierra. Sierra builds guardrails and supervisory controls into its agents and emphasizes safe, on-brand behavior as part of its enterprise pitch. Its model is strong for general enterprise use. The honest read for a regulated buyer is that Sierra's public emphasis is on outcome quality and brand voice, and you should ask specifically how guardrail behavior is tested and proven before go-live rather than assuming it is.
Decagon. Decagon offers supervisory controls and works with large brands that have meaningful compliance needs, and its embedded engineering team configures those controls during deployment. The strength is that a capable team sets this up for you. The trade-off is that the depth of the guardrail layer is partly a function of how much that team builds for your specific case, rather than a self-serve framework you can fully inspect and extend on your own.
Lorikeet. Lorikeet's central design idea is defence in depth, built for regulated workflows. There are four layers: pre-launch adversarial simulations and red-teaming that test the bad paths before you ship, inbound message checks on what comes in, outbound guardrails on what the agent says and does, and 100% post-facto QA on everything that happened. The framing the team uses is that the large language model is the engine and Lorikeet is the cockpit. The practical difference for a regulated buyer is that you can run the guardrail tests and read the pass and fail results before launch, which is what lets a compliance team sign off on behavior rather than on faith. These controls support your compliance obligations, they do not by themselves guarantee or certify regulatory outcomes, and you still own the policy decisions behind them.
Verdict on guardrails. All three have guardrails. Sierra and Decagon treat them as a strong runtime and deployment feature. Lorikeet treats provable, pre-go-live, multi-layer safety as the core of the product, which is why it leads this lens for regulated buyers specifically.
Audit Trails and Explainability
An audit trail is a timestamped, replayable record of every tool call, prompt, and reasoning step the AI took on a ticket. In a regulated examination, a transcript of what the customer saw is not enough. You need to point at the exact reasoning step where a decision was made and show the action that followed it.
Sierra. Sierra provides reporting and analytics on agent performance and outcomes suitable for enterprise oversight. For a regulated buyer the question to push on is whether you can replay a full reasoning-plus-tool-call chain for an individual ticket from weeks ago, not just see aggregate metrics. Ask for that demonstration explicitly.
Decagon. Decagon gives enterprise teams dashboards and visibility into agent behavior, and its scale with large consumer brands means its analytics are mature. The same regulated-grade question applies: confirm the depth of per-ticket, step-level replay and how long that detail is retained, because aggregate analytics and forensic replay are different artifacts.
Lorikeet. Lorikeet pairs the agent with Coach, a second agent that performs analytics and 100% automated QA. Coach does root-cause analysis, assigns a ticket quality score, and verifies resolution, in effect an AI evaluating the AI on every interaction rather than a sampled few. Coach is deployable standalone at roughly $0.10 per ticket, so a team can use it to grade an existing support operation before adopting the concierge. For a regulated buyer the relevant point is that step-level logging and verification are first-class, not an add-on report.
Verdict on audit. Sierra and Decagon offer enterprise-grade reporting that satisfies many oversight needs. Lorikeet's 100% automated QA through Coach plus step-level logging is the deeper fit when a regulator or an internal compliance team expects to replay individual decisions, so Lorikeet leads here for regulated use.
Workflows: Deterministic and Natural Language
How you express business logic determines how predictable the agent is. Pure natural-language instruction is flexible but harder to guarantee. Pure decision trees are predictable but brittle. Regulated workflows often need both: deterministic control on the steps that must happen exactly, and natural-language reasoning on the parts that benefit from judgment.
Sierra. Sierra lets enterprises encode procedures and guardrails for their agents and is effective at expressing brand-consistent, policy-aware behavior. Its approach centers on the agent reasoning over the procedures you define.
Decagon. Decagon supports configurable flows and standard operating procedures, often set up with help from its embedded team. This works well when you want experts to build the logic with you, and it is worth confirming how much of that you can own and edit afterward without going back through the vendor.
Lorikeet. Lorikeet combines deterministic Structured Workflows with natural-language workflows, and the two can be used together inside a single interaction. The deterministic path gives you exact control where a regulated step must run a specific way, and the natural-language path handles the reasoning around it. All configuration is in plain English. For a regulated KYC unlock, a dispute, or a claim, this combination means the must-happen steps are guaranteed while the conversation stays natural.
Verdict on workflows. All three can express real business logic. Decagon leans on embedded setup, Sierra on agent-reasoned procedures, and Lorikeet on a combinable deterministic-plus-natural-language model that suits regulated steps needing exact control. Teams that want to own and edit deterministic logic themselves will find Lorikeet the strongest fit.
Voice and Channels
Regulated support is rarely chat only. A card lock comes by phone, a wire confirmation by email, a dispute on chat, a reminder by SMS. The agent should be the same agent across channels with shared context, or customers repeat themselves and the experience degrades.
Sierra. Sierra supports chat, email, and voice for enterprise deployments and presents these as part of one platform.
Decagon. Decagon supports chat, email, and voice, with large-scale voice and chat deployments at consumer brands demonstrating it can handle volume.
Lorikeet. Lorikeet runs chat, email, voice, SMS, and WhatsApp, plus outbound re-engagement for use cases like collections and abandonment, with compliance handling for do-not-call, call-hour rules, and consent. Its voice agent targets sub-1-second latency with natural conversation and automatic language switching, on the same workflow engine as the other channels. The practical effect is that voice is not a separate stack bolted on with a transcript handoff, it is the same agent that can take actions on the call.
Verdict on voice and channels. Sierra and Decagon cover the core enterprise channels including voice. Lorikeet covers the widest set, adds outbound with compliance controls, and runs sub-1-second voice on the same engine as chat and email, which is the stronger fit for fintechs and healthtechs that need consistent behavior across every channel a regulated customer might use.
Pricing and Total Cost
Pricing model matters as much as price, because the model shapes which tickets a vendor is incentivized to handle well.
Sierra. Sierra bills on outcomes: customers pay only when the AI fully resolves a case, and escalations cost nothing. Reported enterprise contracts fall in the tens to low hundreds of thousands of dollars per year, with the rate per resolution negotiated. The alignment is appealing. The honest caution for regulated buyers is that a vendor paid only on full resolution has a structural incentive toward the tickets that resolve easily, and in regulated support the hard tickets are the ones that matter most.
Decagon. Decagon negotiates per-conversation or per-resolution pricing at enterprise contract sizes, with reported median contract value in the low six figures and embedded engineering bundled into the deployment. You are paying for a high-touch model. The question to ask is how much of that cost reflects ongoing configuration you cannot do yourself.
Lorikeet. Lorikeet prices per resolution at roughly $0.80 for chat, email, and SMS and roughly $1.00 for voice, with Coach at roughly $0.10 per ticket. Escalations are not charged, and the customer holds the veto on what counts as a resolution. The Scale plan is 48,000 resolutions for $48,000 per year as a reference point. Against a human baseline of roughly $1.25 to $4.00 per handled ticket, the per-resolution math is favorable, and the customer-defined-resolution term is a direct answer to the "deflection pricing" concern.
Verdict on pricing. Sierra's outcome-only model is the cleanest alignment story but carries a selection-bias risk on hard tickets. Decagon's enterprise model bundles a team you may or may not need long term. Lorikeet's published per-resolution pricing with a customer-held resolution veto is the most transparent for a regulated buyer who wants to model cost on the tickets that matter and not be charged for escalations.
Deployment and Time to Value
Sierra. Sierra runs high-touch implementations with its own staff embedded during launch, which suits large enterprises that want a managed rollout.
Decagon. Decagon's deployment is white-glove by design, with embedded engineering through the launch period. This is a strength for teams that want experts to build with them and a consideration for teams that want to own the configuration sooner.
Lorikeet. Lorikeet pairs each customer with a forward-deployed product manager and engineer, gets a sandbox running in roughly 20 to 30 minutes, and is typically operational in about a month. The design intent is that your team can own and edit the workflows after launch rather than routing every change through the vendor.
Verdict on deployment. All three offer hands-on launch support. The distinction is ownership afterward: Lorikeet is built so your team holds the keys post-launch, while Decagon and Sierra lean more on the vendor team staying in the loop.
How to Choose Between Sierra, Decagon, and Lorikeet
Choose Sierra if you are a large enterprise that values outcome-only billing and a recognized brand in procurement, your tickets are broad rather than deeply regulated, and you are comfortable with the incentive trade-off of paying only on full resolution.
Choose Decagon if you run a high-volume consumer or enterprise operation, you want a white-glove deployment with an embedded engineering team building alongside you, and a low-six-figure annual commitment fits your budget.
Choose Lorikeet if you are a fintech, lender, healthtech, or insurer whose toughest stakeholder is compliance, you need provable guardrails before go-live, replayable audit trails through 100% automated QA, deterministic plus natural-language workflows you can own, sub-1-second voice across the widest channel set, and transparent per-resolution pricing where you define what a resolution is.
An honest limitation: Lorikeet is purpose-built for complex and regulated industries, with roughly 80% of its customers being US financial institutions and fintechs. If your support is high-volume but low-complexity and not regulated, a generalist platform may get you to value faster and the regulated depth is capability you will not fully use. The strength that makes Lorikeet the right answer for a regulated buyer is the same focus that makes it overbuilt for a simple FAQ deflection use case.
If your hardest tickets are the regulated ones and your compliance team is the stakeholder you have to win, book a Lorikeet demo and bring your hardest tickets to run against your guardrails before you sign.
