The compliance features that matter in enterprise voice AI are guardrails enforced outside the language model, deterministic handling of sensitive steps such as identity checks and payments, redaction of personal data, recording and consent controls, a replayable audit trail, quality review of every call, and published certifications (SOC 2, ISO 27001, HIPAA, and PCI DSS if the agent takes card details). Lorikeet, Sierra, PolyAI, Decagon, Fin (now part of Salesforce), Kore.ai and NiCE Cognigy each publish some of these controls; the comparison below shows which, with a link to the vendor's own page for every claim.

Most enterprise voice AI vendors will demo a friendly conversation. Your compliance team will ask whether the agent quoted a rate it should not have, recorded a call without consent, or left an audit trail you can hand a regulator. The platforms that survive that second conversation are the short list worth your time.

Key takeaways

• Voice is the highest-risk channel in regulated support: a spoken promise about an interest rate, a payout, or a medical instruction carries the same weight as a written one, with none of the time to review it.

• Compliance buyers now evaluate three things first: guardrails on what the agent can say, recording and consent controls, and an audit trail that survives a regulator examination.

• Certifications differ by vendor. Sierra and PolyAI list PCI DSS on their own pages; Lorikeet lists SOC 2 Type II, ISO 27001:2022, HIPAA and GDPR. If the agent will take card details by phone, check PCI scope first.

• Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues without human intervention by 2029, so more regulated calls will be handled end to end by AI.

• Latency is a compliance issue too: a voice agent that pauses for several seconds invites the customer to talk over scripted disclosures, breaking the very compliance step the script exists to deliver.

Last updated: October 2026

Voice support in a regulated business has a problem text channels do not. A customer on the phone asking "so my rate is locked at 4.9%, right?" is not a satisfaction ticket, it is a compliance event happening in real time. The agent has one second to answer, and a wrong answer is a recorded mis-statement of terms. Most voice AI vendors will quote you a deflection rate and a natural-sounding voice. Neither tells you whether the agent will quote a rate it should not, record a call in a two-party-consent state without disclosure, or produce a log your compliance team can hand to a regulator. This guide compares enterprise voice AI platforms on the criteria that actually decide a regulated procurement: guardrails on what the agent says, recording and consent handling, and audit depth.

What is Enterprise Voice AI for Compliance?

Enterprise voice AI for compliance is the use of large language model voice agents to handle inbound and outbound calls in regulated industries, such as financial services, lending, insurance, and healthcare, with controls that keep the agent inside legal and regulatory boundaries. That means guardrails on rates, promises, and eligibility statements; consent and recording handling aligned to jurisdiction; and a complete, replayable audit trail of every call.

The category splits on where the controls live. Consumer-grade voice bots put the rules in a prompt and hope the model follows them. Compliance-grade platforms enforce the rules outside the model: a guardrail layer that checks every outbound utterance before it is spoken, a recording and consent step that fires based on the caller's jurisdiction, and a log that captures the reasoning behind each action. The difference shows up the first time an agent is asked something it should decline to answer. A prompt-only system improvises. A guardrailed system refuses, discloses, or escalates, the way your compliance team scripted it.

Guardrail: A control that checks the agent's behavior outside the language model, for example blocking an outbound sentence that states a rate or makes a promise the agent is not authorized to make, before the customer hears it.

Audit trail: A timestamped, replayable record of every spoken turn, tool call, and reasoning step on a call, the artifact a compliance team uses during a regulator examination or a customer dispute.

How we chose and checked these platforms

We kept the seven platforms from the earlier version of this guide, all of which sell AI voice agents to enterprise and regulated buyers, and judged each on what it publishes about the controls in the opening answer: guardrails, deterministic handling of sensitive steps, redaction, audit and quality review, certifications, and pricing. Every vendor fact was checked on the vendor's own public pages on 22 October 2026, linked in each entry. Where a vendor does not publish something on the pages we checked, we say so rather than guess.

Lorikeet publishes this guide and is one of the tools listed. We have tried to describe every product, including our own, using only what each vendor publishes, and we link to the source for each claim.

Which voice AI platforms handle compliance? At-a-glance comparison

1. Lorikeet: regulated teams that want voice on the same engine as chat and email

Lorikeet is an AI customer support platform for complex and regulated businesses. One agent works across chat, email, voice, SMS and WhatsApp, and voice runs on local numbers in the United States, United Kingdom and Australia with the same workflows, guardrails and quality scoring as the text channels (see its voice page). Sensitive steps such as payments and identity checks run as code the agent can invoke but never alter (how it works).

For compliance review, every customer interaction, model choice and action is tracked, runtime guardrails detect sensitive topics and escalate to your team, and the Coach agent quality-scores every conversation, human or AI. Its trust and security page lists SOC 2 Type II, ISO 27001:2022, HIPAA and GDPR, Business Associate Agreements for healthcare customers, and zero-data-retention agreements with all model vendors. Before launch, you replay historical tickets and synthetic scenarios in bulk and deploy only the topics where the results hold.

Limits: voice numbers are live in three countries, and the trust page does not list PCI DSS, so teams that take card payments by phone should keep card capture in a PCI-scoped system. Setup is more than switching on a bot: you connect your systems, train the agent and run simulations first.

Pricing: voice resolutions cost $1.50 on Start and $1.20 on Scale (3 minute average), on plans from $2,100 a month paid annually, and escalations to a person are not charged (see the pricing page). A 30-day free trial is available.

2. Sierra: large enterprises that take card payments by voice

Sierra builds AI agents for large consumer brands across voice, chat, email and other channels. Its voice agent handles interruptions, background noise and accents, adapts tone to customer sentiment, and speaks in over 55 languages. For payments, card details are collected via DTMF tones through Level 1 PCI compliant infrastructure and routed directly to your payment processor.

Its trust and reliability page says interactions with systems of record are deterministic and controlled, that LLMs are wrapped in supervisory layers to reduce hallucinations and prevent abuse, and that cardholder data never touches Sierra's core platform, LLMs or persistent storage. It lists SOC 2, HIPAA, GDPR, PCI, FedRAMP High, CCPA, CSA STAR, ISO 27001 and ISO 42001, and states PCI DSS Level 1 Service Provider certification.

Best for: large enterprises whose calls include card payments and whose procurement wants the broadest published certification list. Limits: the pages we checked do not publish prices or describe self-serve evaluation, so plan for a sales-led process. Pricing: not published on the pages we checked.

3. PolyAI: phone-first contact centres

PolyAI is a voice-first platform for enterprise contact centres. Its homepage lists SOC 2+ Type 2, ISO 27001, PCI DSS and GDPR, alongside guardrails, PII redaction and human-in-the-loop controls. Its financial services page adds Cyber Essentials and HIPAA and describes customers making payments and account transactions in natural language, and its security page describes regular audits and third-party penetration testing.

According to its pricing page, testing includes test calls, simulated conversations and A/B tests on live traffic at no extra charge, latency and cost per agent show on the console in real time, and voice, chat and messaging run on the same platform and agents.

Best for: phone-heavy contact centres that want a specialist voice vendor with card-payment certifications. Limits: its pages lead with voice, so if journeys often start in chat, check how audit records follow the customer across channels. Pricing: the voice agent is priced per minute through sales; the model-only Dialog-RSN-1 API is 2 cents per connected minute, billed per second, without telephony and orchestration.

4. Decagon: enterprises that want caller authentication and QA on every conversation

Decagon's voice agents support customers in 70+ languages with automatic detection and language switching. Its security page is one of the more detailed in this list: voice authentication to verify callers on sensitive workflows, just-in-time API tokens scoped for minimal privilege, redaction of sensitive data from logs and transcripts with Google's DLP service, a detection system for adversarial prompts and policy violations, a built-in supervisor that catches hallucinations before responses are sent, and an always-on QA system that reviews every conversation against your custom criteria and flags compliance risks. It also enforces zero-data retention with AI providers such as OpenAI and Anthropic.

Best for: large enterprises that want caller authentication and conversation-level QA from one vendor. Limits: certifications sit on Decagon's Trust Center rather than the security page we checked, so request the reports directly. Pricing: not published on the pages we checked.

5. Fin (now part of Salesforce): teams already running Fin in chat

Fin Voice brings Fin's AI agent to phone support and runs on Apex Flash, a Fin model built for latency-sensitive tasks. On a call it can issue refunds, update records, look up account details and verify information through your APIs, and teams can drill into recordings, transcripts and answer logic to see why Fin responded the way it did, then run simulated conversations to catch regressions before launch. Procedures handle multi-step work, business rules and approvals within parameters you define, according to its trust page, which lists SOC 2 Type II, ISO 27001, ISO 27701, ISO 27018, ISO 42001, HIPAA and GDPR/CCPA, with documentation available to self-serve.

Best for: teams already using Fin who want one vendor for chat and phone. Limits: Fin Voice is sold through sales rather than switched on in the product. Pricing: per its pricing page, Fin is $0.99 per outcome and Fin Voice is $1.99 per voice outcome via sales.

6. Kore.ai: enterprises that want policy enforced outside the LLM across many use cases

Kore.ai sells AI agents for customer experience, including voice, with multilingual conversations and language switching. It says compliance rules are enforced at the engine level, outside the LLM, so no AI response can override a policy constraint, and that every agent action and outcome is traced and auditable, with 100% observability. Its AI quality management automates reviews on 100% of calls, and its pre-built use cases ship with integrations and HIPAA compliance built in, with your SOPs applied on top. One published banking example replaced a legacy IVR and supports 1.5 million annual voice interactions across 23 languages.

Best for: large enterprises standardising on one conversational AI platform across service, banking and other functions. Limits: the full certification list is not on the page we checked, and the platform spans many use cases beyond support, so scope a regulated phone line tightly. Pricing: not published on the pages we checked.

7. NiCE Cognigy: contact centres that need wide language coverage

Cognigy is now part of NiCE and sells as NiCE Cognigy. Its voice AI agents speak over 100 languages with real-time translation for human agents, route time-sensitive calls to the best-suited available agent with context attached, and support outbound calling for proactive service. Customer stories on the same page include DHL, which it says handles more than 30 million service inquiries, and Frontier Airlines, with 800,000 monthly conversations automated. NiCE Cognigy also says it was named a Leader in the Forrester Wave 2026 for conversational AI platforms for customer service.

Best for: large contact centres, especially those already on NiCE, that need broad language coverage and routing intelligence. Limits: the voice and about pages we checked do not describe guardrails, audit trails or certifications, and its trust centre needs JavaScript to load, so ask for the security pack early. Pricing: not published on the pages we checked.

How to Choose an Enterprise Voice AI Platform for Compliance

Choose on controls you can verify, in this order: guardrails, consent and call-hour handling, audit depth, latency, one engine across channels, and pre-launch testing. Voice procurement in a regulated business is different from generic CX. Most buying guides lead with containment rate, voice naturalness, and average handle time. In a regulated business those are downstream of one question: can the agent say only what it is allowed to say, and can you prove it. The lenses below separate platforms that survive a compliance review from those that do not.

Guardrails on Rates, Promises, and Eligibility

The single highest-risk thing a voice agent does is speak. Ask whether guardrails are enforced outside the language model, so a sentence that states a rate, makes a promise, or asserts eligibility is checked before the customer hears it, not generated and hoped over. A prompt that says "do not quote rates" is not a control, it is a suggestion. The right answer is a layer that blocks, discloses, or escalates based on rules your compliance team wrote and can read.

Recording, Consent, and Call-Hour Rules

Voice carries obligations text does not. Two-party-consent jurisdictions require disclosure before recording. Outbound calling is bound by do-not-call lists and permitted call hours. Ask how the platform handles consent disclosure by jurisdiction, how it enforces call windows on outbound campaigns, and whether those rules are enforced by the platform or left to whoever wrote the script. If the answer lives in a prompt, it will eventually be skipped under load.

Audit Trail Depth

When a customer disputes what they were told, or a regulator asks, you need a replayable record of the call, every spoken turn, every tool call, and the reasoning between them, with timestamps. Most vendors hand you a recording and a transcript and call it a log. Ask whether you can replay the full reasoning chain for any call from 90 days ago, and whether the agent's decision points are captured alongside the audio. Audit-grade logging is what lets compliance sign off before launch instead of apologizing after.

Latency and Disclosure Integrity

Latency is usually framed as a customer experience number. In compliance it is also an integrity number. An agent that pauses for several seconds invites the caller to interrupt, and the thing most often interrupted is the scripted disclosure. Sub-second response keeps disclosures and confirmations intact. Ask for measured response latency, not a marketing figure, and ask what happens to a disclosure when the customer talks over it.

One Engine Across Channels

Customers do not stay on one channel. A dispute starts on chat and finishes on a call. If voice runs on a different stack than chat and email, the agent loses context at the handoff and the customer repeats themselves, and worse, the compliance controls and audit trail can differ between channels. Ask whether voice runs on the same workflow engine as the other channels, with shared memory and a single audit trail, or whether two systems are bolted together with a transcript. Voice on a single engine is the cleaner compliance story.

Provable Before Go-Live

Compliance teams will not approve behavior they have only been promised. Ask whether you can run an adversarial test suite against the voice agent before launch, red-team the calls that scare you, and read the pass and fail report. Platforms that build pre-launch simulation in let your compliance team approve real behavior. Platforms that only offer runtime monitoring ask your team to approve faith.

Questions to ask your vendor

Demos are built to look good. The questions below are built to make one break.

• Show me a call where your agent declined to quote a rate or make a promise because of a guardrail, and walk me through the config that stopped it.

• How do you handle recording consent in a two-party-consent state, and is that enforced by the platform or by the script?

• On outbound, how do you enforce do-not-call lists and permitted call hours across jurisdictions?

• Replay a full call from last week for me, every turn, every tool call, and the reasoning between them.

• What is your measured voice response latency, and what happens to a scripted disclosure when the customer talks over it?

• Can my compliance team run an adversarial test suite against the voice agent before go-live and read the report?

• Does voice run on the same engine as chat and email, with one audit trail, or two systems joined by a transcript?

Bottom line

The deciding question for regulated voice is not whether the agent sounds human but whether you can show a regulator what it said, why, and what it did. Shortlist on the controls each vendor publishes: Sierra and PolyAI if the agent will take card payments, Decagon and Kore.ai for automated review of every conversation, Fin (now part of Salesforce) if you already run Fin, NiCE Cognigy for language coverage, and Lorikeet if you want voice on the same engine, guardrails and quality scoring as chat and email. Then bring your hardest calls to each demo and work through the questions above.

If that shortlist includes us, book a Lorikeet demo and bring the calls where a wrong word is a compliance event.