Sierra, Decagon, and Lorikeet are three of the most credible AI customer support agent platforms a regulated team will shortlist in 2026. The question that decides between them is not which is best in the abstract, but which one fits the way your team buys, deploys, proves behavior to compliance, and gets audited.

This is a decision guide, not a ranking. Teams evaluating AI customer service for fintech, healthtech, insurance, or gaming routinely put all three of these vendors on the same shortlist, and for good reason: each is a serious agentic platform with real production deployments. They overlap on core capability and differ most on pricing philosophy, deployment model, how each was built, and how deep the regulated-support features run. Below we walk through five evaluation criteria that decide regulated purchases - guardrails, audit, workflows, pricing model, and deployment - describe how Sierra, Decagon, and Lorikeet each fit each one, and then give a recommendation by scenario and buyer profile so you can match the right platform to your situation.

• All three ship multi-step agentic resolution and serve enterprise buyers. Sierra and Decagon are broad enterprise platforms; Lorikeet is purpose-built for complex and regulated industries.

• The clearest pricing difference: Sierra is known for outcome-based billing (you pay on full resolution), Decagon offers selectable per-conversation or per-resolution pricing plus a platform fee, and Lorikeet prices per resolution with the customer defining what counts as a resolution.

• Sierra and Decagon both lean on high-touch, embedded deployment. The regulated-buyer question is who owns and can change the workflows and guardrails after launch.

• In regulated support the deciding criterion is usually whether the agent's behavior is provable before go-live and replayable after, not the headline resolution rate.

• The right answer depends on your situation: outcome-aligned billing, top-of-market scale, or depth on the hardest regulated tickets with provable guardrails each point to a different vendor.

Last updated: June 2026

Most comparison pages try to declare a winner. That framing fails for AI customer support, because the right answer depends on what you are buying for. A high-volume consumer brand that wants billing aligned to outcomes has a different ideal than a fintech whose compliance lead holds veto power over launch. This guide is structured so you can match the criterion that matters most to your business against how Sierra, Decagon, and Lorikeet each behave, then decide. We try to be fair to all three and name the points where each can fall short.

Sierra, Decagon, and Lorikeet at a glance

All three emerged in the current wave of agentic AI support and target buyers who need the agent to take actions, not just answer questions. They are genuine peers on core capability.

Sierra was founded by Bret Taylor and Clay Bavor and scaled quickly, reaching $100M ARR in 21 months and $150M+ ARR by early 2026 per TechCrunch. Its signature is outcome-based pricing: customers pay when the AI fully resolves a case, and escalations to humans cost nothing. It ships voice, chat, and email and leans on a branded persona approach to deployment with embedded Sierra staff.

Decagon is the high-end enterprise AI agent platform with named fintech and consumer customers and a white-glove implementation model. It offers per-conversation or per-resolution pricing that the customer can select, runs voice, chat, and email, and is backed by significant venture funding. Industry data suggests a median total contract value near $400,000 per year, comprising an annual platform fee plus per-interaction fees.

Lorikeet is an AI customer support platform built specifically for complex and regulated businesses - fintech, financial services, healthcare and healthtech, insurance, and gaming. Roughly 80% of its customers are US financial institutions and fintechs. It builds AI concierges that resolve issues end-to-end across chat, email, voice, SMS, and WhatsApp on one engine, and pairs the customer-facing Concierge with a Coach agent that runs 100% automated quality assurance. Pricing is per resolution, with the customer defining what counts as a resolution.

The honest summary: on raw capability the three are close. The differences that decide a purchase show up in pricing structure, who controls the workflows and guardrails after launch, and how deep the features specific to regulated industries run.

Side-by-side

A quick orientation before the criteria-by-criteria walkthrough. Treat published figures as directional, since Sierra and Decagon do not publish rates and contract values vary by company size and ticket volume.

Pricing model · Sierra: outcome-based, pay only on full resolution, escalations free · Decagon: per-conversation or per-resolution, customer-selectable, plus an annual platform fee · Lorikeet: per resolution (~$0.80 chat/email/SMS, ~$1.00 voice), customer defines resolution, escalations not charged

Reported contract value · Sierra: reportedly $50,000 to $200,000/year · Decagon: median near $400,000/year · Lorikeet: usage-based; representative Scale plan 48,000 resolutions for $48,000/year

Channels · Sierra: voice, chat, email · Decagon: voice, chat, email · Lorikeet: chat, email, voice (sub-1-second latency), SMS, WhatsApp, plus outbound re-engagement

Deployment · Sierra: high-touch, branded persona, embedded staff · Decagon: white-glove, embedded engineering · Lorikeet: forward-deployed PM and engineer; plain-English config; sandbox in 20-30 minutes, operational in about a month

Industry focus · Sierra: broad enterprise incl. financial services · Decagon: broad enterprise incl. fintech and consumer · Lorikeet: purpose-built for regulated industries (fintech, healthtech, insurance, gaming)

Signature strength · Sierra: incentive-aligned outcome billing · Decagon: proven scale, selectable pricing · Lorikeet: regulated-grade guardrails, audit, and 100% QA

For a regulated buyer, the rows that do not appear in most vendor decks are the ones that matter most: how deep is the audit trail, and can your compliance team prove the guardrails before launch. That is where the criteria below focus.

The five criteria that decide regulated support

Generic CX buying guides start with deflection rate, response time, and CSAT. In a regulated business those are downstream of correctness and provability. The five lenses below are the ones a fintech, healthtech, insurer, or gaming operator should weigh, and we describe how Sierra, Decagon, and Lorikeet each fit each one.

1. Guardrails

In regulated support, a guardrail is anything that stops the agent from doing the wrong thing: scripted disclosures, dollar-threshold blocks, jurisdiction-specific responses, PII handling rules, and escalation triggers. The test is not whether guardrails exist at runtime - almost every serious vendor has them - but whether you can define them in your own policy language and prove they hold before launch.

How Sierra fits: Sierra deploys with embedded staff and a persona-led configuration, and its guardrail story is tied to that high-touch model. The question for a regulated team is how much of the guardrail surface your team owns versus the vendor's, and whether you can run the bad paths yourself before go-live.

How Decagon fits: Decagon's white-glove model means its team typically helps configure guardrails as part of deployment, which is a benefit for a buyer with limited internal engineering. The consideration for a regulated team is whether your compliance lead can read, change, and re-test those guardrails independently, rather than filing a request with the vendor.

How Lorikeet fits: Lorikeet's approach is defence in depth, built so guardrails are provable before launch rather than reviewed after. Pre-launch adversarial simulations and red-teaming test the bad paths, inbound message checks screen requests, outbound guardrails constrain actions, and the Coach agent runs 100% post-facto QA. Guardrails are configured in plain English and your team can re-test them. The honest framing: these features are designed to support your compliance obligations, not to certify them on your behalf.

What to ask any of the three: can my compliance team write a guardrail in plain language, run it against a test suite, and read the pass and fail report before we launch? If the answer routes through the vendor's services team, factor that into your change-management timeline.

2. Audit

An audit trail in regulated support is a timestamped, replayable record of every tool call, prompt, and reasoning step the AI made on a given ticket. It is the artifact your compliance team uses during a regulator examination, and the single most important fintech and healthtech-specific capability. A transcript is not an audit trail.

How Sierra fits: Sierra's enterprise posture includes the logging large customers expect. The depth question applies: ask to see an end-to-end audit trail for a real decision the AI made last week, with every tool call and the reasoning between them, not a sampled summary.

How Decagon fits: Decagon operates production deployments at scale and maintains logging suitable for enterprise governance. Confirm the depth: can you replay the full reasoning chain plus tool calls for any ticket from 90 days ago, in order.

How Lorikeet fits: Audit and QA are continuous rather than sampled. The Coach agent runs automated quality assurance on 100% of tickets with root-cause analysis, a ticket quality score, and resolution verification - the AI evaluating the AI - and is also deployable standalone at roughly $0.10 per ticket. The intent is replayability at the reasoning-step level so that when a KYC unlock or claim decision goes wrong, you can point at the exact step that failed.

For all three, the standard a regulated buyer should hold is replayability at the reasoning-step level, not a conversation export.

3. Workflows

Most regulated tickets are not single questions. They are sequences: verify identity, check why a transfer failed, refund a fee, update an address, and escalate if a threshold is crossed. The platform has to chain several tool calls in the right order, keep state, and recover when one tool errors mid-chain.

How Sierra fits: Sierra supports agentic, multi-step resolution and integrates with enterprise systems. Its configuration is persona-led and high-touch, so much of the initial workflow build happens with Sierra's team.

How Decagon fits: Decagon is built for multi-step resolution and supports action-taking across integrated systems, which is why it shows up in enterprise fintech shortlists. The deployment model means much of the initial workflow build is done with Decagon's team.

How Lorikeet fits: Lorikeet combines natural-language workflows with deterministic structured workflows in a single interaction, all configurable in plain English. Its Team of Agents pattern dispatches sub-agents to call third parties, send email, and coordinate - for example, contacting a merchant on a dispute or a pharmacy on a prescription question. Because config is plain English, your team can author and edit workflows after launch without a vendor services engagement for every change.

What to ask any of the three: what happens when a core system returns a 5xx error mid-chain - retry, escalate, or roll back? And after launch, can our team author and edit workflows without a vendor services ticket for every change? The answer to the second question shapes how fast you can respond to a new regulation or product change.

4. Pricing model

This is where the three differ most structurally, and the right choice depends on how hard your tickets are and how often they fully resolve.

How Sierra fits: Sierra is the standard-bearer for outcome-based pricing - you pay only when the AI fully resolves a case, and escalations cost nothing. The appeal is incentive alignment and budget predictability. Enterprise contracts are reportedly in the $50,000 to $200,000 per year range, with the per-resolution rate negotiated case by case. The consideration for regulated buyers: any model that pays only on full resolution can create a quiet bias toward easy tickets, and in regulated support the hard tickets (disputes, KYC, claims) are the ones that matter most.

How Decagon fits: Decagon offers per-conversation or per-resolution pricing that the customer selects, plus an annual platform fee. Industry data suggests a median total contract value near $400,000 per year, which places it at the premium end. The per-conversation option can be attractive when you want to pay for engagement regardless of outcome, but it also means you pay on conversations that do not resolve.

How Lorikeet fits: Lorikeet prices per resolution at roughly $0.80 per chat, email, or SMS resolution and roughly $1.00 per voice resolution, Coach at roughly $0.10 per ticket, with escalations not charged and the customer defining what counts as a resolution. A representative Scale plan is 48,000 resolutions for $48,000 per year. 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, while keeping the customer in control of the resolution definition.

Model your own ticket mix against each structure. Outcome-only pricing rewards a vendor differently than per-conversation pricing, and a customer-defined per-resolution model behaves differently again. Pay particular attention to cost on the hard 20% of tickets that do not fully resolve.

5. Deployment

Sierra and Decagon both use a high-touch, embedded deployment model with vendor staff involved during launch. For an enterprise without a large internal AI team, that hand-holding is genuinely useful and shortens time to first production tickets. Lorikeet also deploys with a forward-deployed PM and engineer, but pairs that with plain-English configuration intended to leave the customer self-sufficient: a sandbox stands up in 20 to 30 minutes and an account is typically operational in about a month.

The regulated-buyer consideration: embedded engineering is sold as a feature, and for many teams it is. The honest read is that it can also reflect how hard the platform is to configure alone. The question to settle before signing is ownership: after the launch period ends, can your team independently change workflows, guardrails, and integrations, or does every material change require the vendor's services team? In a regulated business where rules change and you need to move quickly, post-launch self-sufficiency is worth more than launch-period speed.

Recommendation by scenario and buyer profile

With the five criteria in hand, here is how to map your situation to a vendor. None of these is a disqualifier for the others; they describe the conditions under which each is the most natural fit.

Choose Sierra when outcome-aligned billing is the dominant criterion

Sierra tends to fit enterprises that want billing aligned to outcomes and predictability in the support line item, and that find a branded persona deployment appealing. If your priority is paying only for fully resolved cases, you have the procurement appetite for a $50,000 to $200,000 annual spend, and your ticket mix skews toward cases that fully resolve, Sierra is a credible pick. It is strongest where outcome alignment is the deciding buying criterion and the ticket base is not heavily weighted toward the hardest regulated cases.

Choose Decagon when you want top-of-market scale and selectable pricing

Decagon tends to fit large fintech and consumer enterprises with substantial support budgets that want a top-of-market platform and are comfortable with a premium total contract value and a white-glove deployment. If you value being able to choose between per-conversation and per-resolution pricing, want a vendor with proven scale across millions of interactions, and have the budget for a roughly six-figure annual commitment, Decagon belongs on the shortlist. It is a strong choice when breadth, scale, and a selectable pricing model matter more than owning every configuration yourself.

Choose Lorikeet when regulated depth and provable behavior decide it

Lorikeet tends to fit teams whose toughest stakeholder is the compliance lead and whose hardest tickets are the regulated ones - KYC unlocks, disputes, transfers, claims. Consider it when these conditions hold:

• Guardrails are your gating stakeholder. Defence in depth - pre-launch adversarial simulations and red-teaming, inbound message checks, outbound guardrails, and 100% post-facto QA - is designed so your compliance team can sign off before launch rather than review after. Compliance features support your obligations, not certify them on your behalf.

• Audit and QA need to be continuous, not sampled. The Coach agent runs automated QA on 100% of tickets with root-cause analysis and resolution verification, and is also deployable standalone at roughly $0.10 per ticket.

• Your team needs to own the workflows. Natural-language plus deterministic structured workflows, all configurable in plain English, so your team can change behavior after launch without a vendor services ticket for every edit.

• Channels include voice with low latency. Chat, email, voice (sub-1-second latency), SMS, and WhatsApp on one engine, 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. Roughly $0.80 per chat, email, or SMS resolution and $1.00 per voice resolution, the customer defines what counts as a resolution, and escalations are not charged.

Lorikeet holds SOC 2, is BAA-ready for HIPAA, is GDPR-aligned, supports data residency in the US, AU, and UK, and maintains contractual no-train agreements with its model providers. It is the narrower, deeper choice: purpose-built for regulated industries rather than general-purpose enterprise CX, so a buyer whose needs are broad consumer support may find Sierra or Decagon a more natural fit.

Where each can fall short

A fair decision guide names the friction, not just the fit. None of these are disqualifiers; they are the trade-offs to price in.

Sierra. Outcome-only pricing is elegant, but it carries a structural incentive that matters in regulated support: a vendor paid only on full resolution is rewarded for the tickets that resolve cleanly. In fintech, healthtech, and insurance the cases that decide your risk are the messy ones, so confirm how Sierra handles partial resolutions and the hard 20% that escalate. The branded-persona deployment is also more consumer-brand oriented than compliance-first.

Decagon. The premium total contract value puts it out of reach for smaller teams, and the white-glove model can mean your team is dependent on the vendor for configuration changes well past launch. If post-launch self-service is a hard requirement, confirm exactly how much you can change without a services engagement.

Lorikeet. It is deliberately specialized for regulated industries, so its breadth across general consumer CX is narrower than the two generalists. It is also a younger company than the largest incumbents in the broader category, which some procurement teams weigh. Its depth on regulated workflows, audit, and QA is the reason it earns a place on a regulated 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 all three vendors. Bring your hardest tickets - a KYC unlock that failed, a disputed transaction, a claim that needs a threshold check - and ask each vendor to run them in your stack against your guardrails. Then compare on the five criteria in order of what matters most to your business.

• Ask for an end-to-end audit trail of a real decision the AI made last week, with every tool call and the reasoning between them.

• Ask whether your compliance team can write and re-test a guardrail before go-live and read the pass and fail report.

• Ask what happens when a core 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.

Sierra, Decagon, and Lorikeet are all strong, and for many teams any of the three is the right answer. For a regulated buyer whose toughest stakeholder is the compliance lead, the same questions that separate the three are the ones that point toward depth on guardrails, audit, and the hard tickets. If that is your situation, it is worth running Lorikeet alongside Sierra and Decagon and deciding on the cases that matter.