8 Best Kore.ai Alternatives for Contact Centers
Information updated as of January 2026.
TL;DR: Kore.ai offers no-code bot development with pre-built industry templates, but self-service platforms put the design burden on you. Without conversation intelligence to reveal what top performers actually do, organizations risk building AI agents that behave like untrained employees. Leading Kore.ai alternatives include Decagon, Rasa, and Cresta, the unified platform combining AI agents, real-time guidance, and conversation intelligence.
Kore.ai is a platform for deploying AI agents across verticals like banking, healthcare, and retail.
Its Experience Optimization (XO) Platform lets business users build conversational AI without engineering support, and its pre-configured industry workflows can accelerate deployment for common use cases. For organizations focused primarily on virtual assistant development, these strengths are important.
But self-service platforms carry risk. Kore.ai requires you to design and build agents yourself, which works if you already understand what your conversations look like and what top performers do differently. Most organizations don't have that visibility. Without it, you end up building AI agents that behave like untrained new hires, unsure how to handle real conversations when they deviate from scripted paths.
This article examines why contact centers look beyond Kore.ai, compares the leading alternatives across autonomous platforms, open-source frameworks, and unified approaches, and provides guidance for choosing the right platform for your organization.
Why contact centers evaluate Kore.ai alternatives
Kore.ai works well for what it's designed to do. But contact center leaders evaluating AI platforms often discover that bot-building is only part of the problem they need to solve. Organizations seek alternatives for four key reasons:
- They need more than bot-building: Enterprise contact centers need platforms that handle AI agents, human agent assistance, and analytics together rather than focusing on virtual assistant development alone.
- They need conversation intelligence to guide AI agent design: Building effective AI agents requires understanding what your conversations actually look like and what top performers do differently. Platforms with built-in conversation intelligence provide that foundation before automation begins.
- They want real-time guidance, not just automation: In-conversation guidance during live interactions prevents mistakes and surfaces relevant knowledge when agents need it.
- They’re seeking a single platform approach: Organizations want single platforms that handle AI agents alongside human agent assistance and analytics together, rather than stitching together separate tools. Unified platforms share data, models, and integrations across capabilities without fragmentation.
- They have different deployment preferences: Organizations vary in technical resources, control preferences, and data residency requirements.
The table below summarizes how the leading alternatives compare across these dimensions.
Best Kore.ai alternatives at a glance
1. Cresta
Organizations moving beyond bot-building platforms often land on Cresta because it addresses the gaps that pure automation tools leave behind. Where Kore.ai focuses on virtual assistant development, Cresta treats automation and human agent performance as connected problems that require a unified solution.
The platform brings together AI agents, real-time agent guidance, and conversation intelligence on a shared foundation. These aren't separate tools stitched together after the fact. They share data, models, and integrations, so insights from conversation analytics inform both AI agent optimization and human agent coaching. When an AI agent escalates to a human, context transfers and support continue rather than dropping into a blind spot.
Forrester validated this approach in The Forrester Wave for Conversation Intelligence Solutions for Contact Centers, Q2 2025, where Cresta achieved the highest Current Offering score among all evaluated vendors.
Here's how each capability addresses the gaps that bring teams to evaluate Kore.ai alternatives:
- Cresta AI Agent automates conversations across voice and digital channels using a multi-model architecture with 20+ task-optimized models. A routing agent identifies intent and selects specialized sub-agents for different tasks, handling complex multi-intent conversations that would break simpler bot frameworks.
- Cresta Agent Assist provides real-time guidance during live human conversations, surfacing knowledge, behavioral hints, and compliance reminders as agents work. When AI agents escalate to human representatives, Agent Assist continues providing support with full handoff context, so the customer doesn't start over and the agent isn't flying blind.
- Agent Operations Center gives supervisors visibility into hundreds of simultaneous AI conversations with the ability to intervene when needed. This human-in-the-loop oversight addresses a common concern with autonomous platforms: what happens when the AI gets it wrong?
- Enterprise-grade guardrails provide four layers of defense: system-level constraints embedded in prompts, supervisory models monitoring in real time, LLM-driven adversarial testing, and automated behavioral quality management.
- Cresta Conversation Intelligence analyzes 100% of interactions across both AI and human agents. Unlike analytics tied only to bot performance, this connects conversation patterns to business outcomes like revenue, retention, and satisfaction across your entire operation.
This is how Brinks Home, one of North America's largest home security companies, achieved a 30-point NPS increase after implementing Cresta. With in-house agents plus BPOs across multiple locations on various platforms, they lacked visibility across their organization and struggled with inconsistent customer experiences.
Cresta gave them unified visibility across both AI and human agent conversations, and the results showed up across all their core metrics.
Who it's for: Contact centers that need more than chatbots. Organizations in financial services, healthcare, telecommunications, and travel that need compliance visibility, where human agents handle high-value conversations, and that need a seamless handoff between AI and humans.
Ready to see how Cresta works in practice? Visit the resource library to learn more or request a Cresta demo.
2. Sierra
Sierra offers a managed service model where the vendor handles implementation, setup, and ongoing optimization. This approach gets AI agents live without requiring internal AI expertise, but it also means less direct control over how agents are configured and how they evolve over time.
Sierra’s roots are in fully autonomous resolution, but it now also supports agent assist via Live Assist, where the same AI agent powers real-time guidance and drafted responses for human reps.
Who it's for: Brands comfortable with vendor-managed implementations who prioritize speed to deployment over internal control.
3. Decagon
Decagon's Agent Operating Procedures (AOPs) combine natural language instructions with the rigor of code, positioning the platform around operational agility. The idea is that CX teams can launch and experiment quickly without waiting on engineering queues, while the underlying logic remains code-accessible.
That agility comes with tradeoffs. Configuring and optimizing agents still requires technical investment, and organizations without engineering resources may find the learning curve steeper than expected. The platform provides API extensibility for custom integrations, but realizing that flexibility demands ongoing technical involvement.
Who it's for: Organizations with engineering resources who want to iterate quickly on AI agent behavior. Works for teams that view technical investment as a worthwhile tradeoff for control.
4. Cognigy
Cognigy was recently acquired by NICE, making it the AI agent component of the NICE platform. For organizations already in the NICE ecosystem or evaluating it, Cognigy is now the default path for conversational AI.
The platform uses a hybrid architecture that layers LLM capabilities on top of rule-based workflows. This on-rails approach provides predictability for structured conversations but may constrain flexibility when interactions deviate from expected paths.
Who it's for: Global enterprises requiring multilingual support at scale and conversational AI capabilities with flexibility across AI providers.
5. Google Contact Center AI
Google's Customer Engagement Suite with Google AI provides building blocks for contact center automation: Conversational Agents (via Dialogflow CX) for self-service, Agent Assist for live rep support, Conversational Insights for analytics, and CCAI Platform for infrastructure.
The keyword is "building blocks." Google provides the components, but your team must have the technical resources to spec, design, and implement a solution that fits your needs. Organizations without dedicated engineering capacity may find the assembly required more demanding than turnkey alternatives.
Who it's for: Organizations on Google Cloud with internal technical resources to design and build their own implementation. Works for teams comfortable assembling components rather than deploying a pre-integrated platform.
6. Rasa
Rasa provides an open-source conversational AI framework with on-premises deployment options for organizations with strict data sovereignty requirements. The platform's CALM architecture layers LLM capabilities on top of deterministic business logic.
"Open-source" and "self-hosted" sound appealing, but they come with a significant technical burden. Building, maintaining, and optimizing a Rasa deployment requires dedicated developer resources and infrastructure management. Organizations should weigh the control benefits against the ongoing investment required.
Who it's for: Enterprises with developer resources and infrastructure to build and maintain a self-hosted solution. Works for organizations where data sovereignty requirements rule out cloud-hosted alternatives.
7. Replicant
Replicant provides autonomous AI agents designed specifically for voice. The platform focuses exclusively on phone-based automation, which makes it a fit for contact centers where voice is the only channel that matters.
Replicant also offers a conversation intelligence product, though it's focused narrowly on QA and compliance rather than broader performance analytics. Organizations wanting conversation intelligence that spans AI and human interactions, connects to business outcomes, or informs coaching and optimization may find the scope limiting.
Who it's for: Contact centers where voice is the only channel requiring automation. Less suited for organizations needing omnichannel coverage or comprehensive conversation intelligence.
8. Ada
Ada uses Playbooks to define AI agent behavior through structured, pre-built workflows. This approach enables fast deployment, but the rigidity of Playbooks can limit flexibility when conversations deviate from expected paths or when use cases require more dynamic handling.
The platform supports over 50 languages and covers chat, voice, email, and social channels. Compliance certifications include HIPAA, SOC2, and GDPR.
Who it's for: Organizations wanting fast deployment through pre-defined workflows that can accept less flexibility for complex or dynamic conversations. Works for teams that prioritize speed over customization.
Choosing the right alternative for your organization
The right choice depends on which Kore.ai gaps matter most to your organization and what tradeoffs you're willing to accept.
Decagon offers operational agility through its Agent Operating Procedures, though realizing that flexibility requires ongoing technical investment. Rasa provides on-premises deployment for data sovereignty requirements, but building and maintaining a self-hosted solution demands significant developer resources.
Contact centers where voice is the only channel might evaluate Replicant, though its conversation intelligence is limited to QA and compliance. Cognigy, now part of NICE, serves enterprises needing structured workflows within that ecosystem. Google CCAI provides building blocks for organizations on Google Cloud, but requires internal technical resources to assemble them.
These platforms solve different pieces of the puzzle. But if the underlying problem is that you need automation and human agent performance working together, Cresta addresses both.
The platform continues supporting human agents with real-time assistance after AI escalations, analyzes 100% of interactions across both AI and humans, and connects performance data to business outcomes rather than just containment metrics. That visibility across the full operation is what CX leaders need.
Request a demo to see how Cresta works with your specific environment.
Frequently asked questions about the best Kore.ai alternatives
How does Cresta differ from platforms like Kore.ai?
Platforms like Kore.ai focus on creating virtual assistants. Cresta takes a different approach by bringing together AI agents, real-time guidance for human agents, and conversation intelligence across all interactions.
Unlike platforms where visibility ends at escalation, Cresta maintains complete oversight. When AI agents escalate to human representatives, Cresta Agent Assist continues supporting those agents with knowledge surfacing, behavioral guidance, and automated summarization. Post-escalation outcomes feed back into the system to improve future automation.
What should I ask vendors about AI agent governance and oversight?
Ask how they prevent AI agents from generating false information and what verification mechanisms exist before presenting information to customers. You should also ask how supervisors monitor AI agent conversations, what triggers alerts, and whether intervention can happen without disrupting customer experience.
Can I start with conversation intelligence before deploying AI agents?
Yes, and many organizations find this phased approach delivers better results than jumping straight to automation. Start with conversation intelligence to understand performance drivers, add agent assist to improve human agents, then deploy AI agents strategically. Starting with conversation intelligence provides foundational data for making informed automation decisions.
How long does implementation typically take?
Implementation timelines vary based on scope and organizational readiness. Conversation intelligence implementations can deliver initial insights within weeks. AI agent implementations, on the other hand, require more substantial investment because they involve workflow design, knowledge base preparation, testing, and gradual rollout.
What are the risks of self-service bot-building platforms?
Self-service platforms require you to design agents based on what you think conversations look like. Without conversation intelligence to reveal actual patterns, what top performers do differently, and where interactions break down, you risk building AI agents that behave like untrained new hires. They may handle scripted paths but struggle when real conversations deviate.
