Outcome pricing sounds like the regulated buyer's dream: pay only when the AI resolves the ticket. The fine print is who defines "resolved" and whether the model quietly steers your agent toward the easy tickets and away from the disputes, KYC unlocks, and claims your regulator actually cares about.
Outcome-priced AI support agents charge per resolved ticket instead of per seat or per conversation. For regulated industries (fintech, financial services, healthcare, insurance, gaming) the pricing model matters as much as the model architecture, because a per-resolution charge that excludes escalations and lets the vendor define resolution can reward volume on simple tickets while the hard, high-risk cases stay unhandled. This guide ranks seven vendors on how their outcome pricing behaves under regulated conditions.
Human-handled support tickets cost roughly $1.25 to $4 each on a blended basis, so outcome pricing only wins if the per-resolution rate stays well under that and the vendor does not bill on escalations.
The single most important contract term in regulated outcome pricing is who defines a resolution. If the vendor defines it, the incentive bends toward easy wins.
Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues by 2029, up from low double-digits in 2024.
Regulated buyers should price the hard 20% of tickets, not the easy 80%. A low headline rate per outcome can hide a high effective cost once escalations, retries, and compliance review are counted.
Audit trails, pre-launch simulation, and runtime guardrails determine whether an outcome-priced agent is approvable by your compliance team, not the price alone.
Last updated: June 2026
Outcome pricing started as a fairness pitch. Instead of paying for seats your agents never fully use, or per conversation whether or not anything got solved, you pay when a ticket is resolved. In a regulated business the pitch needs a second look. A vendor that bills only on full resolution has a built-in reason to chase the tickets that resolve cleanly (password resets, balance checks, shipping status) and to escalate the ones that are messy, ambiguous, or risky. In fintech, healthtech, and insurance the messy tickets are the point: a card dispute, a KYC unlock, a claims status question, a wire that did not arrive. This ranking judges each vendor on the thing that actually governs cost and risk for a regulated buyer, which is how the outcome-pricing model behaves on the hard tickets and whether the platform can prove what it did. It is a buyer-neutral list based on shipping product, real regulated customers, and what compliance teams approve.
What Is Outcome-Priced AI Support for Regulated Industries?
Outcome-priced AI support is a billing model where you pay a fixed amount each time an AI agent resolves a customer ticket end-to-end, rather than paying per agent seat or per conversation started. Escalations to a human typically are not billed. In regulated industries the model is evaluated alongside compliance capability, because the resolution definition, the escalation treatment, and the audit trail together decide both your cost and your regulatory exposure.
The category splits on two questions. First, who defines a resolution: the vendor or you. When the customer holds the veto on what counts as resolved, the incentive points at solving real problems rather than at closing tickets. Second, what the agent can actually do on a regulated ticket. A retrieval-and-reply bot that answers from a knowledge base is cheap to price per outcome because it only attempts easy tickets. An agent that verifies identity, runs a risk check, files a dispute, and produces an audit trail is doing the regulated work, and its outcome price has to be read against that depth.
Outcome (resolution): A ticket the AI handled to completion without a human finishing it. The contractual definition, and who controls it, is the term that most affects regulated cost.
Defence in depth: A layered safety model (pre-launch adversarial simulation, inbound message checks, outbound guardrails, and post-resolution QA) that lets a compliance team approve agent behavior before go-live rather than audit it after an incident.
Lorikeet is an AI customer support platform built for complex and regulated businesses, with roughly 80% of its customers being US financial institutions and fintechs. It runs AI concierges that resolve issues end-to-end across chat, email, voice, SMS, and WhatsApp, prices per resolution with the customer holding the veto on what counts as resolved, and does not charge for escalations.
At-a-Glance Comparison
At a glance
Platform: Lorikeet · Best For: Regulated companies that want outcome pricing with the customer defining resolution · Key Strength: Per-resolution pricing, customer-defined resolution, no charge on escalations, audit trail plus defence in depth · Pricing: ~$0.80–$0.95 per chat, email, or SMS resolution; ~$1.20–$1.50 per voice; Coach ~$0.25–$0.30 per ticket
Platform: Decagon · Best For: Enterprises wanting customer-selectable per-conversation or per-resolution billing · Key Strength: Flexible billing models with white-glove deployment · Pricing: Custom; reported median contract near $400K/year
Platform: Sierra · Best For: Enterprises wanting pure outcome-only billing · Key Strength: Pay only on full resolution; escalations cost nothing · Pricing: Custom; reported $50K-$200K/year, rate per resolution negotiated
Platform: Fin by Intercom · Best For: Intercom and helpdesk customers wanting the lowest published per-outcome rate · Key Strength: Transparent published per-resolution price · Pricing: $0.99 per resolution, plus helpdesk seat fees
Platform: Gradient Labs · Best For: Financial services teams wanting outcome pricing from a regulated-focused newer entrant · Key Strength: Per-resolution pricing with a financial-services posture · Pricing: Custom per-resolution (contact sales)
Platform: Ada · Best For: Mid-market teams with high chat volume moving to resolution-based billing · Key Strength: Established resolution-based pricing and broad channel coverage · Pricing: Custom; reported median near $70K/year
Platform: Salesforce Agentforce · Best For: Salesforce-native orgs wanting per-action consumption billing · Key Strength: Deep Salesforce data and CRM integration · Pricing: ~$2 per conversation (consumption), plus platform costs
The 7 Best Outcome-Priced AI Support Agents for Regulated Industries in 2026
1. Lorikeet
Lorikeet is the AI customer support platform built specifically for complex and regulated companies, and its outcome pricing is designed to remove the selection bias that hurts regulated buyers. It charges roughly $0.80–$0.95 per chat, email, or SMS resolution and about $1.20–$1.50 per voice resolution, the customer defines what counts as a resolution, and escalations are not charged. Most vendors price per outcome and reserve the right to call a deflection a resolution. Lorikeet gives that veto to the customer, which is the difference between a pricing model that rewards solving hard tickets and one that rewards closing easy ones.
Key Features
Per-resolution pricing with the customer holding the veto on what counts as a resolution, and no charge on escalations, so the incentive points at genuine end-to-end resolution rather than deflection.
End-to-end resolution of multi-step regulated tickets (KYC unlocks, disputes, transfers, claims) across chat, email, voice with sub-1-second latency, SMS, and WhatsApp, plus outbound re-engagement.
Defence in depth: pre-launch adversarial simulation and red-teaming, inbound message checks, outbound guardrails, and 100% post-resolution QA, so compliance can approve behavior before go-live.
Deterministic structured workflows combined with natural-language workflows in a single interaction, all configurable in plain English, with audit trails for regulator examinations.
Coach, a standalone analytics and 100% automated QA agent at roughly $0.25–$0.30 per ticket, that scores ticket quality, verifies resolutions, and runs root-cause analysis.
Ideal For
Regulated companies (fintech, financial services, healthtech, insurance, gaming) that want outcome pricing without the selection bias, and need every resolution backed by an audit trail their compliance team can sign off on. Lorikeet reports a regulated fintech reaching around 85% automation with equal-or-better CSAT, and cross-border payments customers reporting retention lifts on AI-handled tickets versus human-handled ones. The honest limitation: Lorikeet is purpose-built for complex regulated workflows, so a simple FAQ-only deflection use case may be over-served by it and cheaper to run on a basic bot.
Pricing
Roughly $0.80–$0.95 per chat, email, or SMS resolution and about $1.20–$1.50 per voice resolution. Coach runs about $0.25–$0.30 per ticket. Escalations are not charged and the customer defines resolution. Against a human baseline of roughly $1.25 to $4 per handled ticket, the model is built to stay below the cost it replaces.
2. Decagon
Decagon is a high-end enterprise AI agent platform with named financial-services and fintech customers. It offers customer-selectable billing, either per conversation or per resolution, with white-glove implementation. The flexibility is genuine, and so is the catch: vendors at this tier sell embedded engineering as a feature, which is partly a sign the platform is hard to configure without them.
Key Features
Customer-selectable per-conversation or per-resolution billing models.
Voice, chat, and email channels on one platform.
White-glove deployment with embedded engineering during launch.
Production deployments processing large interaction volumes for enterprise customers.
Significant venture backing and rapid growth.
Ideal For
Large financial services and fintech enterprises with sizeable support budgets that can dedicate engineering to a multi-month deployment and want to choose between per-conversation and per-resolution billing.
Pricing
No published rates. Industry data points to a platform fee plus per-conversation or per-resolution charges, with median total contract value reported near $400,000 per year.
3. Sierra
Sierra is the enterprise AI agent company from Bret Taylor and Clay Bavor, and its signature is pure outcome-based pricing: customers pay only when the AI fully resolves a case, and escalations cost nothing. The incentive-alignment pitch is real. So is the side effect for regulated buyers: a vendor paid only on full resolution has a structural reason to favor the tickets that resolve cleanly, which in fintech and insurance are usually not the ones that matter most.
Key Features
Outcome-only pricing: pay only on full resolution, with escalations free.
Voice, chat, and email channels.
Branded AI persona approach to deployment.
Strong enterprise procurement story and high-touch implementation.
Established footprint across large enterprise brands.
Ideal For
Large enterprises, including financial services brands, that want billing aligned to full resolution and have the procurement appetite for a six-figure annual commitment, provided they confirm in contract who defines resolution.
Pricing
Not published. Enterprise contracts are reported in the $50,000 to $200,000 per year range, with the rate per resolution negotiated case by case.
4. Fin by Intercom
Fin is the AI agent layered on Intercom's messenger and helpdesk, and its $0.99 per resolution is the lowest published price in the category. Transparency is a real advantage here. The trap for regulated buyers is reading a low per-resolution sticker as a low total cost: $0.99 still rewards a vendor for handling many easy tickets, and Fin's depth on multi-step regulated actions is narrower than platforms purpose-built for fintech or healthcare.
Key Features
$0.99 per resolved outcome, among the lowest published per-resolution rates.
Works with Salesforce and HubSpot helpdesks, not only Intercom.
Fast trial-to-deployment path with a published free trial of outcomes.
Optional copilot for human agents.
Strong analytics and reporting add-ons.
Ideal For
High-volume teams already using Intercom or comfortable adding it, who want the lowest published per-outcome price for largely standard ticket types and have lighter regulated-action requirements.
Pricing
$0.99 per outcome, plus helpdesk seat fees if not already an Intercom customer, plus optional copilot and analytics add-ons.
5. Gradient Labs
Gradient Labs is a newer entrant positioning its AI support agent for financial services and other regulated sectors, with per-resolution pricing. The regulated focus is a genuine differentiator among recent arrivals. As a younger platform, the practical questions to confirm are deployment maturity, the breadth of channels, and the depth of pre-launch testing and audit tooling your compliance team will need.
Key Features
Per-resolution pricing aimed at outcome-aligned billing.
Positioning and product posture oriented toward financial services and regulated support.
Focus on autonomous resolution of customer tickets rather than retrieval-only answers.
Compliance-conscious design pitched at regulated buyers.
Emerging integration footprint with common support stacks.
Ideal For
Financial services and regulated teams that want outcome pricing from a vendor explicitly built around their sector and are comfortable evaluating a newer platform on deployment references and audit capability.
Pricing
Custom per-resolution pricing (contact sales). Confirm the resolution definition and escalation treatment in contract, as both drive effective cost.
6. Ada
Ada is one of the most established AI support vendors and has moved to resolution-based billing as the category shifted. It does breadth well across chat, voice, and email with mature helpdesk integrations. The honest read for regulated buyers: Ada grew up as a chatbot platform, and that lineage shows most on deep multi-step regulated action chains and audit-grade logging, where architecture is hard to change later.
Key Features
Resolution-based billing aligned to the outcome-pricing trend.
Multi-channel coverage across chat, voice, and email.
Mature integrations with Salesforce, Zendesk, and major helpdesks.
Established enterprise deployment playbooks.
Claimed autonomous resolution rates on supported workflows.
Ideal For
Mid-market and enterprise teams with high inbound chat volume that prefer a long-track-record vendor and whose regulated-action depth requirements are moderate.
Pricing
Not published. Marketplace data shows median annual contracts near $70,000, with a wide range based on company size, increasingly structured around resolution-based billing.
7. Salesforce Agentforce
Salesforce Agentforce brings agentic AI into the Salesforce platform with consumption-based pricing around $2 per conversation. For Salesforce-native organizations the data and CRM depth are the draw. The trade-offs for regulated buyers: per-conversation consumption is not the same as per-resolution outcome pricing, the effective cost depends on conversation volume rather than solved tickets, and platform costs sit on top.
Key Features
Consumption-based pricing at roughly $2 per conversation.
Deep native integration with Salesforce CRM data and workflows.
Coexists with existing Salesforce service tooling.
Broad ecosystem of connectors and platform services.
Enterprise governance and administration controls.
Ideal For
Salesforce-native enterprises that prioritize CRM data depth and are comfortable with per-conversation consumption billing rather than per-resolution outcome pricing. Lorikeet coexists with Agentforce for teams that want a regulated-grade resolution layer alongside Salesforce.
Pricing
Approximately $2 per conversation on a consumption model, plus underlying Salesforce platform and license costs.
Outcome pricing only protects a regulated buyer when the customer defines resolution and escalations are free. See how Lorikeet prices per resolved ticket.
How to Choose an Outcome-Priced AI Support Agent for a Regulated Business
Most pricing comparisons stop at the headline rate per outcome. In a regulated business the rate is the least interesting number. The five lenses below decide whether outcome pricing actually saves money and survives a compliance review.
Who Defines a Resolution
This is the term that governs everything. If the vendor decides what counts as resolved, every ambiguous ticket tilts toward billable. Ask for the contractual definition in writing and confirm whether you hold the veto. When the customer defines resolution (as with Lorikeet), the incentive points at solving real problems rather than at closing tickets to trigger a charge.
How Escalations Are Treated
In regulated support, escalation is a feature, not a failure: some tickets should reach a human. If escalations are billed, the vendor is paid to avoid them, which is exactly backward for the high-risk cases. Confirm that escalations are not charged. Sierra, Fin, and Lorikeet all treat escalations as free, which is the right default.
Effective Cost on the Hard 20%
A low rate per outcome can hide a high effective cost once you count retries, partial resolutions, and the tickets that escalate after the agent spends effort on them. Model your real ticket mix, not the demo mix. Compare against the human baseline of roughly $1.25 to $4 per handled ticket, and price the hard, regulated tickets specifically, because those carry the cost and the risk.
Audit Trail and Pre-Launch Provability
Pricing is moot if compliance cannot approve the agent. The standard is a replayable record of every tool call, prompt, and reasoning step, plus the ability to test guardrails before go-live rather than after an incident. Defence in depth (pre-launch simulation, inbound checks, outbound guardrails, and post-resolution QA) is what lets your compliance team sign off in advance. Ask whether you can run the test suite and read the report before launch.
Channel Coverage on One Engine
Regulated customers do not stay on one channel. A card lock starts on the phone, a dispute on chat, a confirmation by email. If voice runs on a different stack than chat and email, you get two agents pretending to be one, and the customer repeats themselves. Confirm that channels share one workflow engine and memory, and that the agent can take actions on voice rather than only routing to a human.
Questions to ask your vendor
Demos are built to look good. These questions are built to make a demo break.
Who defines a resolution in our contract, and can I see the exact wording before signing?
Are escalations to a human billed, and if so, at what rate?
What is the effective per-resolution cost on our hardest 20% of tickets, modeled on our real mix?
Can my compliance team run your guardrail test suite before go-live and read the pass/fail report?
Show me an end-to-end audit trail for a regulated ticket your AI handled last week, with every tool call and the reasoning between them.
Does voice run on the same workflow engine as chat and email, and can the agent take actions on a call?
What happens to the bill when the agent partially resolves a ticket and then escalates?
Lorikeet's Take on Outcome Pricing in Regulated Industries
Outcome pricing is the right idea pointed in the wrong direction for most vendors. Pay-on-resolution sounds aligned, but if the vendor defines resolution and bills on escalation avoidance, the model quietly rewards handling the easy 80% and steering clear of the regulated tickets that carry your CFPB, AUSTRAC, or HIPAA exposure. That is a pricing model selecting against the work you most need done.
The fix is structural, not cosmetic. Let the customer define what counts as a resolution, never charge for escalations, and back every resolution with an audit trail and defence in depth so compliance can approve behavior before launch. That is how Lorikeet prices at roughly $0.80–$0.95 per chat, email, or SMS resolution and $1.20–$1.50 per voice, with Coach providing 100% automated QA at about $0.25–$0.30 per ticket. If that is the bar your team uses, see how Lorikeet handles end-to-end resolution.
Key Takeaways
Outcome pricing protects regulated buyers only when the customer defines resolution and escalations are not billed; otherwise the model rewards easy tickets over the high-risk ones.
Compare effective cost on your hardest 20% of tickets against a human baseline of roughly $1.25 to $4 per handled ticket, not the headline rate per outcome.
Lorikeet prices around $0.80–$0.95 per chat, email, or SMS resolution and $1.20–$1.50 per voice, lets the customer veto what counts as resolved, and does not charge for escalations.
Sierra and Fin offer clean outcome models but, like any pay-on-full-resolution vendor, can bias toward easy tickets; Decagon and Ada use custom resolution billing; Salesforce Agentforce is per-conversation consumption, not per-resolution.
Audit trails, pre-launch simulation, and runtime guardrails decide whether an outcome-priced agent is approvable, so price and compliance must be evaluated together.
Conclusion
In 2026 the question for a regulated buyer is not whether to adopt outcome-priced AI support but whether the specific model on the table is aligned to your hard tickets. A per-resolution rate is only a saving if the vendor does not define resolution in its own favor, does not bill you for the escalations you want, and can prove what the agent did when your regulator asks.
The seven platforms above each fit a different profile. Lorikeet is the answer for regulated companies that want outcome pricing without the selection bias: customer-defined resolution, free escalations, and an audit trail plus defence in depth that compliance can approve before go-live. The other six are credible depending on your existing stack, budget, and how much regulated-action depth you need.
If you are evaluating outcome-priced AI support for a regulated business, book a Lorikeet demo and bring your hardest 10 tickets, and we will run them in your stack against your guardrails before you sign.
