End-to-end resolution in a regulated industry means the AI finishes the case inside your systems, under your policy, and leaves a record a reviewer can replay months later. Twelve platforms are compared here on the four criteria regulated buyers rank first: cost against current spend, accuracy and hallucination control, security and compliance, and data residency.
Last updated: August 19, 2026
Twelve platforms are covered: Lorikeet, Sierra, Decagon, Gradient Labs, Forethought, Ada, Intercom Fin, Zendesk AI, Salesforce Agentforce, Glia, Posh and Eltropy, in three groups rarely compared on one page: AI-native resolution platforms, financial-institution specialists and suite-native AI layers.
Glia publicly documents PCI DSS and more than 700 financial-institution deployments, Posh over 200 plus a US credit-union focus, and Eltropy positions itself as credit-union-native. If your buying committee sits inside a bank or credit union, those three belong on the shortlist before any general-purpose AI vendor.
Lorikeet holds SOC 2 Type II and ISO 27001, offers a standard-form DPA including a HIPAA business associate agreement from its Scale plan up, and is GDPR-aligned. It does not hold PCI DSS, and its published proof sits in fintech and healthtech rather than banks or credit unions.
Pricing units are not comparable. Intercom publishes $0.99 per resolution for Fin, Salesforce about $2 per conversation or $0.10 per action under Flex Credits, and Lorikeet publishes plans with per-resolution credit costs, charging only for resolved tickets. Seven of the twelve do not publish rates at all.
Pre-launch guardrail testing is the least-published dimension in the category. Sierra publicly documents simulation-based agent testing and Lorikeet lists unlimited testing, simulations and evaluations on every plan. The rest publish nothing, which is a question to put to them rather than a conclusion to draw.
Most comparisons of this category try to crown a winner, which is wrong for a regulated purchase. A credit union buying a member-facing voice assistant and a healthtech company buying clinical-adjacent chat support are not shopping in the same market. This page routes a buyer to the right group first, and no platform here is named the overall best.
What end-to-end resolution means when the case is regulated
End-to-end resolution means the AI completes the customer's case rather than acknowledging it: it authenticates the customer, gathers what the policy requires, reads from and writes to the systems of record, applies the governing rule, checks itself before acting, and records what it did and why. Deflection, by contrast, counts a case as handled once the customer stops asking, and containment rates can look excellent while every case that costs real money still lands on a human. Our guide to resolution rate versus deflection rate covers the distinction in depth.
In a regulated setting that gap is operational rather than semantic. The cases that matter carry a write action: a KYC document rejected for the third time, a failed transfer that needs tracing, a disputed-transaction intake, a fee reversal, a prescription eligibility question, a hardship arrangement. Each touches a system of record under a rule about who may act and with what evidence retained. A platform that cannot take the write action cannot resolve the case, whatever its containment number says.
Four capabilities separate the two: governed write access, deterministic execution for steps carrying legal or financial weight, branching multi-step workflows, and a per-case record of the decision and the action taken.
How to evaluate these platforms, in the order regulated buyers rank them
The ordering below reflects what regulated buyers raise first, not the order vendors present. Treat each criterion as a test to run rather than a box to tick.
1. Cost against what you spend today. This is the criterion that kills deals, and the comparison is against your current fully loaded cost per contact rather than another vendor's list price. Model it in your own units: last year's volume by channel, each vendor's unit and platform fee, then implementation, services and the internal engineering time expected from you. Ask what happens to the bill when volume doubles in a peak week, and when the AI escalates. Intercom, Salesforce and Lorikeet publish enough to model before a sales call, with Lorikeet's plans and credit costs on the pricing page.
2. Accuracy and hallucination control. In a regulated vertical this is a hard blocker rather than a quality preference. Vendor accuracy figures mean little without the denominator, so ask what population produced the number and what share required a write action. Then take fifty of your hardest cases and have each vendor run them in a sandbox against your policy. Count how many close correctly without a human, then how many close incorrectly, which is the number carrying the risk. Our guardrails and transparency comparison sets out what to inspect.
3. Security and compliance posture. Map your obligations first, then check each vendor's published position: SOC 2 Type II, ISO 27001, HIPAA with a signed business associate agreement, GDPR, PCI DSS, and the sector rules that apply to you, such as FINRA, Reg E, NYDFS Part 500, FCA rules in the UK or APRA CPS 234 in Australia. Ask for the trust centre and subprocessor list, and get the training-on-your-data answer into the contract.
4. Data residency. The most repeated hard constraint in European and Australian evaluations, and the one most often answered imprecisely. Storage residency and inference residency are different things: a vendor may store data in an EU or Australian region while the model call is served from the United States. Ask which one is meant and whether voice changes the answer. Lorikeet publishes the split: standard USA geography with zero-data-retention inference on Start and Scale, and geography-specific storage and inference on Enterprise.
5. Audit-trail depth and pre-launch testing. Open a real case record rather than a screenshot: you should be able to reconstruct what the AI saw, which policy it applied, why it acted, what tool calls it made and what changed, all timestamped. Then ask whether your compliance lead can write a rule in plain language, run it against a scenario suite, and read the pass and fail report before anything reaches a customer.
The platforms at a glance
This table covers the ten platforms most often on regulated shortlists; Ada and Forethought are profiled below instead, as broad automation and agent-assist platforms. Every entry comes from each vendor's own public material as of August 2026, so treat it as directional and confirm it in writing during diligence.
Best for | Pricing model | Published compliance | Pre-launch guardrail testing | |
|---|---|---|---|---|
Lorikeet | Fintech, healthtech and insurance, on an existing helpdesk | Published plans, per-resolution credits, resolved tickets only | SOC 2 Type II, ISO 27001, HIPAA BAA from Scale, GDPR. No PCI DSS | Unlimited testing, simulations and evaluations, every plan |
Sierra | Large multi-vertical enterprises wanting a vendor-built agent | Outcome-based, rates not published | SOC 2. HIPAA not foregrounded | Publicly documents simulation-based agent testing |
Decagon | High-volume consumer brands, concierge-style automation | Not published | SOC 2. Confirm HIPAA directly | Not published |
Gradient Labs | UK and European financial services, regulated-first | Not published | Positions around UK and EU financial-services rules | Not published |
Glia | Banks and credit unions consolidating voice and digital | Not published | Publicly documents PCI DSS and financial-services controls | Not published |
Posh | US credit unions and community banks, banking-only vendor | Not published | Positions around US financial-institution rules | Not published |
Eltropy | Credit unions unifying member communications | Not published | Positions around credit-union and community-bank rules | Not published |
Intercom Fin | Teams on Intercom or Zendesk wanting published pricing | $0.99 per resolution, published | SOC 2 Type II, GDPR, HIPAA on higher tiers | Not published |
Zendesk AI | Teams standardised on Zendesk wanting the native layer | Per automated resolution, plus seat licensing | Broad enterprise set including SOC 2 and HIPAA options | Not published |
Salesforce Agentforce | Service Cloud shops with Data Cloud deployed | About $2 per conversation, or $0.10 per action | Salesforce platform certification set | Not published |
The pattern worth noticing in the last column is that almost nothing is published. Treat "not published" as an unanswered question rather than a missing capability, and make each vendor answer it in front of your compliance lead.
AI-native resolution platforms
Lorikeet
Lorikeet is an AI support platform for complex and regulated businesses that runs on top of your existing helpdesk rather than replacing it, with published integrations including Zendesk, Intercom, HubSpot, Front and Salesforce. It covers chat, email, SMS and voice, with voice live in the US, UK and Australia at roughly 1.3 seconds median latency, detailed on the voice product page. Workflows can be natural-language or deterministic structured graphs, and the two mix inside one conversation.
Where it wins: provability before launch and after. Simulations run the bad paths against a scenario suite before anything reaches a customer, runtime guardrails check inbound and outbound messages, and a QA layer scores tickets against your own procedures. Testing, simulations and evaluations are unlimited on every published plan, which few vendors here match. Published proof sits in fintech and healthtech: Carmoola resolves 60% of its support end to end, with 90% of outbound conversations completing end to end and a 60% lift in conversion. Eucalyptus and easykind are published healthtech customers.
Honest limitations: no PCI DSS, which rules Lorikeet out where a card-data certification is a gate, and no published bank or credit-union reference. Geography-specific storage and inference are Enterprise features. There is no on-premise option, no browser agent for legacy interfaces without an API, and less agent-assist tooling than platforms built around helping human agents.
Sierra
Sierra builds custom agents per customer through a vendor-led engagement using its own agent development kit, and prices on outcomes.
Where it wins: breadth and rigour. Sierra publicly documents simulation-based testing of agent behaviour, which few vendors here do, and its track record spans many verticals.
Honest limitations: deployments run in months rather than weeks and lean on Sierra's services team, putting a step between your team and the configuration. Sierra publishes SOC 2 and does not foreground HIPAA as of August 2026, and rates are not published. Our Sierra alternatives guide and the three-way Sierra, Decagon and Lorikeet comparison cover the trade-offs.
Decagon
Decagon builds concierge-style AI agents for consumer-scale support and markets agent operating procedures as the way behaviour is specified.
Where it wins: consumer experience at scale. For brands with large volume and mostly self-contained cases, its interaction design is a real advantage over products that read as automation.
Honest limitations: Decagon publishes SOC 2 and, as of August 2026, does not foreground HIPAA publicly, so healthtech buyers should confirm directly. Voice is less mature than the chat product and rates are not published. A head-to-head read sits in our Decagon versus Sierra comparison for regulated teams.
Gradient Labs
Gradient Labs is a UK company building an AI support agent aimed at financial services, positioned around operating inside a regulated environment rather than retrofitting compliance onto a general product.
Where it wins: regulatory framing native to the UK and EU market, and a product conversation that starts from what a compliance function needs.
Honest limitations: its public footprint is smaller, and as of August 2026 it publishes neither pricing nor a certification list at the depth larger vendors do. Buyers outside the UK and EU should check regional coverage first.
Forethought
Forethought spans automated resolution, ticket triage and assistance for human agents, and has been in this market longer than most of the group. Where it wins: agent assistance and triage, since much of the saving comes from making existing agents faster. Honest limitations: the design centre is assisting and routing as much as resolving autonomously, so deterministic execution on money-movement steps needs testing rather than assuming, and certification detail and pricing are not published at the level a regulated buyer needs. Best for: large human teams where assistance matters as much as autonomy.
Financial-institution specialists
Glia, Posh and Eltropy sell almost exclusively to banks and credit unions, arriving fluent in core-banking integration, member communications and financial-institution procurement. Each is described here from its own public material only. Our guide to AI customer support for US banks and credit unions goes deeper on the segment.
Glia
Glia publicly documents PCI DSS and more than 700 financial-institution deployments, and sells a unified contact-centre architecture that carries a member across digital and voice channels without restarting the interaction.
Where it wins: depth in financial services and channel breadth from one vendor. A published PCI DSS position and hundreds of institutional references answer two procurement questions that stop most AI vendors at the first gate.
Honest limitations: Glia is a contact-centre platform rather than a resolution layer on an existing helpdesk, and does not position outside financial services. Pricing and testing practices are not published.
Posh
Posh publicly documents more than 200 financial-institution deployments and builds voice and digital assistants for US credit unions and community banks, on an engine marketed as REALM.
Where it wins: credit-union fluency. Posh speaks member-service vocabulary, core-banking realities and the league and CUSO ecosystem in a way general vendors do not, and its references sit inside the institutions a buying committee will call.
Honest limitations: the same focus that makes it strong narrows it. Posh is a US financial-institution product, so buyers outside that segment sit away from its centre of gravity. Pricing and testing practices are not published.
Eltropy
Eltropy positions itself as credit-union-native, built around unified member communications spanning text, chat, voice, video and secure messaging with AI agents on top, reaching beyond the contact centre into lending, collections and branch.
Where it wins: communications breadth inside a credit union. Departments outside support get value from the same platform, which changes the internal business case.
Honest limitations: the centre of gravity is communications breadth rather than deep autonomous execution of complex cases, so a credit union whose priority is finishing hard cases without a human should test that specifically.
Suite-native AI layers
These four attach to a platform you already own. The system-of-record question and the AI-agent question are separate, and the suites are excellent at the first and variable at the second. Our roundup of best enterprise AI support platforms covers them at more length.
Intercom Fin
Fin is the most price-transparent product here at a published $0.99 per resolution, and it works across Intercom's own helpdesk as well as Zendesk.
Where it wins: predictable economics and speed. A published flat rate lets a finance team model the bill on day one, and compliance posture includes SOC 2 Type II and GDPR, with HIPAA on higher tiers.
Honest limitations: Fin is designed around answering and light action inside Intercom's model of a conversation. Deep multi-step execution against your own backend systems needs engineering, and the flat rate is less attractive when the resolutions are complex ones a simpler product would escalate.
Zendesk AI
Zendesk is the strongest ticketing system of record in this set, and treating its AI layer and its helpdesk as one product is the most common error buyers make.
Where it wins: the system of record, routing, reporting, the agent console and the integration ecosystem. For many support organisations the native AI layer is sufficient and cheapest.
Honest limitations: the layer is optimised to keep tickets out of the queue, and the ceiling shows on multi-step regulated work requiring reads and writes across several systems under a policy. That is a ceiling on one class of case rather than a failure of the product, and a resolution layer such as Lorikeet runs on top of Zendesk rather than replacing it.
Salesforce Agentforce
Agentforce is Salesforce's agentic layer for Service Cloud, published at about $2 per conversation or about $0.10 per action under Flex Credits. For an organisation already on Service Cloud it puts data, workflows and helpdesk in one governed place.
Where it wins: proximity to your data and existing governance. If Data Cloud is deployed and your service processes live in Flows, Agentforce inherits work you have already done.
Honest limitations: the Data Cloud prerequisite is a project in itself and the most common surprise in Agentforce evaluations, and per-action pricing is hard to forecast at volume. Determinism and audit depth for high-stakes actions depend on how your team governs the underlying Flows, making them an engineering outcome rather than a product guarantee.
Ada
Ada is an established automation platform with a low-code builder and broad coverage across chat, email, voice and social. Where it wins: breadth, plus a published compliance set including SOC 2, HIPAA and GDPR alongside a zero data retention position, which clears more gates than most general-purpose vendors. Honest limitations: the low-code model is built for breadth of automation rather than deterministic, audit-first execution of the hardest money-movement cases, and it prices per conversation rather than per resolution. Best for: mid-market teams automating many journeys across many channels.
Which platform fits which buyer
Start from the constraint that will actually block the deal, then work outward.
If PCI DSS certification is a procurement gate, Glia is the vendor in this set that publicly documents it, and Lorikeet is out. Do not spend cycles on vendors that cannot clear a gate you have no power to waive.
If your buying committee sits in a credit union or community bank, look at Posh, Eltropy and Glia first. References inside your own segment shorten diligence more than any capability claim, and all three publish them.
If HIPAA and a signed business associate agreement are mandatory, Lorikeet and Ada publish HIPAA positions; confirm every other vendor's status in writing before shortlisting.
If budget predictability is the constraint, favour published rates. Intercom Fin, Salesforce and Lorikeet can be modelled before a call. Custom quotes cannot.
If you are locked to a helpdesk you are keeping, let that decide the first round: Fin on Intercom, Zendesk AI on Zendesk, Agentforce on Service Cloud. If the native layer clears your hardest cases, that is the cheapest answer available; if it does not, a resolution layer that runs on top keeps the system of record intact.
If your compliance lead holds a veto over launch, weight pre-launch testing and audit depth above everything else, and ask each vendor to demonstrate both on your own cases. Sierra and Lorikeet are the two here that publish anything on the subject. If your residency rules cover inference and not only storage, raise that on the same call.
Questions to ask every vendor on this list
What was the ticket mix behind your published accuracy figure, and what share of those cases required a write action to a system of record?
Can my compliance lead write a rule in plain language, run it against a scenario suite, and read the pass and fail report before we go live?
Show me one real case record from last week end to end, including every tool call and the reasoning between them, and say how long it is retained.
Who decides what counts as a resolution for billing, and what happens to the bill when the AI escalates to a human?
After launch, can my team change the workflows without you, and what is the fallback when a core system errors mid-chain: retry, escalate, or roll back?
Lorikeet's take
Glia, Posh and Eltropy are strong companies with real depth in banking and credit unions. If you are a financial institution, they publish the segment references and, in Glia's case, the PCI DSS position Lorikeet does not have. Sierra and Decagon are capable AI-native platforms, and Zendesk remains the best system of record here.
Our view, built from working mostly with fintechs, healthtech companies and insurers, is that regulated support is won or lost on provability. The platforms that get approved are the ones whose behaviour a compliance team can test before launch and replay after, on the hard cases. That is what Lorikeet is built around.
If that is the bar your team uses, book a demo and bring your hardest ten cases. We will run them in your stack, against your guardrails, before you sign. If your gate is PCI DSS or a credit-union reference base, one of the other vendors here is the better call and we will say so.
Key takeaways
End-to-end resolution requires governed write access, deterministic execution on consequential steps, branching workflows and a replayable per-case record. A platform missing any of the four is deflecting.
Glia, Posh and Eltropy publish financial-institution reference bases no general-purpose AI vendor here matches, and Glia publicly documents PCI DSS, which Lorikeet does not.
Published pricing is rare. Intercom Fin at $0.99 per resolution, Salesforce at about $2 per conversation, and Lorikeet's plans and credit costs are the only figures you can model before a sales call.
Pre-launch guardrail testing and audit-trail depth are the least-published dimensions in the category, which makes them the most useful demo questions.
Conclusion
Choosing an AI platform for end-to-end resolution in a regulated industry is a question of fit rather than ranking. A credit union should start with the vendors whose entire business is credit unions, a healthtech company with HIPAA and a signed business associate agreement, and a team standardised on a suite with the native layer. Shortlist inside your group, then run every finalist on the cases that would genuinely hurt you to get wrong.
