Cresta and Lorikeet both apply AI to customer conversations, but they aim at different jobs. Cresta makes human contact center agents faster and more consistent with real-time assist and coaching. Lorikeet resolves complex, regulated tickets end-to-end without a human in the loop, with the guardrails and audit trails compliance teams sign off on. The right pick depends on whether you want to amplify your agents or autonomously resolve your hardest tickets.
Cresta vs Lorikeet is a comparison between two contact center AI platforms that attack the same cost-and-quality problem from opposite directions. Cresta is a contact center intelligence platform built to lift the performance of human agents through live agent assist, automated coaching, and conversation analytics. Lorikeet is an AI concierge platform built for complex and regulated industries (fintech, financial services, healthtech, insurance, gaming) where the goal is autonomous end-to-end resolution backed by deterministic workflows and a compliance-approvable audit trail. This is a head-to-head on what each is built for, autonomy, regulated-grade guardrails, channels, pricing, and deployment, written to help you shortlist the right one rather than to crown a universal winner.
Cresta's strength is agent augmentation in the contact center: real-time agent assist, automated quality and coaching, and conversation intelligence that raise the performance of human teams at scale.
Lorikeet's strength is autonomous resolution of complex, multi-step tickets: deterministic and natural-language workflows in one interaction, defence-in-depth guardrails, omnichannel including sub-1-second voice, and 100% automated QA via its Coach agent.
Pricing models differ. Cresta uses enterprise annual contracts that are typically quoted per agent or per seat; Lorikeet prices per resolution, roughly $0.80–$0.95 per chat, email, or SMS resolution and about $1.20–$1.50 per voice resolution, with escalations not charged and the customer defining what counts as a resolution.
Both serve enterprise buyers and carry SOC 2. Lorikeet adds BAA-ready (HIPAA) posture, GDPR alignment, PII redaction, RBAC, and US, UK, and AU data residency, which matters for regulated resolution work.
Choose Cresta to make a large human agent workforce faster and more consistent. Choose Lorikeet when you want to resolve regulated, multi-step tickets autonomously with an audit trail your compliance team can approve before launch.
Last updated: June 2026
Most contact center AI comparisons assume both products do the same thing and then argue over feature checklists. Cresta and Lorikeet do not do the same thing, and that is the most important fact to settle before you compare anything else. Cresta is an agent-assist and coaching platform: a human is still in the conversation, and the AI listens, suggests, and grades. Lorikeet is an autonomous resolution platform: the AI is the agent for the tickets it handles, and a human is the escalation path, not the default. Both reduce cost and improve quality, but they do it through different mechanisms, and the better fit depends on the shape of your operation. This comparison treats both fairly: Cresta earns real credit for what it does well in the contact center, and Lorikeet is positioned where it genuinely leads, on autonomous regulated resolution.
Cresta vs Lorikeet at a Glance
Cresta · Best for: Enterprise contact centers with large human agent teams that want real-time assist, automated coaching, and conversation intelligence · Key strength: Lifting human agent performance and consistency at scale · Primary mode: Agent augmentation (human in the loop), with virtual agent capabilities · Pricing: Enterprise annual contracts, typically per agent or per seat · Compliance: SOC 2
Lorikeet · Best for: Complex and regulated companies (fintech, financial services, healthtech, insurance, gaming) needing autonomous end-to-end resolution with audit trails · Key strength: Resolution depth on multi-step regulated tickets; defence-in-depth guardrails; 100% automated QA · Primary mode: Autonomous resolution (AI is the agent), with human escalation · Channels: Chat, email, voice (sub-1-second latency), SMS, WhatsApp, plus outbound re-engagement · Pricing: Per resolution (~$0.80–$0.95 chat/email/SMS, ~$1.20–$1.50 voice; escalations not charged) · Compliance: SOC 2, BAA-ready (HIPAA), GDPR-aligned, US/UK/AU data residency
What Each Platform Is Built For
Cresta is a contact center intelligence platform built to make human agents better in real time. Its core is generative AI that listens to a live conversation and surfaces suggested responses, next-best actions, and knowledge to the agent as they talk, paired with automated quality management and coaching that scores interactions and identifies where agents need help. Cresta is strong in large, voice-heavy contact centers, sales and retention floors, and operations where a sizable human workforce is the engine of customer contact and the goal is to raise the performance of that workforce. It also offers virtual agent capability, but its center of gravity, and where it is most differentiated, is amplifying the humans you already employ.
Lorikeet is an AI concierge platform built specifically for complex and regulated industries. Roughly 80% of its customers are US financial institutions and fintechs. Rather than assisting a human agent, Lorikeet builds concierges that resolve issues end-to-end across channels, and a second agent, Coach, that runs analytics and 100% automated QA. The design center is the hard ticket, the kind that needs several actions executed in the right order against live systems, with a record a compliance team can replay. Where Cresta optimizes for human agent performance, Lorikeet optimizes for autonomously resolving the ticket so it never reaches a human at all, unless a guardrail or your policy says it should.
Autonomy: Agent Assist vs End-to-End Resolution
The clearest difference between the two platforms is who is doing the work. With Cresta, a human agent owns the conversation and the AI augments them: it suggests what to say, flags compliance language, retrieves the right answer, and afterward scores how the agent did. That model is genuinely valuable. It lifts the floor across a large team, shortens ramp time for new hires, and makes quality more consistent without removing the human judgment that hard or emotional conversations sometimes need. For a contact center whose strategy is a strong human workforce made faster and more uniform, agent assist is the right mechanism.
With Lorikeet, the AI is the agent for the tickets it handles. A question like "why was my transfer declined, and can you refund the fee" is not answered by prompting a human; it is resolved by the concierge verifying identity, checking the transaction in a payment system, applying policy, taking the action, and confirming the outcome, keeping state across all of it and recovering gracefully if a step errors. Lorikeet combines deterministic structured workflows with natural-language workflows, and the two can run in a single interaction, so a flow that must follow an exact regulated script can hand off to flexible reasoning and back without leaving the conversation. The Team of Agents capability dispatches sub-agents to call third parties and coordinate, for example contacting a merchant on a dispute or a pharmacy on a prescription question. The honest tradeoff: autonomous resolution of regulated work is more configuration than turning on agent assist, which is why Lorikeet ships with forward-deployed implementation help rather than expecting you to self-serve the hardest workflows alone.
Regulated Guardrails
For a regulated buyer evaluating autonomous resolution, the question is not "does it help my agents" but "can my compliance team approve the agent acting on its own before launch." That is where Lorikeet concentrates its design effort, and it is a question that matters differently for an autonomous agent than for an assist tool. When a human is in the loop, that human is a control; when the AI resolves end-to-end, the controls have to be built into the system.
Lorikeet runs what it calls defence in depth: pre-launch adversarial simulations and red-teaming, inbound message checks, outbound guardrails, and 100% post-facto QA through the Coach agent. The framing the team uses is that the large language model is the engine and Lorikeet is the cockpit. In practice that means you can simulate the bad paths before go-live, read the results, and prove the agent declines to act when a guardrail trips, rather than discovering the failure mode in production. These features support your compliance obligations; they do not by themselves certify compliance, and any vendor that promises certification is overstating it.
Cresta operates with SOC 2 compliance and includes real-time guardrails aimed at keeping live agents on script and compliant, such as flagging required disclosures or risky language during a call. That is a real strength for a human-assist model, where the goal is to guide the person speaking. The distinction is the unit being governed: Cresta's guardrails shape what a human agent says in the moment, while Lorikeet's are designed to govern an autonomous agent's actions end-to-end, with simulation-based validation before launch and automated QA on every ticket afterward. If you are augmenting humans, Cresta's runtime guidance fits the model; if you are resolving autonomously in a regulated domain, the deeper requirement is to prove the agent behaves correctly without a person there to catch it.
The reason this matters is the cost of the tail. Any platform can post strong aggregate numbers while still failing on the small set of interactions that carry real risk, a disclosure left out of a collections call, a PII detail surfaced to the wrong party, an action taken above a dollar threshold that should have required human approval. Aggregate metrics hide those cases by design. Lorikeet's approach is to make the tail testable: you can run adversarial simulations against the exact scenarios that worry your compliance team, read a pass-or-fail report, and gate launch on it, then keep Coach watching every ticket afterward so a regression shows up in QA rather than in a regulator inquiry. That posture is specifically built for letting an AI act on its own in regulated work.
Channels
Cresta is strongest in the voice contact center, with deep real-time capability on live calls, and also supports chat and messaging channels for agent assist and analytics. For an operation centered on phone-based human agents, that voice depth is a genuine strength.
Lorikeet covers chat, email, voice, SMS, and WhatsApp on the inbound side, plus outbound re-engagement over voice, SMS, and email for use cases like collections and abandonment, with compliance controls for do-not-call lists, call-hour rules, and consent. Its voice agent runs at sub-1-second latency with natural conversation and automatic language switching. The architectural point that matters for autonomous support is that voice runs on the same workflow engine as chat and email, so the agent carries shared context across channels and can take actions on a call rather than routing to a human. A customer who starts in chat and moves to voice does not have to repeat themselves, and the agent can lock a card or file a dispute live. The difference from Cresta is again about who acts: Cresta's voice strength helps a human handle the call, while Lorikeet's voice agent handles the call itself.
Pricing
The platforms use different commercial models, and the difference follows directly from the assist-versus-autonomy split.
Cresta uses enterprise annual contracts, typically quoted on a per-agent or per-seat basis, which fits an agent-assist product where the value scales with the number of human agents you are equipping. Public per-seat figures are not consistently published, so the right reference point is a custom enterprise quote based on your agent count. For a large contact center with a known headcount, a per-seat model is straightforward to budget against.
Lorikeet prices per resolution and lets the customer define what counts as a resolution. Chat, email, and SMS resolutions run about $0.80–$0.95 each, voice resolutions about $1.20–$1.50, and the Coach QA agent runs about $0.25–$0.30 per ticket and can be deployed standalone. Escalations to a human are not charged, which removes the incentive for the vendor to claim a resolution it did not earn. Set against a human baseline of roughly $1.25 to $4 per handled ticket, per-resolution pricing is designed to track value rather than seats.
The practical question for a buyer is which model maps to your strategy. If you are keeping a large human workforce and want to make it more productive, a per-seat tool like Cresta charges in proportion to the team you are equipping, which is a clean fit. If your goal is to remove tickets from human queues entirely, per-resolution pricing charges only for the work the AI actually completes, and because escalations are not billed and the customer defines a resolution, you are not paying for interactions the agent did not finish. The two models are not really competing on price so much as on what you are buying: agent hours made better, or tickets resolved without an agent.
Deployment
Cresta deploys into an existing contact center stack and integrates with major telephony and CCaaS platforms, then trains its models on your conversation data to tune assist and coaching to your domain. The deployment effort centers on integration, data, and rolling the tooling out to a human agent population, with change management for the agents who will use it day to day. For organizations that already run a large contact center and want to layer intelligence onto it, that is a familiar shape of project.
Lorikeet pairs a plain-English configuration model, where workflows, guardrails, and tools are defined in natural language, with a forward-deployed product manager and engineer during implementation. A working sandbox is typically standing in 20 to 30 minutes, with a production deployment operational in about a month. The tradeoff is honest: standing up autonomous resolution of regulated flows asks for more upfront partnership than enabling assist features for existing agents, because the agent has to do the whole job correctly rather than suggest it. The payoff is that the hard, regulated flows are built and validated before they go live.
Where Cresta Is the Stronger Choice
It would be unfair to frame this comparison as if autonomy were always the goal. For many operations it is not, and Cresta is the better fit for several real situations.
If your strategy depends on a large human agent workforce, in sales, retention, or high-touch service where human judgment and relationship matter, Cresta is built to make that workforce better rather than replace it. Its real-time assist shortens new-hire ramp, lifts the performance of mid-tier agents toward your best ones, and makes quality more consistent across a big team, and its automated coaching turns every interaction into a learning signal instead of sampling a handful for QA. For voice-heavy contact centers, Cresta's depth on live calls is a particular strength. And if your conversations genuinely need a person, because they are emotional, high-value, or too varied to fully automate, an assist model keeps the human in control while still capturing the efficiency and consistency gains. The honest summary is that Cresta wins when the human agent is central to your strategy and the goal is to amplify that human, and for a great many contact centers that is exactly the right goal.
Feature-by-Feature Summary
Primary mode: Cresta augments human agents with real-time assist and coaching; Lorikeet resolves tickets autonomously end-to-end, with humans as the escalation path.
Resolution depth: Cresta surfaces the right answer and action to a human in the moment; Lorikeet executes multi-step regulated tickets itself using deterministic plus natural-language workflows in one interaction.
Regulated guardrails: Cresta guides what live agents say with SOC 2 coverage; Lorikeet adds defence in depth (pre-launch simulations, message checks, outbound guardrails, 100% QA) designed to govern an autonomous agent before go-live.
Channels: Cresta is strongest on voice for live agents, with chat and messaging support; Lorikeet runs chat, email, voice, SMS, and WhatsApp plus outbound, with voice on the same engine at sub-1-second latency.
Pricing: Cresta uses per-agent or per-seat enterprise contracts; Lorikeet prices per resolution (~$0.80–$0.95 chat/email/SMS, ~$1.20–$1.50 voice) with escalations not charged.
Compliance posture: Both SOC 2; Lorikeet adds BAA-ready (HIPAA), GDPR alignment, PII redaction, RBAC, and US, UK, and AU data residency.
How to Choose Between Cresta and Lorikeet
The decision comes down to whether you are amplifying agents or replacing the work that reaches them.
Choose Cresta if your strategy is built around a large human agent workforce and you want to make it faster, more consistent, and better coached. Cresta's real-time assist, automated quality management, and conversation intelligence are real strengths, and for sales floors, retention teams, and voice-heavy contact centers that depend on human agents, they are exactly what the job needs.
Choose Lorikeet if you operate in a regulated or complex domain, your important tickets require several actions in the right order against live systems, and you want to resolve them autonomously with behavior your compliance team can approve before launch. Lorikeet leads on autonomous resolution depth, defence-in-depth guardrails, deterministic plus natural-language workflows, single-engine omnichannel including sub-1-second voice, simulation-based validation, audit trails, and 100% automated QA. The cost of that depth is a more involved implementation, which is why Lorikeet provides forward-deployed help rather than leaving you to self-serve the hardest flows. The two can also coexist: assist humans on the conversations that need them, and resolve the rest autonomously.
If your hardest tickets are regulated and multi-step, book a Lorikeet demo and bring your toughest 10 tickets. We will run them in your stack against your guardrails before you sign.
Key Takeaways
Cresta and Lorikeet solve the contact center problem from opposite directions: Cresta amplifies human agents with assist and coaching, Lorikeet resolves complex regulated tickets autonomously end-to-end.
On autonomy, Cresta keeps a human in the conversation and suggests and scores; Lorikeet makes the AI the agent, combining deterministic and natural-language workflows and dispatching sub-agents for multi-step work.
On guardrails, Cresta guides live agents with SOC 2 coverage; Lorikeet's defence in depth (pre-launch simulations, message checks, outbound guardrails, 100% QA) is built to govern an autonomous agent before go-live.
Pricing follows the model: Cresta is per agent or per seat, Lorikeet is per resolution (~$0.80–$0.95 chat/email/SMS, ~$1.20–$1.50 voice) with escalations not charged.
Pick by your strategy: amplifying a human workforce points to Cresta; autonomously resolving regulated, multi-step tickets points to Lorikeet.
