Gladly is one of the best platforms in the market at building a single, lifelong relationship with a consumer across every channel. The question this comparison answers is what happens on the ticket that needs five tool calls, a risk check, and a replayable audit trail. That is the line between the two products.
Gladly and Lorikeet both put AI in front of customer support, but they are built to win different jobs. Gladly is a people-centered platform: it organizes support around the customer rather than the ticket, keeps one continuous conversation across channels, and layers in an AI agent called Sidekick. Lorikeet is built for end-to-end resolution of complex, regulated, multi-step tickets across chat, email, voice, SMS, and WhatsApp, with guardrails you can prove before launch and audit trails you can replay after. This is a head-to-head on the dimensions that actually decide which one fits: how each handles the customer relationship versus the hard workflow, guardrails, channels, and pricing.
A unified customer relationship and end-to-end resolution of complex tickets are different jobs. Gladly is judged on how seamless and personal the consumer experience feels across channels; Lorikeet is judged on correctness on the hard, regulated tickets.
Gladly is strongest for B2C retail, hospitality, and consumer brands that value a single lifelong customer thread and human-plus-AI service. That is a real strength worth choosing for.
Lorikeet prices per resolution at about $0.80–$0.95 for chat, email, or SMS and about $1.20–$1.50 for voice, with escalations not charged and the customer holding veto over what counts as a resolution.
Gartner predicts 80% of common customer service issues will be resolved autonomously by 2029, up from low double digits in 2024.
For regulated buyers (fintech, healthtech, insurance), the deciding criteria are guardrails you can prove before launch and audit trails you can replay after, which is where Lorikeet is built to win.
Last updated: June 2026
Most teams comparing Gladly and Lorikeet are really asking one question: is my priority the customer relationship across every interaction, or autonomous correctness on the hardest tickets? If you sell to consumers and your support advantage is making each shopper feel known across chat, email, voice, and SMS as one continuous conversation, Gladly is a strong, mature choice built precisely for that. If your hardest tickets are KYC unlocks, payment disputes, transfers, claims, and policy-bound conversations that have to run in order and recover when a step fails, the question shifts from relationship continuity to resolution correctness, and the answer shifts toward Lorikeet. This comparison treats Gladly fairly because it is genuinely good at what it was built for. It then makes the honest case for where Lorikeet pulls ahead.
Gladly vs Lorikeet at a Glance
Gladly · Best for: B2C retail, hospitality, and consumer brands that want a people-centered platform with one lifelong customer conversation across channels · Core strength: Customer-centered model with no tickets, a single thread per person across chat, email, voice, and SMS, and a polished agent and consumer experience, with Sidekick adding AI automation · Channels: Chat, email, voice, SMS, and social, unified around the customer rather than the case · Pricing: Per-agent seat licensing, commonly cited at roughly $180 to $210 per agent per month with annual minimums, plus Sidekick AI conversations billed separately at around $0.60 each (Gladly does not publish a public price list, so confirm current terms directly).
Lorikeet · Best for: Complex, regulated, multi-step resolution with audit trails · Core strength: End-to-end action chains, deterministic plus natural-language workflows in one interaction, defence-in-depth guardrails, omnichannel including sub-1-second voice, 100% automated QA · Channels: Chat, email, voice, SMS, WhatsApp, plus outbound re-engagement · Pricing: ~$0.80–$0.95 per chat/email/SMS resolution, ~$1.20–$1.50 per voice; escalations not charged; customer defines what counts as a resolution.
Lorikeet is an AI customer support platform built for complex and regulated businesses such as fintech, financial services, healthtech, and insurance. It builds AI concierges that resolve multi-step tickets end-to-end and verifies behavior through pre-launch simulations and 100% post-resolution QA. Around 80% of Lorikeet customers are US financial institutions and fintechs.
A Customer Relationship Platform vs a Resolution Engine
The clearest way to understand the two products is to look at what each was built to optimize. Gladly is a customer relationship platform. Its defining idea is that support should be organized around the person, not the ticket: every interaction a customer has, across any channel and across years, lives in one continuous conversation, so an agent or the AI always has the full history in view. That model is excellent for consumer brands where loyalty and a personal feel drive revenue, and Gladly does it well. Sidekick, its AI agent, automates conversations and assists human agents inside that experience.
Lorikeet is a resolution engine. It is judged on the hard 20% of tickets, the ones a single answer or a friendly conversation cannot finish. "Where is my order" handled warmly with full history is the kind of interaction Gladly is built for. "Verify my identity, find out why my transfer failed, refund the fee, and update my address" is a complex workflow: several tool calls in the right order, decisions between them, state held across the conversation, and a recovery path when a step errors. Lorikeet chains those actions end-to-end, looking up the transaction, running a risk or KYC check, updating the system of record, drafting the message, and escalating only when a guardrail blocks it. A Team of Agents can dispatch sub-agents to coordinate with third parties, such as calling a merchant on a dispute or a pharmacy on a prescription issue.
The honest stress test that separates the two: ask what happens when a payment processor or core banking API returns a 5xx halfway through a chain. A relationship-first platform keeps the conversation warm and hands off to a human. A resolution agent recovers, retries, or routes the workflow safely while keeping the rest of the context intact. Both behaviors are legitimate; they just answer different problems.
What Counts as a Complex Workflow?
Because the two products are scored on different jobs, it helps to define the boundary precisely. A complex workflow is a support interaction the AI cannot finish with a single answer or a single empathetic reply. It requires multiple tool calls, decisions between them, state held across the conversation, and a recovery path when something errors. The category of AI support tools splits cleanly around this distinction.
Action chain: A sequence of tool calls the agent executes to resolve a ticket end-to-end, for example verify identity, check balance, update the CRM, file a dispute, send a confirmation, as opposed to a single retrieval-and-reply.
Audit trail: A timestamped, replayable record of every tool call, prompt, and reasoning step the agent took on a ticket, used by compliance and QA teams to review exactly what happened.
Relationship-centered tools keep the customer's full history in one place and make every reply feel personal, which is the job Gladly does well. Complex-workflow agents take actions and have to sequence several of them reliably, then produce a record you can audit afterward. The hard part is not any single action; it is the ordering, the recovery, and the proof.
Workflows: Deterministic and Natural-Language Together
Gladly's automation is organized around Sidekick and reusable conversation flows, sometimes called Threads, that respond to common situations and trigger steps inside the customer conversation. For the consumer-service end of support, where the priority is a warm, consistent, on-brand reply with the customer's history in view, that model fits well and is straightforward to operate.
Lorikeet is built so the steps that must happen exactly the same way every time and the steps that need judgment can live in one interaction. It combines deterministic structured workflows, where a regulatory disclosure or a value-threshold check cannot vary, with natural-language workflows, where the situation is ambiguous and reasoning is needed. Both are configured in plain English, so non-engineers can build and edit them. Pure decision trees break on edge cases; pure natural-language reasoning is hard to constrain on the steps that must be exact. Complex, regulated work needs both, and combining them in a single workflow is the capability most relationship-first platforms carry only in part.
Guardrails and Compliance
For regulated buyers, this is usually the deciding section. A compliance or operations lead will not approve a system whose behavior is "trust us, it usually works." Complex workflows touch money, accounts, and personal data, so the guardrails have to be testable before launch, not discovered in production.
Lorikeet's approach is defence in depth: pre-launch adversarial simulations and red-teaming surface the bad paths before customers hit them, inbound message checks screen what comes in, outbound guardrails constrain what goes out (no PII leaks, scripted disclosures, value-threshold blocks, escalation triggers), and Coach runs 100% post-resolution QA so quality is measured on every ticket rather than a sampled few. On posture, Lorikeet is SOC 2, BAA-ready for HIPAA, GDPR-aligned, with PII redaction, RBAC, and US/AU/UK data residency, and it has passed security reviews including major US banks. These features support your compliance obligations; they do not discharge them, so your own review still owns the sign-off.
Gladly is a mature, well-secured platform with the certifications consumer brands expect, and it handles standard support data responsibly. Where the gap shows is the regulated, action-taking end: pre-launch adversarial simulation you can read as a pass/fail report, guardrails scoped to financial and health workflows, and 100% automated QA on every resolution are not the center of what Gladly was built to do. For teams whose toughest stakeholder sits in compliance, that difference matters more than relationship continuity.
Audit Trails and Post-Resolution QA
When a multi-step workflow goes wrong, the team reviewing it needs to point at the exact step where it went wrong. A replayable record of every tool call and reasoning step, in order, with timestamps, is the artifact compliance and QA teams rely on. This is comparatively simple when the AI handled a conversation and replied, and much harder for a resolution agent, where six actions ran in sequence and one of them touched money or personal data.
Lorikeet logs the full action chain for replay and adds Coach, an analytics and QA agent that scores resolutions after the fact so quality is measured on 100% of tickets rather than a sampled few. Coach can run standalone at about $0.25–$0.30 per ticket, doing root-cause analysis, ticket quality scoring, and resolution verification, effectively AI evaluating the AI. For complex work, automated QA on every resolution beats the spot checks most teams rely on, because the tickets that go wrong are rarely the ones a sample happens to catch. Gladly provides strong reporting on conversations, agent performance, and customer experience, which is the right lens for its job; the step-by-step replay and 100% post-resolution QA built for action chains is where Lorikeet is purpose-built.
Channels: A Unified Customer Thread vs One Resolution Engine
Gladly is strong on channels by design. Its model unifies chat, email, voice, SMS, and social into a single conversation per customer, so an agent never asks a returning consumer to repeat themselves. For consumer brands, that continuity is the product, and it is a genuine strength.
Lorikeet runs chat, email, SMS, WhatsApp, and voice on one workflow engine with shared memory, plus outbound re-engagement for collections and abandonment with DNC, call-hour, and consent controls. Voice runs at sub-1-second latency with automatic language switching, and the agent can take actions on a call rather than route to a human. The distinction for complex work goes beyond shared history. The same agent can execute a regulated action chain on any channel: a dispute that starts in chat, gets a confirmation by email, and a card lock requested by phone is resolved by the same agent with shared context, not handed to a person at the action step. Both products avoid the trap of bolting voice onto a separate stack and joining it with a transcript handoff.
Pricing: Per-Agent Seats vs Per-Resolution
This is where the two pricing models reflect the two jobs. Gladly licenses primarily per agent, commonly cited at roughly $180 to $210 per agent per month on annual terms with seat minimums, with Sidekick AI conversations billed separately at around $0.60 each. Gladly does not publish a public price list, so treat those figures as directional and confirm current terms directly. Seat-based pricing fits a model where skilled human agents, supported by AI, are central to the consumer experience.
Lorikeet prices per resolution, at about $0.80 for a resolved chat, email, or SMS ticket and about $1.00 for a resolved voice call, with the customer holding veto over what counts as a resolution and escalations not charged. Coach runs at about $0.25–$0.30 per ticket and can be deployed standalone. For context, human-handled tickets typically cost about $1.25 to $4 each, so Lorikeet sits well below the human baseline. The pricing comparison is not really about the headline number; it is about what you are buying. Gladly's seat model buys a relationship platform that human agents drive with AI assistance. Lorikeet's per-resolution price buys a full multi-step, multi-channel action chain that ends with the issue actually resolved, and you do not pay when the agent escalates. Map each model to the job it does for your tickets rather than comparing a seat fee against a per-resolution rate in the abstract.
Which One Should You Choose?
Choose Gladly if you are a B2C retail, hospitality, or consumer brand whose support advantage is a personal, continuous relationship with each customer across every channel, you want a mature platform with a polished agent and consumer experience, and you are comfortable with a human-plus-AI model where skilled agents stay central. It is genuinely good at that job, and for many consumer brands it is the right choice.
Choose Lorikeet if your hardest tickets are KYC unlocks, disputes, transfers, claims, and policy-bound conversations, if you operate in a regulated industry, and if your toughest stakeholder sits in compliance or operations. Lorikeet is built so the hard cases are the ones it handles well, not the ones it routes away, and so behavior is provable before launch and reviewable after. Its honest limitation: it is deliberately specialized for complex and regulated work, so if your priority is a warm, relationship-centered consumer experience and your tickets are mostly conversational rather than action-heavy, Gladly will feel more purpose-built for that and its people-centered model is a real differentiator. Gladly also carries deep retail and consumer-brand maturity that a regulated-resolution platform is not trying to replicate.
Some teams run both: Gladly as the customer relationship and human-service layer, and Lorikeet for the complex, regulated tickets that carry real risk and need autonomous resolution. That is a reasonable architecture, not a contradiction.
How to Run a Fair Head-to-Head
If you are evaluating both, the trials below cut through the demo gloss faster than any feature matrix.
Bring your three hardest tickets, not your average ones
Demos are built to look good on a clean path. Run your three hardest real tickets through each product end to end, with multiple tool calls, a deliberate mid-chain failure, and an escalation. Watch what each does when a core system returns a 5xx halfway through, and whether the AI resolves it or keeps the conversation warm and hands to a human.
Ask whether deterministic and natural-language steps combine
For regulated work you need exact, repeatable steps where a disclosure cannot vary, and flexible reasoning where the situation is ambiguous. Ask whether you can mix both in one workflow and whether non-engineers can configure it in plain language.
Require provable guardrails before go-live
Ask whether you can run the guardrail and simulation suite before launch and read the pass/fail report. Pre-launch adversarial testing is the difference between approving behavior and approving faith. These features support your compliance obligations; your own review still owns the sign-off.
Demand a replayable audit trail and 100% QA
Ask each vendor to replay a real ticket from last week, step by step, with every tool call and the reasoning between them. Then ask how quality is measured: on a sample, or on every resolution. Automated QA on 100% of tickets beats spot checks for complex work.
Key Takeaways
Gladly and Lorikeet win different jobs. Gladly leads the people-centered customer relationship across channels for B2C and retail brands; Lorikeet leads end-to-end resolution of complex, regulated, multi-step tickets.
The dividing line is what happens when a single answer or a warm reply cannot finish the ticket: chained tool calls, mid-chain recovery, deterministic plus natural-language steps, and a replayable audit trail.
Gladly unifies chat, email, voice, SMS, and social into one conversation per customer; Lorikeet runs the same channels plus WhatsApp on one engine and executes regulated action chains on any of them, including sub-1-second voice.
On price, Gladly licenses per agent (commonly cited around $180 to $210 per agent per month, plus ~$0.60 per Sidekick conversation, unpublished so confirm directly); Lorikeet is ~$0.80–$0.95 per chat/email/SMS resolution and ~$1.20–$1.50 per voice, with escalations not charged. Compare by the job each model covers.
For regulated buyers, the buying test that cuts through demos: bring your three hardest tickets, force a mid-chain failure, and ask to replay the full audit trail.
Conclusion
Comparing Gladly and Lorikeet is not a contest to find the single best customer support product. It is a question of which job you are solving. Gladly is a strong, mature platform for building a lifelong, people-centered relationship with consumers across every channel, and a fair comparison should say so plainly. Lorikeet is built for the harder job: resolving the complex, regulated, multi-step tickets where correctness matters most, where guardrails have to be provable before launch, and where audit trails have to be replayable after.
If your priority is a warm, continuous consumer relationship, start with Gladly. If your hardest tickets are the ones that carry real risk and your toughest stakeholder sits in compliance or operations, that is the bar to buy against.
If you are weighing Gladly against Lorikeet for complex workflows, book a Lorikeet demo and bring your three hardest tickets. We will run them against your guardrails before you sign.
