If your call center is under pressure from rising volumes and slow resolution, contact Nova to help you operationalize generative AI in your live contact center environments without disrupting your existing systems.
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Rising call volumes, slow resolutions, and uneven service make it hard to keep pace. This is especially true as customer expectations grow faster than your team can respond.
Agents lose time digging for context, and burnout rises when repetitive work crowds out real problem-solving. So, you might be in need of generative AI call centre companies that can help steady the load and improve operational control.
In this guide, you’ll compare the top 10 leading platforms and see which ones deliver real gains. You’ll also spot the tradeoffs that matter.
Let's get started!
GenAI call center companies are:
They apply large language models to support automation and agent-assisted workflows across the contact center, both for customers and agents. They can help with routine requests, assist with complex conversations, and surface context in real time through agent assist, sentiment analysis, and call routing.
GenAI is in demand now more than ever. According to Cresta’s 2024 agent survey, 65% of agents want real-time AI help on calls, while 81% say AI support improves confidence when shifting from service to sales. That shift to GenAI can change your conversion outcomes.
There are more reasons why GenAI call center companies matter. That leads us to our next point.
Running service at scale creates pressure across cost, speed, and consistency. When implemented correctly, GenAI can help enterprises address rising volumes, cost pressure, and service inconsistency. When implemented poorly, it introduces new friction, inaccurate responses, and broken handoffs.
So here are the core reasons good providers can make or break real operations:
These are just a few ways generative AI is applied in call centers by solid providers. Many teams follow similar contact center adoption trends as they scale automation and reduce cost pressure.
But here are five more capabilities that you should know:
Now, let’s move on to how the top platforms were selected.
Choosing the right platforms requires more than feature lists. The focus stays on how each option performs under real pressure and how it fits enterprise constraints.
Here are the criteria used to evaluate this list:
Pro tip: Are your customer support systems ready to handle retail traffic spikes across voice, chat, and post-purchase support? If not, you can compare how teams approach this in our GenAI eCommerce development guide across retail environments.
With all of this out of the way, let’s move on and compare the companies that made the list.
The top GenAI call center companies are Nova, AI Superior, Cresta, VoiceSpin, C3, and others. These platforms differ based on how they perform in live environments and where they fit enterprise needs.
Here are all of the companies worth examining more closely, starting with the leaders and moving through the broader field.
Nova supports the design and deployment of GenAI contact center systems that run in production. You work with us when you need real control over how AI systems perform across voice and messaging channels, especially with Amazon Connect and generative AI.
Our team at Nova designs serverless architectures on AWS using Amazon Bedrock, Amazon Lex, Lambda, and retrieval-based knowledge layers. We do this so that routine issues can be addressed automatically while complex cases are routed to human agents at the appropriate moment.
And this focus shows up in live results.
In a telecom deployment for Diri, we helped the company reduce customer wait times from 43-80 minutes to near-immediate response. Meanwhile, our team redirected agent capacity away from repetitive Tier-1 calls and into higher-value interactions that require judgment and empathy.
If you want similar results for your contact center, Nova can support your contact center implementation!
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Website: novacloud.io
AI Superior is a Germany-based AI services firm founded in 2019 that builds custom voice and speech systems for enterprise use. Its work typically centers on conversational AI, speech recognition, text-to-speech, and voice bots that plug into existing call center solutions.
Also, delivery follows a project-based model, with a focus on data preparation, model selection, and controlled rollout rather than packaged products. In practice, this approach fits teams that want custom speech pipelines instead of prebuilt platforms.
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Website: aisuperior.com
Cresta is an enterprise platform built for applying generative AI inside contact centers. The system combines an automated AI assistant for routine interactions with real-time guidance for agents during live calls.
At the same time, speech analytics reviews conversations across voice and chat to surface patterns tied to coaching and quality assurance. Deployment typically connects with existing CRM systems, which keep context available during handoffs and follow-ups.
The company applied this approach in several financial services environments. In one deployment, the platform supported broader self-service coverage. Meanwhile, it gave supervisors visibility into agent behavior across channels.
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Website: cresta.com
VoiceSpin is a cloud-based platform that combines outbound and inbound automation for sales and support operations. The system includes virtual assistants for voice and chat, predictive dialing for outbound campaigns, and routing logic that manages inbound requests across channels.
Meanwhile, it has speech monitoring that tracks tone and keywords during live calls to feed structured customer data back into connected systems. This setup usually appears in environments where outbound volume and speed matter as much as inbound coverage.
In one case study, VoiceSpin automated dialing and call timing. That changed how agents engaged leads and supported higher throughput across daily campaigns.
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Website: voicespin.com
C3.ai applies generative AI across large operational systems, including customer support environments. For contact centers, the company provides packaged applications that apply natural language models to answer questions using customer interaction histories and enterprise data sources.
The system relies on agent-based orchestration to retrieve records, reason across documents, and return grounded responses inside existing CX stacks (if that’s how you integrate the platform). Governance and validation layers sit alongside this logic to support controlled use in various enterprise environments, including regulated settings.
C3 once supported a global financial services organization by shortening the time agents spent searching internal systems for information. This changed how cases were handled during live support sessions, because the agents could focus on support rather than research.
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Website: c3.ai
If you need predictable GenAI behavior inside Amazon Connect, contact Nova to automate Tier-1 calls, guide agents in real time, and keep service consistent as demand scales.
Replicant provides a conversational AI platform built for voice and chat automation in contact centers. The system uses AI agents trained on large volumes of platform and customer interaction data (including historical conversations) to handle routine requests. It can also transfer calls to humans based on intent or context.
Voice and chat agents operate without fixed scripts and rely on conversation history to guide responses.
For example, Replicant once automated a large share of payment-related calls for an enterprise support team. This changed how agents allocated time across routine and complex cases.
Alongside automation, the platform provides conversation intelligence and QA analytics to support monitoring and review workflows.
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Website: replicant.com
Avaya is a long-standing enterprise communications vendor with a contact center platform that now supports AI-driven orchestration. The Avaya Infinity Platform brings voice, digital channels, and routing into a single environment that runs in cloud, on-prem, or hybrid setups.
AI integration happens through AI‑agnostic orchestration layers that let enterprises include multiple models and partner tools inside existing call flows. Virtual agents, IVR, recording, and performance monitoring remain part of the same system, which keeps legacy operations intact while adding automation paths.
As such, in one case study, Avaya expanded the automated call handling of a public-sector healthcare operation. This reduced the share of interactions reaching live agents while maintaining service continuity.
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Website: avaya.com
Level AI is focused on conversation analysis and automated quality workflows inside contact centers. The system applies generative and semantic intelligence models to review voice, chat, and email interactions against predefined scorecards and policies.
Call scoring, disposition tagging, and sentiment tracking run continuously across recorded conversations rather than sampled sets. At the same time, real-time insights surface during live calls to guide responses and capture outcomes for reporting.
For example, Level AI once automated quality review and coaching workflows for a large support organization. This changed how audits and performance feedback were handled across teams.
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Website: thelevel.ai
Observe AI centers on speech analysis, transcription, and agent coaching inside contact centers. It applies multi‑model AI (including contact-center-trained language models) to review calls, generate summaries, and surface guidance during live conversations.
Quality review runs across recorded interactions rather than samples, while post-call analysis feeds dashboards used for coaching and operational review. Knowledge retrieval via Knowledge AI pulls answers from internal documents and policies during live sessions, which keeps responses aligned with internal rules.
Observe AI supported an insurance provider by automating quality review and coaching workflows. This changed how quickly leadership identified issues and adjusted service processes.
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Website: observe.ai
Qualtrics operates an experience management platform that extends into contact center environments through analytics and workflow automation. The system ingests interaction data from voice, chat, email, and surveys. Then it applies AI models to score sentiment, compliance, and effort across every conversation.
Insight layers sit above this data. In fact, Qualtrics Assist and related analytics let users query and explore interaction data with AI. Also, automated workflows trigger follow-up actions based on predefined thresholds, which link contact center activity to broader experience metrics.
The platform focuses on measurement, analysis, and response orchestration rather than direct call handling or routing.
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Website: qualtrics.com
Selecting a provider shapes how automation fits into daily operations and long-term plans. Here are the factors that matter most once vendor shortlists start to look similar:
Pro tip: You need to prevent AI-generated responses from drifting when policies, knowledge bases, or CRM data change. How do you do that? Our guide explains how retrieval-augmented generation on AWS Bedrock helps keep responses grounded in your own data.
Next, it makes sense to look at the challenges teams face during adoption.
Rolling out GenAI changes how service teams work, but gaps appear fast when foundations are weak. So here are the issues that most often slow progress once pilots move into production:
1. Hallucinations.
AI models can produce confident but incorrect answers when context is missing or poorly grounded. That risk grows in complex cases and forces rework or escalations.
2. Poor data hygiene.
Outdated policies, conflicting records, or incomplete histories feed bad inputs into the system. As a result, responses drift and trust erodes across teams.
3. Legacy CRM integration.
Older systems typically limit real-time data access. This breaks context sharing and forces agents to verify information manually during live calls.
4. Regulatory concerns.
Data residency, retention rules, and audit trails require tight controls. Without them, rollout stalls in regulated environments.
5. Need for human oversight.
Automation still requires review paths, escalation logic, and ownership. Without clear guardrails, errors can compound instead of resolving.
Across this list, the differences come down to ownership boundaries, operational control, and how AI systems perform under real pressure. Some platforms optimize parts of the workflow, while others influence how calls move, how agents work, and how data stays consistent.
Those tradeoffs affect cost, risk, and service quality over time. So the right choice depends on how much automation you want, how complex your stack already is, and how much control your team needs in production.
If you’re aiming to support systems designed around real call volumes and enterprise constraints, Nova is the place to start the conversation. Contact us today to learn more.
Generative AI is used in call centers because it supports routine work at scale while keeping responses consistent. As a result, teams spend less time on repetition and more time on complex customer issues.
GenAI is safe for customer service when controls like grounding, logging, and human review are in place. Without those safeguards, risk increases through incorrect responses and compliance gaps.
Generative AI is used for call automation, agent assistance, summaries, quality review, and knowledge retrieval. According to McKinesey, this can reduce manual effort by up to 50% across the interaction lifecycle.
GenAI does not replace call center agents because human judgment is still required for complex and sensitive cases. Instead, automation can shift agent effort away from repetitive work and into higher-value interactions.