Glia and Posh are both built specifically for banks and credit unions, and both publish financial-institution deployment histories that Lorikeet does not have. Lorikeet is a general AI support platform for complex, regulated work that runs on top of an existing helpdesk. The criterion that separates the three is whether your hardest member requests are answered from knowledge and routed to a person, or completed end to end across your core and your ancillary systems.
Everything stated here about Glia and Posh comes from their own public marketing and documentation as of August 2026, with sources linked. Nothing here is a first-hand assessment of their products.
Glia publicly documents over 700 financial institutions, a ChannelLess architecture spanning voice, chat, video and messaging, 100+ pre-built integrations to online banking platforms, cores and CRMs, and a contractual guarantee against AI hallucinations and prompt injections announced in March 2026.
Posh publicly documents over 200 AI deployments across banks and credit unions, named core integrations to Symitar, Fiserv, Corelation and Jack Henry, and SOC 2 Type II plus CSA STAR Level 1 attestations.
Glia publishes PCI DSS among its compliance statuses. Lorikeet does not hold PCI DSS, and Lorikeet has no published bank or credit union customer. Both statements are true and both matter to this decision.
Lorikeet is the only one of the three with public list pricing: $1,500 per month on Start and $4,000 per month on Scale, both paid annually, chat, email and SMS resolutions at $0.80 to $0.95, voice at $1.20 to $1.50, no per-seat charges, and only resolved tickets charged.
Lorikeet's separating claim is depth on multi-step, backend-touching resolution, supported by SOC 2 Type II, ISO 27001, HIPAA and GDPR, Google Cloud hosting, unlimited pre-launch guardrail testing on every plan, voice live in the US, UK and Australia at roughly 1.3 seconds median latency, and a forward-deployed engineering build model.
Last updated: August 2026
The usual version of this comparison asks which platform is best for credit unions and answers with whichever vendor has the most credit union logos. Logo count answers a real question, whether a vendor understands the vertical. It does not answer the second, whether the platform can complete the work your members ask for.
Glia, Posh and Lorikeet at a Glance
Glia sells a unified interaction platform to banks and credit unions. Its public positioning centres on ChannelLess architecture, which lets a member move between voice, chat, video and messaging inside one interaction. Glia publicly states that over 700 financial institutions use the platform and that it offers 100+ pre-built integrations to leading online banking platforms, cores and CRMs. On its security page it lists SOC 2 Type 2, PCI DSS and HIPAA/HITECH Type 1 among its maintained compliance statuses and states that it is hosted exclusively on Amazon Web Services. In March 2026 it announced a contractual guarantee against AI hallucinations and prompt injections, built on an approvals framework rather than on output filtering. No other vendor discussed here offers an equivalent commitment.
Posh sells an AI layer purpose-built for financial institutions, with a pronounced US credit union specialisation. Its public materials describe a voice assistant, a digital assistant, a website answer product, an employee knowledge assistant, a training simulator, a conversation QA product and an outreach agent, and it publicly documents over 200 AI deployments across banks and credit unions. Its voice product page names Symitar, Fiserv, Corelation and Jack Henry as core integrations and states that Posh Voice goes live in weeks against an industry norm of six to twelve months. Posh states that it adheres to SOC 2 Type II and CSA STAR Level 1, and has announced a partnership with the Cooperative Credit Union Association.
Lorikeet is an AI customer support platform for complex and regulated businesses. It runs as a resolution layer on top of an existing helpdesk rather than replacing it, with published integrations to Zendesk, Intercom, HubSpot, Front and Salesforce, and works across chat, email, SMS and voice. It holds SOC 2 Type II, ISO 27001, HIPAA and GDPR, is hosted on Google Cloud, and includes unlimited testing, simulations and evaluations on every plan. Workflows are built under a forward-deployed engineering model, where Lorikeet engineers build against your systems rather than handing you a builder. Lorikeet does not hold PCI DSS and has no published bank or credit union customer.
The honest summary: Glia and Posh have vertical depth, integration breadth and deployment history Lorikeet does not have. Lorikeet has depth on multi-step resolution, published pricing and a build model that puts vendor engineers on your workflows. For a wider view, see our guide to the best AI customer support platforms for US banks and credit unions.
What to Look For, in Priority Order
1. Cost measured against current spend. The number that matters is the change in total member-service cost, including contact-centre licences, telephony, overflow staffing and after-hours coverage. Ask every vendor to model twelve months at your actual volume with the peak included, and to name every charge trigger.
2. Accuracy and hallucination control. Ask how the vendor prevents a wrong answer rather than how it detects one afterwards. The strongest evidence is a pre-launch test suite you can read: adversarial conversations run against the configured agent, with a pass and fail report your risk team reviews.
3. Security and compliance, including PCI scope. Establish whether your deployment will touch cardholder data. If the agent will read a full card number, take a payment, or sit inside the cardholder data environment, PCI DSS is a gating question and you should shortlist only vendors that publish it. Have your compliance lead make that call before the shortlist is written.
4. Data residency and hosting. Ask where conversation data is stored, where inference runs, which cloud the vendor uses, whether inference is zero-retention, and whether a geo-specific option exists. For a US-only institution this is a short conversation; for anyone with cross-border members it can eliminate a vendor.
5. Core banking and channel integration. Ask for the named list of cores, online banking platforms and contact-centre systems the vendor runs in production. Named integrations shorten deployment; a generic API story means your integration is a project. See our guide to AI support with core banking integration.
6. What the agent completes without a person. Take your ten highest-volume member requests, write down every system that has to be read from and written to, then ask which the agent finishes on its own in production today. Containment describes how many conversations ended, not how many problems were solved.
Glia vs Posh vs Lorikeet: Side by Side
Where a vendor does not publish a figure the cell reads "Not published", a statement about disclosure rather than about capability. Treat each as an unanswered question for the vendor.
Glia | Posh | Lorikeet | |
|---|---|---|---|
Built for | Banks and credit unions; voice, chat, video, messaging | Banks and credit unions, deep US credit union focus | Complex and regulated businesses; chat, email, SMS, voice |
Published pricing | Not published | Not published | $1,500/mo Start, $4,000/mo Scale, per-resolution credits |
Published FI footprint | 700+ financial institutions | 200+ AI deployments at banks and credit unions | None published |
PCI DSS | Published as maintained | Not published | Not held |
Other certifications | SOC 2 Type 2, HIPAA/HITECH Type 1, CCPA | SOC 2 Type II, CSA STAR Level 1 | SOC 2 Type II, ISO 27001, HIPAA, GDPR |
Hosting and residency | AWS, stated exclusively; regions not published | Not published | Google Cloud; zero-retention inference, geo-specific option on Enterprise |
Named core integrations | 100+ pre-built to banking platforms, cores and CRMs | Symitar, Fiserv, Corelation, Jack Henry | Not published; helpdesk layer over Zendesk, Salesforce, Intercom, Front |
Pre-launch testing published | Contractual guarantee against hallucinations and prompt injections | REALM orchestration described as traceable and audit-ready | Unlimited simulations, evaluations and guardrail tests on every plan |
Treat published figures as directional; deployment counts and containment rates are self-reported.
Cost Against Current Spend
Member-service economics rarely look like SaaS economics: the largest line is people and the second largest is the contact-centre stack those people sit in.
How Glia fits: Glia does not publish list pricing. Its commercial story is consolidation, replacing a separate contact-centre stack, digital channels and AI tooling with one system, so the comparison to run is against the contracts you would retire at renewal.
How Posh fits: Posh does not publish list pricing either. It publishes customer-reported outcomes instead, including annual savings figures at named credit unions and a 95% containment rate at one bank. Those are the vendor's own claims and worth interrogating: ask which contact types were in scope and what counted as contained.
How Lorikeet fits: Lorikeet publishes a full price list. Start is $1,500 per month paid annually with 18,000 credits a year; Scale is $4,000 per month with 48,000 credits. A chat, email or SMS resolution costs 0.95 credits on Start and 0.80 on Scale; a voice resolution up to three minutes costs 1.50 and 1.20. There are no per-seat charges, implementation is included on both plans, and only successfully resolved tickets are charged, with the customer defining what counts as resolved.
What to ask all three: give each vendor the same twelve-month volume file, ask for a single annual number with every charge trigger named, then ask what happens to the bill when the agent handles a conversation and the member calls back anyway.
Accuracy and Hallucination Control
In member servicing a wrong answer can be a Regulation E dispute handled incorrectly, a fee reversal promised that policy does not allow, or a disclosure that was never approved. Every vendor here claims accuracy; the useful distinction is what evidence they offer for it.
How Glia fits: Glia holds the strongest public commercial commitment of the three. Its March 2026 announcement describes a contractual guarantee against hallucinations and prompt injections, and states that the platform uses AI to understand intent while never using that same AI to improvise answers in real time. A guarantee written into the contract is a form of evidence that a benchmark slide is not.
How Posh fits: Posh describes an orchestration layer it calls REALM and states that all responses follow the institution's guidelines and are traceable, controllable and audit-ready. It also publishes a QA product that evaluates 100% of conversations. Ask for a sample of that traceability output.
How Lorikeet fits: Lorikeet's published mechanism is pre-launch testing. Every plan includes unlimited simulations and evaluations, and guardrails are run against adversarial and edge-case scenarios before an agent goes live, producing a pass and fail report a compliance team can read. Lorikeet does not offer a contractual hallucination guarantee. Our comparison of transparency and guardrail approaches sets out what a testable guardrail contains.
What to ask all three: ask for the artefact your risk team reads before approving launch, and for the contractual remedy when the agent is wrong.
Security, Compliance, PCI Scope and Data Residency
PCI scope is the one dimension here where the answer is binary for some institutions, and where Lorikeet is straightforwardly the weaker option.
How Glia fits: Glia lists PCI DSS on its security page alongside SOC 2 Type 2, HIPAA/HITECH Type 1 and CCPA, and states that it is hosted exclusively on AWS. For an institution whose AI use cases sit inside or adjacent to the cardholder data environment, that published PCI DSS status makes Glia eligible for conversations the other two vendors here cannot enter without a scope carve-out. Specific residency options are not published.
How Posh fits: Posh publicly states SOC 2 Type II and CSA STAR Level 1, and does not publish PCI DSS, hosting or residency detail. CSA STAR Level 1 is a cloud-security self-assessment against the Cloud Controls Matrix, a reasonable addition to SOC 2 in a vendor-management file. If cardholder data is in scope, ask Posh directly rather than inferring a position from the absence of a badge.
How Lorikeet fits: Lorikeet holds SOC 2 Type II, ISO 27001, HIPAA and GDPR. Lorikeet does not hold PCI DSS. That is a real gap for any deployment placing the agent inside the cardholder data environment, and if your evaluation is gated on PCI DSS, Lorikeet should come off the shortlist at that gate. On residency, Lorikeet is hosted on Google Cloud with standard USA geography and zero-data-retention inference on Start and Scale, and geo-specific storage and inference on Enterprise. There is no on-premise option and no published Canadian region.
What to ask all three: have your compliance lead decide, before the shortlist, whether the agent will touch cardholder data in any target use case, then get storage location, inference location and retention answered in the DPA.
Core Banking Integration and Channel Breadth
The gap between a vendor already integrated with your core and one that is not is measured in months of deployment time.
How Glia fits: Glia publicly claims 100+ pre-built integrations to leading online banking platforms, cores and CRMs, and its ChannelLess architecture carries a member across voice, chat, video and messaging inside one interaction. For an institution that wants one system covering the whole member communication surface, that breadth is the product.
How Posh fits: Posh names Symitar, Fiserv, Corelation and Jack Henry as core integrations and NICE CXone, Genesys Cloud, RingCentral and Five9 as contact-centre integrations. That is a precise, checkable list against the systems most US credit unions run, explaining the deployment-speed claim on its voice page.
How Lorikeet fits: Lorikeet does not publish a list of core banking integrations. It publishes helpdesk integrations to Zendesk, Intercom, HubSpot, Front and Salesforce, and connects to backend systems through APIs built during implementation by Lorikeet engineers. Where member-facing work runs through a modern helpdesk and internal APIs that model works well; where the entire service surface is a core and a legacy contact-centre platform, the named-integration vendors start ahead. Our guide to voice AI as an IVR replacement for banks covers telephony.
What to ask all three: name your core, online banking platform and contact-centre system, ask for a production reference on each, then ask what an unlisted integration costs in weeks and dollars.
Multi-Step Resolution Across Backend Systems
This is the dimension Lorikeet was built for and where the three vendors are least alike: the distinction between answering a member's question correctly and finishing the member's task.
How Glia fits: Glia markets Glia Agentic Workflows alongside its assistant products and states that its AI can automate up to 80% of interactions and handle 1000+ banking inquiries out of the box. That library is a genuine time-to-value advantage. The question to put to it is what proportion of those inquiries end in a system write rather than a correct answer.
How Posh fits: Posh's voice page is specific about transactional capability, listing balance checks, loan payoffs, transfers, stop payments, card controls, travel notices and profile updates with MFA, OTP and PIN-based authentication. That detail exceeds what most vendors in this category publish. The open question is how far past that list the agent goes when a request needs conditional logic across several systems.
How Lorikeet fits: Lorikeet's design point is the long tail: requests that require reading state from several systems, applying policy, taking an action and confirming it. Workflows are built by Lorikeet engineers against your APIs, tested in simulation before launch, and every decision is recorded as a replayable trail of tool calls and reasoning steps. Lorikeet has published this capability in fintech, healthtech and remittance contexts and has not published a bank or credit union deployment, so an institution evaluating it is buying capability rather than a peer reference. Our analysis of AI platforms that resolve end to end in regulated industries sets out how to test the claim.
What to ask all three: pick one request that takes an agent four minutes and three screens today, ask each vendor to complete it end to end in a sandbox including the write-back, then ask to see the record of what the agent did.
When Glia Is the Right Fit
Start from the constraint that will block the deal rather than from the vendor. If PCI DSS scope is the constraint, Glia is the only one of these three that publishes it, and that decides the shortlist before anything else is discussed. Glia's model is also a platform your own team operates, backed by a partner ecosystem and a large installed base of similar institutions on the same cores. If breadth of member communication is the constraint, meaning voice, chat, video banking and messaging on one platform with context carried across them, that is the core of Glia's product and the largest deployed footprint of the three. If your risk committee wants a contractual commitment on AI behaviour rather than a benchmark, Glia's March 2026 guarantee is the strongest public position in the category.
When Posh Is the Right Fit
Choose Posh if you are a US credit union or community bank running Symitar, Fiserv, Corelation or Jack Henry, you want named production integrations to your exact stack, and you want a deployment measured in weeks. Its specialisation is narrow by design and that narrowness is the value: the product vocabulary, the member journeys and the integration list are already shaped like a credit union. Its association partnership is aimed at smaller institutions without an internal automation team. Choose Posh as well if your problem extends into employee enablement, because the knowledge assistant, training simulator and conversation QA products cover ground the other two vendors do not sell.
When a Bank or Credit Union Should Consider Lorikeet
Consider Lorikeet in a narrower situation. If your hardest member requests need an agent to read state across several systems, apply a policy, take an action and confirm it, and your current AI stops at answering, that is the gap Lorikeet is built to close. If you run a modern helpdesk and want an AI resolution layer on top of it rather than a new system of record, that model removes the rip-and-replace question. If your compliance team wants a pass and fail report from adversarial testing against your own configuration before launch, that is published and testable.
Do not shortlist Lorikeet if PCI DSS is a gating requirement, because Lorikeet does not hold it. Do not shortlist it if a peer reference at a comparable institution is required to pass vendor management, because there is no published bank or credit union customer to point at. Do not shortlist it if video banking or a contact-centre replacement is what you are buying. If you are a digital-first lender or neobank rather than a chartered institution, our comparison of AI support platforms for neobanks and digital lenders is a closer match.
Questions to Ask All Three Vendors
Will the AI agent read, store or transmit cardholder data in any of our target use cases, and if so what is your PCI DSS position in writing?
Show us the pre-launch artefact our risk team reviews: the test scenarios, the configuration they ran against, and the pass and fail report.
Name a production customer on our core, our online banking platform and our contact-centre system, and let us speak to them.
Model twelve months at our real volume including our peak month, name every charge trigger, and tell us what happens to the bill when a handled conversation produces a call-back.
Walk us through adding one new workflow six months after launch: who writes it, who tests it, who approves it, and how many days it takes.
Lorikeet's Take
Glia and Posh are both credible companies with real depth in this market, and on the two dimensions that decide many credit union evaluations, published PCI DSS status and financial-institution deployment history, they are ahead of Lorikeet. Neither is a gap Lorikeet can talk its way past.
Lorikeet's view, built from working with fintechs, lenders, remittance businesses and healthcare companies, is that AI support in regulated markets is won on what the agent can finish and on what you can prove about how it behaved. That view is narrow and it is falsifiable. The test: take the ten member requests that would hurt you most to get wrong, bring them to every vendor on your shortlist including this one, and ask each to resolve them end to end against test data with the audit record produced afterwards. Honest limitation: Lorikeet does not hold PCI DSS, has no published bank or credit union customer, is not a contact-centre or ticketing system, and is built around a vendor-led build model rather than a self-service builder. If any of those is disqualifying for your institution, one of the other vendors on this page is the better answer.
Key Takeaways
Glia publishes PCI DSS, over 700 financial institutions, 100+ pre-built integrations and a contractual guarantee against hallucinations and prompt injections. It fits best when PCI scope or channel breadth is the constraint.
Posh publishes over 200 AI deployments, named core integrations to Symitar, Fiserv, Corelation and Jack Henry, and a weeks-long deployment claim. It fits best for a US credit union that wants a specialist on its exact stack.
Lorikeet does not hold PCI DSS and has no published bank or credit union customer. Both facts should be weighed before a shortlist is written.
Lorikeet's separating criterion is multi-step, backend-touching resolution with pre-launch guardrail testing, a replayable audit record, published per-resolution pricing and a forward-deployed build model where Lorikeet engineers own the workflows.
Order the evaluation by cost against current spend, accuracy evidence, compliance and PCI scope, then data residency. Those four eliminate vendors faster than any feature matrix.
There is no winner across these three, because they answer different questions. A credit union whose constraint is PCI scope, or whose ambition is one platform for every member channel, should look hardest at Glia. One that wants a specialist already integrated with its core and live in weeks should look hardest at Posh. An institution whose hardest member requests still require a person to touch four systems, and whose compliance team wants to read the test results before launch, is the situation Lorikeet is built for, with the PCI and reference gaps stated openly. For the full framework, read our guide to AI support for US banks and credit unions, then run the same ten hardest tickets against every vendor on your list.
