In a regulated business, the platform that wins a demo and the platform that survives a compliance review are rarely the same. Decagon and Sierra both demo well. The harder question is what your risk team signs before launch.
Decagon and Sierra are two of the most prominent enterprise AI customer service platforms in 2026. Both resolve tickets autonomously across chat, email, and voice, and both sell to large companies including financial services brands. For a regulated buyer (fintech, banking, insurance, healthcare), the deciding factor is not the demo. It is whether the vendor can prove its behavior before go-live and produce the audit trail an examiner will ask for after.
Decagon leads on configurable, multi-step resolution and white-glove deployment, with a median annual contract reported near $400,000 and embedded engineering during launch.
Sierra leads on outcome-based pricing (you pay only on full resolution) and reached $100M ARR in 21 months, with reported enterprise contracts of $50,000-$200,000/year.
Neither was purpose-built for regulated workflows, which is where a third option, Lorikeet, fits: defence-in-depth guardrails, simulation-based pre-launch testing, and replayable audit trails for the tickets that carry regulatory weight.
For regulated buyers, the evaluation criteria that matter are auditability, provable guardrails before launch, and correctness on the hard 20% of tickets, not headline deflection rate.
Last updated: June 2026
This comparison is written for a regulated buyer deciding between Decagon and Sierra, and weighing where each falls short for compliance-heavy work. It is fair to both: Decagon and Sierra are strong platforms with real enterprise deployments, and either can be the right answer for the right company. The goal here is to be honest about what each does well, where each leaves a gap for a regulated team, and how Lorikeet positions itself as the regulated-fit alternative on the dimensions that decide a compliance sign-off.
The reason regulated CX is a category of its own comes down to who the toughest stakeholder is. In a typical software business, the buyer optimizes for cost per ticket and customer satisfaction, and the worst case for a wrong answer is a refund or a churned account. In a regulated business, the buyer optimizes for correctness on the tickets a regulator can examine, and the worst case for a wrong answer is a consumer-protection complaint, a state insurance commissioner notice, or a financial-conduct finding. That difference reorders the entire evaluation. A platform that resolves 90% of tickets but cannot reconstruct what it did on the other 10% is a liability, not an asset, to a fintech or a carrier. So the comparison below leads with auditability and provable behavior, and treats deflection rate as the downstream metric it actually is.
Decagon vs Sierra: At a Glance
Decagon · Best for: Enterprises wanting a highly configurable agent with embedded engineering support · Pricing model: Platform fee plus per-conversation or per-resolution, customer-selectable; median total near $400K/year · Channels: Chat, email, voice · Deployment: White-glove, embedded engineers during launch
Sierra · Best for: Enterprises wanting incentive-aligned outcome billing · Pricing model: Outcome-based (pay only on full resolution); reported $50K-$200K/year · Channels: Chat, email, voice · Deployment: High-touch, embedded Sierra staff
Lorikeet (regulated-fit alternative) · Best for: Fintech, healthtech, insurance, and gaming teams whose compliance lead is the toughest stakeholder · Pricing model: Per-resolution usage (~$0.80 chat/email/SMS, ~$1.00 voice; escalations not charged) · Channels: Chat, email, voice (sub-1-second latency), SMS, WhatsApp, plus outbound · Deployment: Forward-deployed PM plus engineer; operational in about a month
What Decagon Does Well
Decagon is a high-end enterprise AI agent platform with named customers across consumer and financial services. Its strength is configurability paired with hands-on deployment: the platform can be shaped to complex resolution flows, and Decagon staffs embedded engineering during the launch period so the agent ships configured to a specific business rather than out of the box.
Key strengths
Flexible pricing: per-conversation or per-resolution, selectable by the customer.
Voice, chat, and email in one platform, with production deployments processing large interaction volumes.
White-glove implementation with embedded engineers during launch, which shortens time to a working configuration for teams without internal AI expertise.
Strong enterprise procurement footing, backed by significant venture funding and a fast-growing customer base.
The honest trade-off for a regulated buyer
Embedded engineering is sold as a feature. The fair read is that it is also a signal: a platform that needs embedded engineers to configure is a platform your own team may struggle to own and change after launch. In a regulated business, where workflows shift with new rules and your compliance team needs to adjust guardrails without filing a vendor ticket, that ownership gap matters. The reported median near $400,000/year also puts Decagon at the top of the market, which narrows the buyer set to enterprises with large support budgets.
What Sierra Does Well
Sierra is the enterprise AI agent company founded by Bret Taylor and Clay Bavor. It scaled quickly and its hallmark is pure outcome-based pricing: customers pay only when the AI fully resolves a case, and escalations to a human cost nothing. For a buyer who wants billing tied directly to value delivered, that model is genuinely attractive, and Sierra's enterprise procurement story is among the strongest in the category.
Key strengths
Outcome-only pricing: you pay on full resolution, not on attempts or escalations.
Voice, chat, and email channels with a branded agent approach to deployment.
High-touch implementation with embedded Sierra staff during launch.
Strong CFO-level procurement narrative, which eases enterprise buy-in.
The honest trade-off for a regulated buyer
Outcome pricing aligns incentives, but it also creates a quiet selection bias. A vendor paid only on full resolution is rewarded for handling the easy tickets and escalating the hard ones. In a regulated business the hard tickets (a KYC unlock, a disputed transaction, a claims decision, a fraud hold) are exactly the ones that matter, because those are the tickets a regulator examines. A pricing model that gently steers the agent toward easy volume is working against the regulated buyer's actual need. This is not a flaw in Sierra's product so much as a property of the billing model, and it is worth weighing against the appeal of paying only for wins.
A Regulated Ticket Both Platforms Have to Handle
Consider a single concrete ticket to make the comparison real. A customer messages on a Friday evening: a transfer they expected to clear has not arrived, and they want to know why and want the failed-transfer fee refunded. In a regulated fintech this is not one action. The agent has to verify the customer's identity, look up the transfer status in the payments system, determine why it failed (insufficient funds, a risk hold, a downstream bank error), apply the correct disclosure language for the jurisdiction the customer sits in, decide whether the fee qualifies for a refund under policy, execute the refund if it does, log the decision, and escalate cleanly if any guardrail blocks it. Five to seven tool calls, in order, with state preserved across them, and a record at the end that an examiner could read.
Decagon and Sierra can both be configured to attempt this chain. The questions that decide the comparison for a regulated buyer are downstream of that: when the payments system returns a server error mid-chain, does the agent retry, escalate, or roll back, and is that behavior provable before launch? When the refund is declined by a dollar-threshold guardrail, can the compliance team see why in the log? And after the ticket closes, can someone replay the entire reasoning chain ninety days later during an examination? Those answers, not the demo, decide whether a platform is regulated-grade.
Decagon vs Sierra: Head to Head for Regulated CX
For a regulated team, four dimensions decide the comparison. Deflection rate is not one of them.
Auditability
Both platforms log interactions, and both can produce records for review. The regulated standard is higher than a transcript: a replayable record of every tool call, prompt, and reasoning step in order, with timestamps, for any ticket months later. Buyers evaluating either vendor should ask to replay the full reasoning chain for a specific ticket from 90 days ago and read the artifact a compliance team would hand an examiner. This is the single most important capability for regulated CX, and it is where buyers should push hardest in a demo.
Pricing transparency and incentive alignment
Decagon's configurable per-conversation or per-resolution model is flexible but lands at a high median total cost. Sierra's outcome-only model is transparent and aligns billing to wins, but carries the easy-ticket selection bias above. Neither is wrong; they suit different buyers. A regulated team should model cost on the hard 20% of tickets specifically, because that is where both the value and the risk concentrate.
Provable guardrails before go-live
A compliance team will not approve behavior described as "trust us, it usually works." The question for either vendor is whether you can run a guardrail test suite before launch, covering PII handling, scripted disclosures, dollar-threshold blocks, and jurisdiction-specific responses, and read a pass/fail report. Many platforms treat guardrails as a runtime feature rather than a pre-launch, testable contract. Ask each vendor directly.
Channel parity and team ownership
Both cover chat, email, and voice. The deeper questions are whether voice runs on the same workflow engine as chat (so a customer does not repeat themselves on a handoff) and whether your own team can own and change workflows after the embedded engineers leave. For regulated teams that re-tune guardrails as rules change, post-launch ownership is a recurring cost or saving depending on the vendor.
Where Lorikeet Fits: The Regulated-Fit Third Option
Lorikeet is an AI customer support platform built specifically for complex and regulated businesses: fintech, financial services, healthcare and healthtech, insurance, and sports betting and gaming. Around 80% of its customers are US financial institutions and fintechs. Where Decagon and Sierra are strong general-purpose enterprise platforms, Lorikeet is designed around the constraint that defines regulated CX: behavior has to be provable before launch and reconstructable after.
Deeper guardrails: defence in depth
Lorikeet's approach to safety is layered rather than a single runtime filter. Pre-launch adversarial simulations and red-teaming test the bad paths before you ship. Inbound message checks screen what reaches the agent. Outbound guardrails constrain what the agent says and does, including scripted disclosures and threshold blocks. Then 100% post-facto QA, run by a second agent called Coach, evaluates every resolved ticket. The framing the team uses is that the language model is the engine and the guardrail stack is the cockpit. For a compliance lead, the value is that the safety behavior is testable and reportable before a single customer ticket is touched.
Audit trails built for examination
Lorikeet's logs are designed for compliance approval pre-go-live and regulator examination after. Every tool call, prompt, and reasoning step is recorded and replayable, so when a KYC unlock or a dispute decision needs to be reconstructed, the team can point at the exact step where the agent reasoned and acted. This is the artifact regulated buyers should demand from any vendor, and it is the dimension Lorikeet leads on by design.
A team of agents for multi-party work
Regulated tickets often cannot be resolved inside one company's systems. A disputed transaction may require contacting the merchant. A pharmacy benefit question may require a call to the pharmacy. Lorikeet's Team of Agents dispatches sub-agents to handle these external steps, calling third parties, sending email, and coordinating the work so the customer-facing concierge can complete a resolution that depends on another party. For a regulated buyer, the value is that the coordination happens inside the same audited system rather than being handed off to a human and lost from the record.
Resolution that holds up, priced honestly
Lorikeet resolves multi-step tickets end-to-end across chat, email, voice (with sub-1-second latency), SMS, and WhatsApp, and supports outbound re-engagement with compliance controls such as do-not-call and call-hour rules. It combines natural-language workflows with deterministic structured workflows in a single interaction, all configured in plain English so your own team can own and change them. Pricing is per resolution: about $0.80 for chat, email, or SMS and about $1.00 for voice, with Coach standalone QA around $0.10 per ticket. The customer defines what counts as a resolution and escalations are not charged, which removes the incentive to chase easy tickets. The published Scale plan is 48,000 resolutions for $48,000/year. Against a human baseline of roughly $1.25 to $4 per handled ticket, the economics favor automation on volume while keeping the hard tickets correct.
An honest limitation
Lorikeet is deliberately focused on complex and regulated industries. If you are a high-volume, low-complexity consumer brand whose tickets are mostly simple FAQ deflection and who has no compliance or audit obligation, the depth of Lorikeet's guardrail and simulation tooling is more than you need, and a lighter or lower-touch tool may be a better fit. The regulated depth that is a strength for a fintech is overhead for a business that does not face an examiner.
How to Choose Between Decagon, Sierra, and Lorikeet
The right choice depends on what your toughest stakeholder cares about.
Choose Decagon if you are a large enterprise that wants a highly configurable agent and is comfortable with embedded engineering and a top-of-market budget, and your compliance needs are moderate rather than examiner-grade.
Choose Sierra if outcome-only billing is the deciding factor, your ticket mix skews toward resolvable volume, and you want a strong enterprise procurement story with CFO appeal.
Choose Lorikeet if you operate in a regulated industry, your hardest tickets carry regulatory weight, and your compliance team needs provable guardrails and replayable audit trails before go-live across chat, voice, email, SMS, and WhatsApp.
Whichever way you lean, take the same hard questions into every demo: replay an audit trail end-to-end, run the guardrail suite before launch, and price the hard 20% of tickets, not the easy 80%.
It is also worth weighing implementation reality, because it shapes total cost as much as the per-resolution rate. Both Decagon and Sierra deploy with embedded staff during launch, which gets a configured agent live quickly but means the deepest workflow knowledge starts outside your team. Lorikeet uses a forward-deployed product manager and engineer, a sandbox that stands up in roughly 20 to 30 minutes, and a target of operational within about a month, with workflows configured in plain English so your own team can read and change them afterward. For a regulated business that re-tunes guardrails every time a rule changes, the question of who can edit the agent without filing a vendor ticket is a recurring cost, not a one-time launch detail. Ask each vendor to show you the configuration surface and to confirm that a non-engineer on your compliance team could adjust a disclosure or a threshold themselves.
Security and data posture round out the shortlist. A regulated buyer should confirm SOC 2 status, HIPAA readiness through a business associate agreement where health data is involved, GDPR alignment, PII redaction, role-based access control, and data residency in the regions you operate. Lorikeet holds SOC 2, is BAA-ready for HIPAA, is GDPR-aligned, supports US, AU, and UK data residency, maintains contractual no-train agreements with its model providers, and has passed security reviews including those of major US banks. Request the current attestations from any vendor under NDA, since scope and dates drift and matter for a regulated procurement file.
Lorikeet's Take
Decagon and Sierra are both credible platforms, and a regulated team can succeed with either if it pushes hard on auditability and pre-launch guardrails. The reason regulated buyers shortlist Lorikeet alongside them is narrower and specific: the entire platform is built so a compliance team can sign off before launch rather than apologize to a regulator after. That is the bar regulated CX is actually graded on, and it is the bar Lorikeet was designed to clear.
If you are choosing an AI support platform for a regulated business, book a Lorikeet demo and bring your hardest tickets and your compliance lead. We will run them against your guardrails before you sign.
