In regulated industries, end-to-end resolution means the AI completes the full case with deterministic actions and a full audit trail, not deflection. Lorikeet is purpose-built for this.

Gartner projects that agentic AI will autonomously resolve 80% of common customer service issues by 2029, without human intervention. In 2026, the best production deployments are landing 55% to 70% automation. But in fintech, healthtech, and insurance, the headline automation number is the least interesting part of the decision. What matters is whether the platform genuinely resolves a case end to end, executes the right backend actions deterministically, and leaves an audit trail a regulator can follow.

This guide ranks 8 AI customer support platforms specifically on their fitness for regulated industries. The criteria are resolution depth (not deflection), deterministic action execution, audit-trail completeness, and compliance posture. We include pricing, channels, certifications, and the honest gaps for each vendor, so a CX or risk leader can shortlist without a discovery call.

Where we cite resolution rates, treat them as vendor-stated unless noted. Many of the figures below come from competitors' own published material, which is a useful baseline but not an independent benchmark.

What to look for in an AI platform for regulated industries

Regulated support is different from ecommerce support. A wrong answer about a refund window is an annoyance. A wrong money-movement action, an unlogged PII disclosure, or an unauditable decision is a compliance event. When you evaluate platforms for fintech, healthtech, or insurance, weight the following far more heavily than raw containment percentage.

• Resolution depth, not deflection. Can the platform complete the hard 20% of cases that require reading and writing to your core systems, or does it only answer the easy questions and route the rest to a human?

• Deterministic action execution. For auth, refunds, and money movement, the same inputs must produce the same action every time. A probabilistic free-text agent is not acceptable for steps with legal or financial consequences.

• Audit trail per conversation. Every decision needs a timestamped record: what the AI saw, what policy it applied, why it acted, and what it changed. This is what survives a regulatory review.

• Compliance certifications that match your obligations. SOC 2 Type II is table stakes. HIPAA is mandatory for healthtech. Data residency and retention windows matter for FINRA, BSA, and APRA-regulated workloads.

• Guardrails on both sides of the conversation. Runtime checks on what the customer says (prompt injection, distress, fraud signals) and on what the AI is about to say or do, with corrective actions rather than a single blunt block.

Quick comparison: 8 AI platforms for regulated resolution

How these platforms were selected

This is a focused list for regulated buyers, not a ranking of every AI support vendor. The pool was chosen and ordered against the criteria below.

Selection criteria:

• Capable of executing multi-step backend actions, not only retrieving knowledge-base answers.

• Production deployments in at least one regulated vertical (fintech, healthtech, or insurance).

• A documented compliance posture (SOC 2 at minimum) that a risk team can evaluate.

• Enterprise-grade controls for data handling, access, and logging.

Evaluation factors used to rank them:

• Resolution depth versus deflection on complex, account-specific cases.

• Determinism of high-stakes actions (auth, refunds, money movement).

• Completeness and accessibility of the per-conversation audit trail.

• Certification coverage relative to regulated obligations, including HIPAA.

• Total cost of ownership and time to a controlled production rollout.

What is end-to-end resolution (and how it differs from deflection)

End-to-end resolution means the AI completes the customer's case inside your systems: it authenticates the customer, gathers the required information, applies your policy, executes the necessary read and write actions against your core systems, runs guardrail checks, and records the outcome with a full rationale. The customer leaves with the problem actually solved, and you have a defensible record of how it happened.

Deflection is the opposite. A deflection metric counts a ticket as "handled" when the customer stops asking, whether or not anything was resolved. A bot that answers a billing question with a help-center link, or that says "I have created a ticket for our team," has deflected, not resolved. Containment and deflection rates can look impressive while the hard cases, the ones that actually cost money to handle, still land on a human.

In regulated industries the distinction is not cosmetic. A deflective bot cannot issue a refund, reverse a fee, update a policy, or correct an account error, because those are write actions with compliance consequences. Genuine resolution requires the platform to take those actions safely and to prove it did so correctly. The capabilities that separate resolution from deflection:

• Backend write access to the systems of record, governed by policy and permissions.

• Deterministic execution of the consequential steps, so behavior is repeatable and testable.

• Branching, multi-step workflows that mirror how a trained human agent would handle the case.

• An audit trail that captures the decision, the data, and the action for every interaction.

The 8 best AI platforms for end-to-end resolution in regulated industries

1. Lorikeet

Best for: Fintech, healthtech, and insurance teams that need to genuinely resolve the hard 20% of cases, with deterministic action execution and a regulator-ready audit trail.

Lorikeet is an agentic AI customer support platform built specifically for regulated, high-stakes industries. Its positioning is direct: it is the AI concierge that resolves rather than deflects, and it is designed to solve the hard 20% of cases that black-box chatbots route to a human. Instead of treating support as a question-answering problem, Lorikeet treats each ticket as a case to be executed: it gates and authenticates, follows branching logic, reads from and writes to core systems, runs guardrail checks, produces an outcome, and records a full audit trail of what happened and why.

The architecture is what makes this defensible in a regulated setting. Lorikeet wraps a conversational shell around deterministic execution kernels for the steps that carry legal or financial weight, such as authentication, refunds, and money movement. Within a single conversation it can step up to strict determinism for a consequential action and step back down to flexible dialogue for the rest. That step-level determinism is the difference between an agent you can certify and one you can only hope behaves. One money-movement kernel scored 299 out of 300 across 100 repeated runs in internal testing, which is the kind of repeatability a risk team can actually sign off on.

Lorikeet runs a dual-agent model. A Concierge agent handles resolution, while a Coach agent provides always-on quality assurance, scoring tickets against your SOPs and policy rather than just tone. Dual-sided guardrails run runtime checks on every incoming customer message and every AI response, with corrective actions (alert, steer, escalate, or add an action) and a steer-once-then-escalate pattern that prevents the doom loops cheaper bots fall into. Every conversation carries a per-conversation audit trail with timestamps, source attribution, and decision rationale.

The regulated-industry proof is concrete. A card-issuing fintech adopted Lorikeet and its audit trail passed regulatory review with the company's neobank partners, which is the bar that matters when a regulator asks you to reconstruct a decision. A global eSIM and telco brand resolves roughly 68% of 7,500 to 9,000 daily tickets on the platform. A remittance fintech found that customers whose cases were handled by the AI were 11 percentage points more likely to remain active at 30 days than those handled by humans, which suggests resolution quality, not only deflection. In a head-to-head evaluation, one fintech chose Lorikeet over other leading AI vendors specifically on provable pre-go-live compliance guardrails.

Key features:

• Case execution with read and write access to core systems, not knowledge-base lookups.

• Step-level determinism: deterministic kernels for auth, refunds, and money movement inside a conversational shell.

• Dual-agent model: Concierge for resolution, Coach for always-on QA against SOPs and policy.

• Dual-sided runtime guardrails on every customer message and every AI response, with alert/steer/escalate/add-action corrections.

• Per-conversation audit trail with timestamps, source attribution, and decision rationale.

• Assertion-based simulations for pre-go-live scenario testing, scored on the same framework as live tickets.

• Channels: chat, email, SMS, and production voice, plus proactive and outbound.

• Compliance: SOC 2 Type II, ISO 27001, HIPAA, and GDPR, with AU data residency at the infrastructure layer.

Honest gaps: Lorikeet orchestrates third-party and open-weight models with automatic failover rather than running a proprietary house model, so model behavior depends on those providers. While AU data residency holds at the infrastructure layer, AI inference still relies on US LLM providers. Clinical and medical topics carry a hard ceiling and always require human oversight, and production guidance is to take final consequential actions inside structured sub-workflows rather than free-text steps.

Pricing: Custom and outcome-based, with deployments commonly starting around $60K per year rather than the $500K-plus entry points common at the enterprise end of this market.

G2 rating: No public reviews yet.

2. Sierra

Best for: Large enterprises that want a heavily vendor-built agent and have the budget and timeline for a services-led engagement.

Sierra is an enterprise-grade conversational AI platform that builds custom agents for each customer, typically through a vendor-led engagement using its TypeScript-based agent SDK. It is a capable, premium product, and it markets customer-specific resolution rates in the 70% to 90% range, though those figures are not independently benchmarked. For regulated buyers, the main considerations are time and control: deployments commonly run three to seven months and lean on Sierra's services team, which slows iteration and can put the deep configuration logic a step away from your own team. Sierra holds SOC 2, but does not foreground the HIPAA posture that healthtech buyers require, and it has no native helpdesk.

Key features:

• Custom agents built per customer via a TypeScript agent SDK.

• Outcome-based pricing aligned to resolutions.

• Voice, chat, and email channels.

• Supervisory and guardrail tooling for agent behavior.

• Enterprise security with SOC 2.

Pricing: Custom and outcome-based, commonly estimated at $150K-plus per year. G2 rating: Limited public reviews.

3. Decagon

Best for: High-volume consumer brands that want concierge-style conversational automation and are not bound by HIPAA.

Decagon is an AI agent platform aimed at consumer-scale support, and it markets a concierge experience. It handles conversational interaction well and prices on a $50K-plus platform fee plus a per-conversation or per-resolution charge. For regulated buyers, two facts are decisive. First, Decagon is not HIPAA compliant, which has been an explicit deciding factor against it in healthcare evaluations, so it is effectively off the table for healthtech. Second, its architecture has been reported to struggle with multi-party coordination, which is exactly the kind of complex, multi-step case that regulated resolution depends on. Voice support is limited. It holds SOC 2.

Key features:

• Concierge-style conversational agents for high-volume consumer support.

• Per-conversation or per-resolution pricing on top of a platform fee.

• Analytics and monitoring tooling.

• Chat and email, with limited voice.

• SOC 2 security.

Pricing: $50K+ annual platform fee plus per-conversation or per-resolution charges. G2 rating: Limited public reviews. Note: Not HIPAA compliant.

4. Ada

Best for: Mid-market and enterprise teams that want broad, low-code automation across many channels.

Ada is a well-established automation platform with a low-code builder and a services layer, and it markets resolution rates of 70% to 80%-plus. It covers chat, email, voice, and social, and carries a strong compliance set including SOC 2, HIPAA, GDPR, the AIUC-1 AI standard, and zero data retention, which makes it a credible option for some regulated workloads. The trade-offs for deep regulated resolution are pricing structure and depth: Ada prices per conversation rather than per resolution, has no native helpdesk, and its low-code model is built for breadth of automation more than for the deterministic, audit-first execution of the hardest money-movement cases.

Key features:

• Low-code automation builder with a services layer.

• Chat, email, voice, and social channels.

• SOC 2, HIPAA, GDPR, AIUC-1, and zero data retention.

• Reasoning and action capabilities across integrations.

• Per-conversation usage-based pricing.

Pricing: Custom usage-based, estimated $30K to $300K-plus per year. G2 rating: Established presence with strong reviews.

5. Kore.ai

Best for: Banks and telcos with developer resources and large voice/IVR programs.

Kore.ai is an enterprise conversational AI platform with deep voice and IVR strengths and support for 100-plus languages, and it offers on-premise and private-cloud deployment that appeals to banks with strict data-control requirements. It holds SOC 2, ISO 27001, and GDPR. The trade-off is effort and orientation. Kore.ai is developer-heavy, deployments run for months, and pricing is high and structured around 15-minute sessions plus per-seat charges, which is estimated at $300K-plus per year. It has no native helpdesk and is built more for conversational automation and routing than for the deterministic, write-action resolution of complex financial cases out of the box.

Key features:

• Strong voice and IVR automation, 100+ languages.

• On-premise and private-cloud deployment options.

• SOC 2, ISO 27001, and GDPR.

• Extensive developer tooling and integrations.

• Session-based plus per-seat pricing.

Pricing: Custom, estimated $300K-plus per year. G2 rating: Established enterprise presence.

6. Cognigy

Best for: Contact centers automating voice on top of an existing CCaaS stack.

Cognigy, now part of NiCE, is a conversational automation platform whose core strength is voice and IVR across 100-plus languages, typically deployed as an overlay on an existing contact-center stack. It holds SOC 2, ISO 27001, and GDPR, with on-premise and private-cloud options. For regulated end-to-end resolution, the limitations are similar to other CCaaS-oriented platforms: it has no native helpdesk, deployments run for months, and its design center is voice automation and routing rather than the deterministic, auditable execution of complex backend financial and account actions.

Key features:

• Voice and IVR automation, 100+ languages.

• Overlay on existing contact-center platforms.

• On-premise and private-cloud deployment.

• SOC 2, ISO 27001, and GDPR.

• Low-code conversational flow builder.

Pricing: Custom, estimated $150K-plus per year. G2 rating: Established presence.

7. Salesforce Agentforce

Best for: Organizations already standardized on Salesforce Service Cloud with Data Cloud deployed.

Agentforce is Salesforce's agentic layer for Service Cloud. For teams already on the Salesforce stack it offers a native path to AI resolution, with the data, workflows, and helpdesk in one place. The practical prerequisites matter for regulated buyers: Agentforce effectively requires Data Cloud, and getting clean, well-governed data into it is a project in itself. Pricing runs around $2 per conversation or, under the Flex Credits model, about $0.10 per action, which can become unpredictable at volume. The platform is strong on integration within the Salesforce ecosystem, but the determinism and audit depth for the hardest regulated actions depend heavily on how carefully you build and govern the underlying flows.

Key features:

• Native to Salesforce Service Cloud, with a built-in helpdesk.

• Action execution via Flows and the Salesforce data model.

• Chat, email, and voice through Service Cloud.

• Enterprise security and governance via the Salesforce platform.

• Requires Data Cloud for grounding and context.

Pricing: ~$2 per conversation, or Flex Credits at ~$0.10 per action; Data Cloud required. G2 rating: Tied to the broader Salesforce ecosystem.

8. Boost.ai

Best for: Banking and telecom teams focused on self-service automation in those verticals.

Boost.ai is a conversational AI platform with a clear focus on banking and telecom self-service, and it has real traction in those regulated verticals. It handles chat and voice and is built around containment of high-volume self-service queries. For deep end-to-end resolution, the considerations are coverage and depth: Boost.ai has no native helpdesk, and its design center is conversational self-service and deflection of routine queries more than the deterministic, write-action resolution of the most complex, multi-step financial cases. It is a reasonable shortlist entry for a bank prioritizing routine self-service, with regulated resolution depth to verify against your own hardest cases.

Key features:

• Banking and telecom self-service focus.

• Chat and voice channels.

• Conversational automation with containment metrics.

• Enterprise security posture.

• Integrations with core banking and telecom systems.

Pricing: Custom. G2 rating: Established presence in its verticals.

How to choose an AI platform for regulated end-to-end resolution

Test resolution depth on your hardest cases, not your easiest. Any platform can answer a balance question. The evaluation that matters is whether it can complete a multi-step case that touches your systems of record, such as a disputed transaction, a fee reversal, or a policy change. Ask each vendor to run your real hard-20% cases in a sandbox and measure how many close without a human. A high containment rate on simple traffic tells you almost nothing about regulated resolution.

Demand determinism for consequential actions. For auth, refunds, and money movement, you need repeatable behavior, not a probabilistic best guess. Ask how the platform guarantees that the same inputs produce the same action, and whether it can run a step deterministically while keeping the rest of the conversation flexible. If the answer is that a single free-text model decides the final action, that is a risk to flag with your compliance team.

Inspect the audit trail before you buy. Open an actual conversation record and check whether you can reconstruct what the AI saw, which policy it applied, why it acted, and what it changed, all timestamped. A platform that only logs the transcript is not enough for a regulatory review. The audit trail is the artifact your risk and audit teams will live with.

Match certifications to your obligations. SOC 2 Type II is a baseline. Healthtech needs HIPAA, and on that point Decagon is not HIPAA compliant, which removes it from healthcare shortlists. Check data residency and retention windows against your regulatory regime, since some platforms' retention defaults fall short of multi-year HIPAA, BSA, or FINRA requirements.

Weigh total cost of ownership and time to controlled rollout. Look past the headline price to the services cost, the data prerequisites (Agentforce's Data Cloud dependency is a common surprise), and how long it takes to reach a monitored production rollout. A platform that is cheaper per resolution but takes seven months of vendor-led work to deploy may cost more in practice than one with a higher unit price and a faster, more controllable path to production.

Feature matrix: how the 8 platforms compare

Two honest notes on this matrix. First, no platform here ships a proprietary house model that out-performs the frontier labs; the meaningful differences are in architecture, determinism, and audit, not in a secret model. Second, Lorikeet's standalone subscriber-admin guardrail audit view is still being built out, so today its guardrail activity is surfaced through the conversation timeline rather than a separate dashboard. The per-conversation audit trail itself is real and in production.

Why Lorikeet wins for regulated end-to-end resolution

For fintech, healthtech, and insurance, the decision comes down to three questions: does it actually resolve the hard cases, can you trust each consequential action to be repeatable, and can you prove what happened to a regulator. Lorikeet is built around all three. It resolves the hard 20% by executing real cases against core systems instead of deflecting them to a queue. It makes the consequential steps deterministic through dedicated kernels, so auth, refunds, and money movement behave the same way every time and can be tested before go-live. And it records a per-conversation audit trail with decision rationale, which is the artifact a regulatory review actually needs.

The proof is in regulated production. A card-issuing fintech's audit trail passed regulatory review with its neobank partners. A global eSIM and telco brand resolves roughly 68% of 7,500 to 9,000 tickets a day. A remittance fintech saw AI-handled customers 11 percentage points more likely to remain active at 30 days, a sign that the AI was resolving cases well, not only closing them. And in a head-to-head evaluation, one fintech selected Lorikeet over other leading AI vendors on provable pre-go-live compliance guardrails. Pricing starts around $60K per year, well below the typical enterprise entry point, with a controllable, monitored path to production.

If you are comparing options directly, see Lorikeet vs Sierra and Lorikeet vs Decagon. For the deeper thinking behind this approach, read why resolution beats deflection, how to safely let AI take actions in backend systems, our guide to AI customer support for fintech in 2026, and AI support for insurance in 2026. When you are ready to see it on your own hard cases, book a demo.