11 Best Conversational AI Platforms for CX Teams in 2026

Search for a conversational AI platform, and you'll find two lists mashed together: enterprise platforms that run customer service across chat, voice, and messaging, and developer tools for building a single phone agent. Picking from the wrong list costs months.

At Cekura, we test agents that teams build on platforms like Kore.ai, Google’s Gemini Enterprise for CX, and Genesys Cloud, so we see where they hold up under real customer conversations and where they wobble.

This guide focuses on the enterprise side, which is 11 platforms built to automate customer service at scale, compared across channels, governance, deployment, and cost, so you can shortlist the two or three that fit your contact center.

The 11 best conversational AI platforms for enterprise:

1. Kore.ai: Best for complex, multi-system service deployments

2. Google Gemini Enterprise for CX: Best for teams on Google Cloud that want published per-session pricing

3. NiCE Cognigy: Best for large contact centers that need to plug into any CCaaS

4. Boost.ai: Best for banks, insurers, and public-sector teams

5. Yellow.ai: Best for fast time to value with non-coders building

6. IBM watsonx Orchestrate: Best for enterprises that want one control plane for every AI agent

7. Microsoft Copilot Studio: Best for organizations standardized on Microsoft

8. Amazon Lex: Best for AWS teams building on Amazon Connect

9. Rasa: Best for developer teams that need full on-prem control

10. LivePerson: Best for messaging-heavy customer engagement

11. Sprinklr: Best for enterprises already running Sprinklr for social and service

What Is a Conversational AI Platform?

A conversational AI platform is software for building, deploying, and managing AI agents that talk with customers over chat, voice, and messaging, and that take action in back-end systems, like updating an account. That ability to act is what separates it from a basic chatbot.

How a Conversational AI Platform Works

Under the hood, most platforms pair a language model (or several) with deterministic flows for the steps that have to go exactly right, like verifying identity or issuing a refund. The model handles open-ended questions, and the flows keep high-stakes paths predictable.

Around that core, an enterprise platform adds the parts a contact center needs, like voice connectivity, integrations with your CRM and CCaaS, handoff to human agents with context, guardrails, analytics, and a way to test changes before they reach customers.

Conversational AI Platform vs. Voice Agent Builder vs. Chatbot

A chatbot answers a defined set of questions on one channel. A voice agent builder like Retell or Vapi gives developers the parts to ship a phone agent fast. A conversational AI platform runs customer service across every channel, with governance and contact-center integrations built in.

If you're building a single phone agent, our best AI voice agent platforms guide is the better fit. And if you're weighing agents that only talk against agents that act, our comparison of agentic AI vs conversational AI separates the two.

How We Evaluated These Platforms

We compared each platform against the criteria enterprise buyers get grilled on in procurement, using the vendors' own product pages, pricing pages, documentation, and security pages as of October 2026:

• Channels: whether voice, chat, and messaging run on one platform or need separate products.

• Agentic capabilities and guardrails: how the platform mixes LLM reasoning with deterministic control, and what stops an agent from going off-policy.

• Integrations: native connectors for CRMs, help desks, and contact-center platforms like Genesys, NiCE CXone, and Amazon Connect.

• Human handoff: whether the agent passes full context to a person.

• Deployment and compliance: SaaS, private cloud, or on-prem options, plus stated certifications.

Conversational AI Platforms Compared

Most failures we catch trace back to untested edge cases, whichever platform a team picked, which is worth keeping in mind as you read the vendor claims below.

Disclaimer: Prices are subject to change without notice. Always check each vendor's site for current pricing.

1. Kore.ai: Best for Complex, Multi-System Service Deployments

What it does: Kore.ai is an enterprise agent platform for building, governing, and optimizing AI agents, with an "AI for Service" suite that covers voice and digital AI agents, agent assist, quality management, and a native contact center.

Best for: Large enterprises with many back-end systems and strict compliance needs, especially in banking, healthcare, and telecom.

Kore.ai relaunched in May 2026 as the Kore.ai Agent Platform, branded {Artemis}, built on a "dual-brain" design that runs agentic reasoning and deterministic flows side by side.

In practice, compliance rules sit outside the LLM, so a clever prompt can't talk the agent out of a policy.

One Fortune 100 financial institution reports 1.5 million annual voice interactions handled and a 42.3% automation rate after replacing its legacy IVR.

Key Features

• Omnichannel reach: 40+ voice and digital channels and 300+ integrations, including Salesforce, HubSpot, and Microsoft.

• Engine-level guardrails: policy constraints enforced outside the model, with every action traced and auditable.

• Context-rich handoffs: human agents pick up where the AI agent left off, with real-time agent assist alongside.

• Flexible deployment: SaaS, private cloud, or on-prem, with data residency by region.

Pros and Cons

Pros:

✅ Leader placements from both Gartner (2026) and Forrester (Q2 2026)

✅ Strong compliance list: SOC 2 Type II, ISO 27001, and PCI DSS, plus FedRAMP Moderate per its Artemis launch release

✅ Built-in agent assist and QA, so service teams don't need separate tools

Cons:

❌ No public pricing so buying goes through a demo or RFP

❌ Rollouts lean on professional services and forward-deployed engineers

❌ Artemis launched on Microsoft Azure first, with broader cloud availability to follow, and the older XO Platform is still a separate product

Pricing

Kore.ai doesn't publish pricing. Plans are quoted through a demo request or RFP.

2. Google Gemini Enterprise for CX: Best for Teams on Google Cloud

What it does: Customer Experience Agent Studio (CX Agent Studio) is Google's builder for voice and chat customer-service agents, part of the Gemini Enterprise for Customer Experience suite. Google's docs describe it as the evolution of Dialogflow CX, which is now listed as legacy.

Best for: Teams already on Google Cloud that want to build, test, and ship agents quickly with predictable per-session pricing.

CX Agent Studio pulls a low-code builder, a conversation simulator, evaluations, and tracing into one interface, with 35 prebuilt agent templates to start from. Voices cover more than 40 languages.

The Home Depot is one of the headline customers. In a 50-store pilot, its AI voice agents got callers to a solution four times faster than traditional phone menus.

Key Features

• Omnichannel gateway: web, mobile, voice, email, social channels, and apps from one agent.

• Built-in evaluations: automated tests catch regressions after each change, with versioning and one-click rollbacks.

• Agent Assist and CX Insights: real-time coaching for human agents plus analysis of every customer conversation.

• Integrations: out-of-the-box connectors, MCP support, and partnerships with CCaaS vendors.

Pros and Cons

Pros:

✅ Published per-session pricing that includes evaluations and monitoring

✅ HIPAA BAA coverage and ISO 27001, SOC 2, and PCI DSS scope on Google's compliance pages

✅ Fast build cycle, with templates and a simulator in the same tool

Cons:

❌ Dialogflow CX agents don’t carry over directly (Google says CX Agent Studio isn’t a drop-in replacement), and Google sends flow-based customers to sales for pricing

❌ Google Cloud only, with no on-prem option on the pages we checked

❌ Per-session billing means long chats count as extra sessions and long voice calls bill a per-second overage, so costs need modeling

Pricing

CX Agent Studio costs $0.50 per chat or voice session on demand, or $0.45 and $0.40 on 1- and 3-year savings plans. A new session starts after 50 chat turns, and voice sessions over 5 minutes bill at an overage rate of $0.0025 per second.

3. NiCE Cognigy: Best for Contact Centers That Need Any-CCaaS Flexibility

What it does: NiCE Cognigy is an enterprise customer-service AI agent platform that runs voice and digital agents across contact centers.

Best for: Global contact centers that want agentic AI on top of the telephony and agent desktop they already run.

Cognigy joined NiCE in 2025, and the platform now ships as NiCE Cognigy. You pick the LLM per agent job (OpenAI, Anthropic, Google, AWS), and you keep your CCaaS, since Cognigy connects to Genesys, NiCE CXone, Amazon Connect, Avaya, Microsoft, and 8x8.

Frontier Airlines runs 800,000 conversations a month through its Cognigy AI agent, and the company reports 1 billion+ annual interactions across its customers.

Key Features

• Voice Gateway: CCaaS and CPaaS connectivity with barge-in, DTMF, outbound calling, and built-in machine translation for 100+ languages..

• Agentic AI plus intent flows: blends structured workflows with agentic reasoning, 100+ prebuilt tools, and MCP access.

• AI Ops Center: live monitoring, error alerts, and root-cause investigation across agents.

• Agent Copilot and Live Agent: assist for human agents, or plug into your existing desktop.

Pros and Cons

Pros:

✅ Works with almost any CCaaS, which makes it easy to slot into an existing contact center

✅ Free choice of LLM, NLU, STT, and TTS providers

✅ States GDPR and SOC 2 Type II compliance, plus ISO 27001

Cons:

❌ No public pricing; evaluation starts with a demo

❌ Self-hosting requires Kubernetes administration skills

Pricing

NiCE Cognigy doesn't publish pricing. Plans are custom and start with a demo request.

4. Boost.ai: Best for Banks, Insurers, and the Public Sector

What it does: Boost.ai is a conversational AI platform built for regulated industries, running voice and chat agents on one platform with a hybrid NLU and generative architecture.

Best for: Financial services, insurance, telecom, and public-sector teams that need tight control over what the agent says.

Boost.ai calls itself "the conversational AI platform regulated industries trust," and the design backs that up.

Its Adaptive Voice feature uses speech-to-speech for low-risk conversations and a structured, compliant pipeline for transactions, which is the split a bank's risk team asks for on day one.

Acorn Insurance automates 60,000+ chats a month with it, with 96% handled by the AI agent.

One thing to watch: Automation Anywhere announced a deal to acquire Boost.ai on October 7, 2026, expected to close in Q4 2026.

Key Features

• Voice built in: call-center automation alongside digital channels, with STT benchmarking and side-by-side TTS comparison.

• Multi-agent setups: many agents on one instance that hand off to each other, with built-in and custom guardrails.

• CCaaS handoff: passes full context to humans and aligns with queues, priorities, and opening hours.

Pros and Cons

Pros:

✅ ✅ Designed around regulated conversations, with ISO 27001, ISO 27701, and SOC 2 Type II certifications

✅ Prebuilt industry modules (1,500+ topics for insurance alone) and 36+ languages

✅ Built-in testing studio for chat and voice

Cons:

❌ Ownership is changing mid-evaluation, so roadmap and contracts may shift

❌ Narrower footprint than the suite vendors, with its strength concentrated in Europe

❌ No public pricing

Pricing

Boost.ai doesn't publish pricing. Plans are quoted after a demo.

5. Yellow.ai: Best for Fast Time to Value

What it does: Yellow.ai is an agentic AI platform for automating customer and employee service across voice, chat, and email, now built around its Nexus platform and Nexus VOX for voice.

Best for: Enterprise service teams that want non-coders building and launching agents quickly.

Forrester's take, quoted on Yellow.ai's site, is that it's "a good fit for organisations that are looking for fast time to value and want to empower non-coders" to build self-service.

Nexus pushes that further: agents test themselves against thousands of simulated users and try to diagnose and fix failures within the guardrails you set.

Waste Connections automated customer service for 250,000+ users a month with Yellow.ai and reports roughly $4 million in annual savings.

Key Features

• Multi-LLM: 15+ models, including OpenAI and Anthropic, plus its own.

• Nexus VOX: voice agents with sub-400ms responses and voice cloning.

• Integrations: prebuilt connectors including Salesforce, Zendesk, Genesys, and NiCE, with a Genesys AppFoundry connector.

• Data residency: hosting in the US, EU, Singapore, India, Indonesia, and UAE, with an on-prem option.

Pros and Cons

Pros:

✅ A free plan lets you try it before talking to sales

✅ States SOC 2 Type II, ISO 27001, and PCI DSS compliance

✅ Strong fit for non-technical builders

Cons:

❌ Its own pages disagree on integration counts (100+ on one page, 150+ on others), so verify specifics in your demo

❌ Enterprise pricing is quote-only

❌ Gartner placement is Challenger; Leader placements on this list are stronger

Pricing

Yellow.ai's free plan includes one AI agent and 500 chat sessions a month, then $0.99 per resolution. Enterprise plans are custom.

6. IBM watsonx Orchestrate: Best for One Control Plane Across Every AI Agent

What it does: IBM watsonx Orchestrate is an enterprise agent platform for building, orchestrating, and governing AI agents across customer service, IT, HR, finance, and more. It replaces watsonx Assistant, whose pages now redirect to Orchestrate.

Best for: Enterprises that want customer-service agents governed alongside every other AI agent in the company.

Orchestrate's customer-service agents run across web chat, phone, SMS, and WhatsApp, mixing agentic reasoning with deterministic patterns for interactions that can't go wrong.

The bigger pitch is governance: pre-deployment evaluation, audit trails, policy enforcement, and an agent directory in one control plane.

Agent Assist works with Genesys Cloud in voice and chat. Camping World reports a 33% increase in agent efficiency and wait times down to 33 seconds after launching Arvee, its virtual assistant built on watsonx Assistant.

Key Features

• Agentic orchestration: an orchestrator coordinates specialized agents, calls tools, and applies policy checks.

• Governance: pre-deployment evaluation of accuracy and tool-call reliability, plus audit and traceability.

• Integrations: Salesforce, ServiceNow, Zendesk, Twilio Flex, Microsoft Dynamics 365, and open standards like A2A and MCP.

Pros and Cons

Pros:

✅ Published starting prices and a free 30-day trial

✅ On-prem and multi-cloud deployment

✅ Agent assist included without an extra purchase

Cons:

❌ Customer service is one use case among many, so it feels less purpose-built than CX-first platforms

❌ Phone connects through two documented paths only: Genesys Audio Connector and SIP trunk

❌ The HIPAA-ready option sits on the contact-sales Premium plan

Pricing

Essentials starts at $530 a month (4,000 monthly active users, up to 200,000 messages), and Standard starts at $6,360 a month. Premium is custom. IBM notes prices are indicative and vary by country.

7. Microsoft Copilot Studio: Best for Organizations Standardized on Microsoft

What it does: Microsoft Copilot Studio is a low-code platform for building internal and customer-facing agents, with customer-service voice running through Dynamics 365 Contact Center. It absorbed Power Virtual Agents.

Best for: Organizations already on Microsoft 365, Dynamics 365, Azure, and Entra.

If your company runs on Microsoft, Copilot Studio is the path of least resistance. It connects to 1,500+ data connectors and MCP servers, routes governance through Entra, Defender, and Purview, and gives you access to thousands of models through Azure AI Foundry.

Real-time voice agents became generally available in North America through Dynamics 365 Contact Center in April 2026. Virgin Money built its “Redi” agent on Copilot Studio and reports that 97% of customers who engage with Redi’s outbound messages complete the journey.

Key Features

• Channels: websites, mobile apps, Teams, and voice through Dynamics 365 Contact Center.

• Real-time voice agents: speech-to-speech agents with DTMF input and call transfer.

• Context-intact handoff: in Dynamics 365 Contact Center, full context transfers to human agents.

• Agent evaluations: single-response and multi-turn conversation tests, plus content-safety checks.

Pros and Cons

Pros:

✅ Deep integration with the Microsoft stack and its security tools

✅ Broad compliance coverage, including HIPAA under the BAA, SOC, ISO, and PCI DSS

✅ Huge connector library

Cons:

❌ Customer-facing voice needs Dynamics 365 Contact Center and a phone number set up through Azure Communication Services

❌ Credit-based billing makes costs hard to predict, since one turn can consume several credit types

Pricing

Copilot Studio is sold as packs of 25,000 Copilot Credits for $200 a month, with a pay-as-you-go option through Azure. Generative voice uses 35 credits per minute, which works out to about $0.28 a minute at the pack rate by our math.

8. Amazon Lex: Best for AWS Teams Building on Amazon Connect

What it does: Amazon Lex is AWS's managed service for building voice and text bots, and it plugs natively into Amazon Connect Customer (Amazon Connect's new name), Genesys Cloud CX, and custom apps.

Best for: Engineering teams on AWS that want pay-per-request bots inside an Amazon Connect contact center.

Lex is the bot engine many AWS contact centers already run, and the customer numbers are solid. NAB reports 80% IVR containment, and TransUnion cut IVR time from 2 minutes to 18 seconds. Generative features include a RAG-based FAQ intent through Amazon Bedrock and LLM-assisted slot filling.

Worth knowing: AWS’s 2026 agentic launches, including the agentic CX designer from its NLX acquisition, ship inside Amazon Connect Customer. Lex’s own 2026 updates so far are on the speech side, like a neural English speech model and adjustable voice activity detection, both from January.

Our explainer on Amazon Connect covers that side.

Key Features

• Telephony-ready voice: 8 kHz audio support, barge-in, and wait-and-continue.

• Generative AI features: RAG FAQ answers, assisted slot resolution, and a descriptive bot builder.

• Contact-center integrations: native Amazon Connect, Genesys Cloud CX, and SIP via Amazon Chime SDK.

• Test Workbench: builds audio and text test sets from past interactions.

Pros and Cons

Pros:

✅ Low, usage-based pricing with no minimums, plus AWS Free Tier credits for new accounts

✅ HIPAA eligible, with PCI, SOC, and ISO compliance

✅ Proven at contact-center scale inside AWS

Cons:

❌ AWS cloud only, with back-end logic written in Lambda

❌ New agentic investment is going into Amazon Connect Customer

❌ Many advanced features are English-only. Custom vocabulary works in US and UK English, and the Automated Chatbot Designer works in US English only.

Pricing

Lex costs $0.004 per speech request and $0.00075 per text request. Our Amazon Lex pricing breakdown covers streaming rates. Amazon Connect Customer charges $0.038 per voice minute plus standard telephony rates, with all AI capabilities included in the channel price.

9. Rasa: Best for Developer Teams That Need Full On-Prem Control

What it does: Rasa is a developer-first platform for building customer-facing chat and voice agents that enterprises run on their own infrastructure, using CALM to pair LLMs with structured flows.

Best for: AI product teams in regulated industries that want to own the stack and the data.

Rasa's approach is simple to explain: "The LLM interprets what the user wants. The logic decides what happens next." That keeps business logic deterministic and cuts unnecessary LLM calls, and it's on-prem by default, including air-gapped environments.

Rasa relaunched its platform in September 2026 with Mantle, a new orchestrator for what it calls an "agent factory." Swisscom doubled automation in 20 weeks and cut costs 50%.

Key Features

• CALM: structured, versioned flows with built-in conversation repair.

• Voice: sub-second latency from end of speech to response, with your choice of ASR and TTS.

• Simulation testing: runs agents through simulated conversations in voice and text.

• Governance: SSO, RBAC, PII management, and turn-by-turn structured logs.

Pros and Cons

Pros:

✅ Full control over deployment and data, with no customer data hosted by Rasa

✅ Free Developer Edition that can run in production

✅ Forrester gave it top scores in pricing flexibility and transparency

Cons:

❌ Engineering-heavy: self-managed deployment means Kubernetes, Helm, Redis, and Kafka

❌ The phone IVR connector (AudioCodes) is a paid add-on

❌ No third-party certifications stated; Rasa says its controls are aligned with ISO 27002

Pricing

The Developer Edition is free for one bot per company, with up to 1,000 external (or 100 internal) conversations a month.

10. LivePerson: Best for Messaging-Heavy Customer Engagement

What it does: LivePerson is a conversational AI platform for orchestrating AI agents and human agents across messaging channels, now part of SoundHound AI after an acquisition that closed in September 2026.

Best for: Brands that run most customer conversations through messaging apps like WhatsApp, Apple Messages for Business, and SMS.

LivePerson's Conversational Cloud covers an unusually long list of messaging channels and powers nearly 1 billion conversational interactions a month, per the company. Its newer Syntrix product simulates customer interactions to stress-test flows, handoffs, and guardrails before launch.

Key Features

• GenAI agents: KnowledgeAI Agent, hallucination detection, and bring-your-own LLM.

• Human-AI orchestration: agents can review and approve AI responses before they're sent.

• Integrations: 50+ APIs, a Salesforce connector, and connectors for third-party bots.

Pros and Cons

Pros:

✅ A long list of native messaging channels, from WhatsApp to LINE and RCS

✅ States SOC 2, HIPAA via BAA, PCI DSS 4.0, and HITRUST

✅ Pre-launch simulation testing available through its separate Syntrix product

Cons:

❌ Mid-integration into SoundHound's platform, so expect roadmap churn

❌ Voice leans on integrations more than native telephony

❌ Real costs depend on GenAI token entitlements and channel fees

Pricing

LivePerson sells Bronze, Silver, and Gold tiers on a quote basis.

11. Sprinklr: Best for Enterprises Already Running Sprinklr

What it does: Sprinklr is an AI-native customer experience platform whose AI agents and Sprinklr Service product handle customer conversations across 30+ voice, social, and digital channels.

Best for: Large enterprises that already use Sprinklr for social listening or marketing and want service on the same platform.

Sprinklr's AI agents move across voice, chat, email, and social without making customers repeat themselves, and the platform supports fully autonomous, semi-autonomous, and AI-assisted workflows.

Its Spring ‘26 release added autonomous evaluation for AI agents, and the Fall ‘26 release added voice AI agents and Agent Architects, which builds agents from a plain-language brief.

Umniah cut average handling time by 91% with Sprinklr's AI chatbots, and Gulf Bank brought first response time from 58 minutes to under 6. Sprinklr serves 1,900+ customers, per the company.

Key Features

• Omnichannel AI agents: voice, chat, email, social, and messaging in one agent.

• Guardrails: input and output guardrails plus AI-powered PII masking.

• Testing: simulated conversations and live case replays.

• Routing and analytics: classifies every conversation by intent, urgency, and sentiment.

Pros and Cons

Pros:

✅ Strong fit when social and service run on one platform

✅ SOC 2 Type II, PCI DSS, ISO 27001, and FedRAMP authorization

✅ 100+ pre-integrated applications

Cons:

❌ Conversational AI is one module of a very broad suite

❌ No public pricing

❌ No conversational AI analyst placement stated on its site

Pricing

Sprinklr doesn't publish pricing. Plans are quoted after a demo.

How to Choose a Conversational AI Platform

Here's how we'd narrow 11 options down to the one you sign with.

1. Start With Your Contact Center

The platform has to work with the telephony and agent desktop you already run. If you're on Genesys, NiCE, or Amazon Connect, check the native connector first, because a weak connector turns every handoff into a project. Our explainer on Genesys Cloud is a good reference if that's your stack.

2. Map Your Most Common Conversations

List the five to ten requests that make up most of your volume and mark which ones touch money, identity, or health data. Those are the flows that need deterministic control, and they'll show you quickly which platforms handle them well.

3. Check Governance Before Features

Ask how each platform enforces policy, logs every action, and masks PII. A framework like the NIST AI Risk Management Framework is a useful checklist for those conversations with your security team.

4. Price Your Real Volume

Per-session, per-request, per-credit, and custom licenses are hard to compare on a pricing page. Run your monthly conversation count and average length through each model before you shortlist.

5. Plan for Human Agents Too

The best deployments improve life for the humans on the line, through clean handoffs and agent assist. An NBER study of 5,179 support agents found AI assistance raised issues resolved per hour by 14% on average, and by 34% for novice and low-skilled workers.

6. Run a Pilot With Real Edge Cases

Demos are scripted. Before you sign, put the platform through the conversations that break agents: interruptions, accents, off-script questions, and policy edge cases.

Run each scenario more than once. In Cekura Bench, our public benchmark for voice agents, each of 82 scenarios runs three times on a simulated phone call, and an agent only gets credit when it passes all three.

Even the most reliable voice platform on the current leaderboard clears that bar 87.8% of the time.

Conversational AI Platform Pricing Models

Pricing across this category falls into five models, and most enterprise deals mix two of them:

• Per session or conversation: Google charges $0.50 per session, which makes costs easy to forecast when conversations are short.

• Per request or message: Amazon Lex charges per speech or text request, and LivePerson bills outbound messages at channel-provider rates plus a 15% handling fee.

• Credits or capacity packs: Microsoft sells Copilot Credits, where each action consumes a set number of credits.

• Platform license: Kore.ai, NiCE Cognigy, Boost.ai, and Sprinklr quote custom licenses after a demo.

• Per resolution: Yellow.ai charges $0.99 per resolution after its free allowance.

IBM sits in between, with published monthly plans capped by users and messages. Whatever the model, ask for a quote based on your real volume, and ask what happens to the bill when conversations run long.

Which Conversational AI Platform Should You Choose?

Choose Kore.ai if you're running complex service across many systems and want the strongest analyst credentials right now.

Choose Google Gemini Enterprise for CX if you're on Google Cloud and want simple per-session pricing with testing built in.

Choose NiCE Cognigy if you need agentic AI on top of the CCaaS you already run, whatever it is.

Choose Boost.ai if you're a bank, insurer, or public agency that needs strict control over voice and chat.

Choose Yellow.ai if you want non-coders shipping agents fast.

Choose IBM watsonx Orchestrate if you want customer-service agents governed in the same place as every other AI agent.

Choose Microsoft Copilot Studio if your company runs on Microsoft and Dynamics 365.

Choose Amazon Lex if you're building inside Amazon Connect on AWS.

Choose Rasa if you need full on-prem control and have engineers to run it.

Choose LivePerson if most of your customers talk to you through messaging apps.

Choose Sprinklr if social, marketing, and service already live on Sprinklr.

Comparing AI agents built specifically for customer service, like Sierra and Decagon? Our Sierra AI vs Decagon comparison covers that newer category.

How to Know Your Conversational AI Agent Works Before Customers Do

Whichever conversational AI platform you choose, most failures show up after launch. A prompt tweak breaks a refund flow, a new STT model mishears account numbers, or a caller interrupts at exactly the wrong moment, and the platform's own dashboards don't always tell you why.

Many of these platforms now ship their own testing, like Google’s evaluations and Boost.ai’s testing studio. Those tools test inside one vendor’s stack, while Cekura runs on top of the platform you pick and adds infrastructure testing and production monitoring.

Cekura runs on top of the platform you pick, with documented integrations for Kore.ai, Gemini Enterprise CX, and Genesys Cloud, plus SIP for platforms that answer a phone line.

If part of your stack runs on a voice agent builder, native integrations work out of the box for Retell, VAPI, ElevenLabs, LiveKit, Pipecat, Bland, and more.

With Cekura, teams can:

• Pre-production testing: Simulate thousands of conversations before go-live, with accents, interruptions, background noise, and off-script turns. Red-team agents with jailbreaks and prompt extraction, and rerun the full suite in CI/CD every time a prompt, model, or provider changes.

• Infrastructure testing: Check latency, turn-taking, and barge-in across voice agents.

• Observability: Score every conversation on accuracy, missed intents, tool-call correctness, and custom metrics. Get alerts when quality, latency, or containment drifts, and replay failed production calls as regression tests.

Cekura is SOC 2-, HIPAA-, and GDPR-compliant: Transcript redaction, role-based access, and audit trails.

Book a demo with Cekura to see how it tests and monitors agents built on your conversational AI platform.

Frequently Asked Questions

What is the best conversational AI platform in 2026?

The best conversational AI platform depends on your stack and scale. Kore.ai holds Leader placements from both Gartner and Forrester in 2026, Google Gemini Enterprise for CX leads for Google Cloud teams, and NiCE Cognigy is the most flexible for existing contact centers.

What is an example of a conversational AI platform?

Examples of conversational AI platforms include Kore.ai, Google Gemini Enterprise for CX, NiCE Cognigy, and IBM watsonx Orchestrate. Each lets a business build AI agents that handle customer conversations across chat and voice and take actions in connected systems.

Is ChatGPT a conversational AI platform?

No, ChatGPT is a general-purpose conversational AI assistant. Enterprise conversational AI platforms add contact-center integrations, human handoff, governance, and analytics, and many of them let you plug in OpenAI's models alongside others.

What is the difference between conversational AI and a chatbot?

Conversational AI understands natural language and takes action across channels, while a basic chatbot follows a fixed script. A conversational AI platform can handle open-ended questions, remember context, and complete tasks like rebooking a flight or updating an account.

Which conversational AI platforms are HIPAA compliant?

Several conversational AI platforms support HIPAA, including Google CX Agent Studio, Microsoft Copilot Studio, Amazon Lex, and LivePerson through business associate agreements.

Kore.ai and Yellow.ai also state HIPAA compliance, and IBM offers a HIPAA-ready option on its Premium plan. Confirm current terms with each vendor.

Do I need developers to use a conversational AI platform?

Not always. Yellow.ai and Microsoft Copilot Studio are built for low-code and non-technical builders, while Rasa and Amazon Lex expect engineers. Most enterprise platforms sit in between, with visual builders for flows and code for integrations.