The Best Voice AI Agents for Call Centers for 2025

Introduction

The rise of Artificial Intelligence (AI) has brought many benefits to industries requiring intense manual work. One such example are call centers. 8x8’s survey showed that 94% of respondents reported productivity improvements while 93% believe that it’s important to increase adoption of AI in contact centers. The global AI call center market is projected to grow from USD 1.95 billion in 2024 to USA 10.07 billion by 2032 according to Fortune Business Insights.

In this article, we will cover the various use cases of AI agents in call centers as well as prominent AI agents that can be implemented in call centers.

What are Voice AI Agents in Call Centers?

AI agents are software programs that utilize underlying technologies such as Large Language Models (LLM) and various other machine learning algorithms to independently interact with its environment following a set of instructions provided by humans. It does this by collecting and using data to determine the best action to take.

For everyday customers interacting with AI, chatbots and AI agents answering phone calls are the most visible examples in call centers. However, AI’s role in call centers extends far beyond these customer-facing applications.

AI agents in call centers can also improve internal workflows, from assigning and routing tasks to automating administrative work and performing repetitive analysis based on customer interactions.

How are Voice AI Agents Being Used in Call Centers?

There are many ways that AI Agents can be integrated into contact centers, we will cover 5 of its most popular uses below.

AI Voice and Chat Bot Agents

Virtual AI agents are now more prominent than ever, handling the frontline of customer service. They manage routine inquiries without human intervention. These systems rely on Large Language Models (LLM) to respond to a user’s requests faster, more efficiently, and more cost-effective than humans can.

These systems use Natural Language Processing (NLP) to understand the customer’s intent. Additionally, whether through voice or text, they can handle multiple conversations simultaneously while retaining context and personalization.

When situations become too complex, these AI agents can smoothly transfer conversations to human agents while giving them full context of the inquiry.

Most importantly, modern AI agents can be rapidly trained on company-specific information through techniques like Retrieval Augmented Generation (RAG). Organizations can simply feed hundreds of pages of documentation, policies, and product information to these systems, allowing them to instantly become experts in company-specific knowledge.

With the decreasing costs of AI deployment and the rise of on-premise solutions like LocalAI and Ollama, companies can now implement these powerful systems while keeping sensitive customer data within their own infrastructure.

Real-Time Sentiment Analysis

AI agents analyze every customer interaction across channels to understand emotional patterns and satisfaction levels in real-time. These systems process multiple data points - from voice tone and pitch to word choice and speaking pace - identifying subtle indicators of customer frustration, satisfaction, or confusion that human agents might miss.

By detecting emotional shifts early, agents can adjust their approach before situations escalate. This proactive response leads to better resolution rates and improved customer satisfaction, while supervisors can step in during critical moments to provide additional support.

The real value comes from aggregating sentiment data across thousands of interactions to uncover root causes of customer dissatisfaction. AI analyzes these patterns to identify specific products, policies, or processes that consistently trigger negative responses, helping companies make data-driven improvements to their customer experience.

These analysis tools usually use the underlying machine learning technology of LLMs, transformers.

Intelligent Call Routing

AI systems can analyze incoming customer requests in real-time to match them with the most suitable agents, human or another AI. The routing considers multiple factors simultaneously—from customer history, state intent, to the current workload—by fetching huge amounts of data and performing simple NLP.

These routing systems can also improve themselves by learning from historical data, improving efficiency and accuracy while ensuring customer satisfaction.

During peak hours, AI can dynamically adjust routing priorities to maintain service levels and prevent bottlenecks: so your super important call won’t stay on hold for hours on end!

Post-call Analytics and Summarization

AI agents in call centers transform the tedious process of call documentation into an automated workflow. Within seconds after each interaction, the system will generate detailed information from summaries to customer sentiments presented in a clear, structured format.

These AI tools scan through entire conversations at lightning speed to extract critical information that might otherwise slip through the cracks. By identifying patterns across thousands of interactions, the system spots emerging issues, common customer pain points, and successful resolution strategies that can be shared across the team - all without agents spending hours manually reviewing calls.

Instead of manually shifting through call transcripts (which by the way, are transcribed by AI technologies as well!), contact center supervisors can now review a day’s worth of customer interactions in minutes instead of hours.

Through longer periods of analysis, like sentiment analysis, summarization and call documentation can reveal valuable insights about customers’ needs and areas of improvement for the company.

Again, typically some type of underlying transformer models are used for analyzing text.

Real-time Agent Assistance During Calls

Instead of directly taking important calls, AI agents can act as an intelligent assistant “whispering” helpful suggestions to human agents based on their real-time analysis of the conversation.

During each call, AI agents analyze customer queries in real-time and instantly display relevant information from knowledge bases, previous interactions, and customer profiles—eliminating the need for manual searches.

The technology goes far beyond simple information retrieval. These AI assistants can predict customer needs based on conversation context and past behaviors, suggesting next best actions before agents even have to ask.

When conversations take unexpected turns, the AI adapts instantly to provide new guidance. A simple customer inquiry about billing might reveal an opportunity for upgrade recommendations, or a technical support call could uncover a broader system issue affecting other users—and the AI ensures agents have the right information to handle these shifting scenarios effectively.

Top 7 Voice AI Agents for Call Centers in 2025

1. Deepgram

Deepgram stands at the forefront of both text-to-speech and speech-to-text providers, offering a balance between cost, accuracy, and speed. Deepgram’s blazing-fast APIs can handle real-time transcriptions as well as speech synthesis from text at a remarkable speed and realism. Their Nova-2 model processes speech up to 40 times faster than competitors, while their Aura text-to-speech model delivers natural-sounding AI voices with response times under 250 milliseconds - essential for fluid customer conversations.

Beyond basic transcription and voice synthesis, Deepgram’s AI excels at sentiment analysis, intent recognition, topic detection, and entity extraction. This comprehensive analysis helps call centers track satisfaction trends and identify improvement areas across thousands of interactions, making it a powerful foundation for both fully automated AI agents and human agent assistance.

Finally, Deepgram is extremely cheap, costing only around $0.0043 per minute of audio transcription and $0.015 per one thousand characters for text to speech: up to three times faster than competitors offering cheaper prices and performance.

Users can sign-up and get $200 in free credits without strings attached.

Pros:

• Industry-leading speed and accuracy in speech processing

• Comprehensive analysis capabilities (sentiment, intent, topics)

• Natural-sounding AI voices with ultra-low latency

• Cost-effective for high-volume call centers

• Real-time processing for instant agent assistance

Cons:

• Limited language support compared to competitors (new ones releasing over time)

2. Groq

Groq leads the LLM inference race with speeds hitting 700 tokens per second on Llama 3.1 8B - nearly double its competitors. For call centers, this lightning-fast processing lets AI agents respond to customers instantly and help human agents fetch information on the fly. Through GroqCloud and on-premise GroqRack, teams can process customer queries and pull company documentation while keeping conversations natural.

The platform delivers value with 20 million input tokens per dollar and extends to speech processing, achieving 200x speed-up for models like Whisper V3. This combination of speed and efficiency makes Groq ideal for powering AI voice agents, real-time assistance, and automated routing where every second counts.

Pros:

• Fastest inference speeds in the industry

• Flexible cloud and on-premise deployment options

• Wide selection of popular LLMs

• Highly cost-effective token pricing

• Support for vision models

Cons:

• Limited to basic LLM inferencing and speech-to-text

• Lacks advanced processing features (summarization, sentiment analysis)

• Limitations that current LLMs have, such as hallucinations

3. CallMiner

CallMiner transforms contact centers through AI-powered conversation intelligence. Their platform excels at automating quality assurance, providing real-time agent guidance, and managing brand sentiment across interactions. By identifying at-risk customers in real-time, CallMiner enables immediate action - whether that’s de-escalating a heated conversation or connecting customers with specialized support teams.

CallMiner can transform recorded conversations into clear, actionable insights through advanced analytics and predictive modeling. From campaign management to survey targeting, CallMiner delivers everything modern call centers need in one comprehensive package, complete with intuitive visualizations that make complex data digestible.

Pros:

• Comprehensive suite covering most call center needs

• Powerful real-time analysis during conversations

• Strong visualization tools for insights

Cons:

• Closed technology stack with undisclosed models

• Enterprise-focused with limited individual access

• Significant setup and integration required

4. Twilio Flex

Twilio Flex is a digital engagement platform that layers real-time data and AI capabilities on top of sales and service interactions. Their system focuses on intelligent routing that matches customers with the best communication channels, while pulling customer data on the fly to personalize every interaction through a clean, customizable interface.

The platform’s routing system packs escalation logic and fallback rules, and through integration with Google Contact Center AI, agents receive recommended responses during customer conversations. At $1 per hour of use and a free trial to start, Flex offers straightforward pricing - though costs can add up for bigger teams.

Pros:

• Customizable and intuitive user interface

• Smart channel selection and routing

• Real-time data integration

Cons:

• No automated AI agents/bots

• Per-user pricing expensive at scale

• Limited access to underlying technology

5. Amazon Connect

Amazon Connect brings AI muscle to cloud-based contact centers through Contact Lens and Amazon Lex integration. Their platform combines speech-to-text, natural language understanding, and sentiment analysis - powered by the same tech behind Alexa. From skills-based routing to task management, agents can start handling customer conversations across voice and chat in minutes.

The platform runs entirely on AWS infrastructure, making it quick to deploy and scale. Using native real-time analytics, Connect tracks customer sentiment and interaction patterns while giving agents unified customer profiles and AI-powered assistance. With a pay-as-you-go model starting at a few cents per minute, it’s particularly appealing for smaller operations.

Pros:

• Quick deployment on AWS infrastructure

• Native integration with AWS AI services

• Real-time analytics and sentiment tracking

• Omnichannel support (voice and chat)

Cons:

• Requires AWS expertise for full utilization

• Complex pricing across multiple services

• Limited customization compared to competitors

6. DVS Analytics

DVS Analytics tackles call center optimization through comprehensive interaction recording and analysis. Their platform turns call recordings into searchable data, extracting business intelligence while maintaining compliance and reducing risk. From automated quality management to workforce scheduling, they’ve built tools that help managers make better decisions about their contact center operations.

The platform offers flexible deployment options—SaaS, subscription, or perpetual licensing. Their analytics engine integrates with major communication platforms to pull metadata from CRM systems, turning raw conversations into actionable insights for improving agent performance and customer experience.

Pros:

• Complete interaction recording and analysis

• Automated quality management

• Flexible licensing options

• Real-time agent coaching tools

Cons:

• Complex feature set may overwhelm smaller teams

• Pricing not publicly available

• Requires significant setup investment

7. EvaluAgent

EvaluAgent brings AI-driven conversation intelligence to call center quality assurance. Used by brands like Samsung and Ubisoft, their platform automates QA tasks and provides targeted coaching for agents through customizable workflows. The system analyzes customer interactions automatically, spotting areas for improvement while cutting down on manual review time.

EvaluAgent help build better agents through targeted development resources and coaching tools. By connecting with existing systems and offering out-of-the-box solutions, EvaluAgent streamlines the entire QA process, earning recognition in G2 Fall reports and a Gold Globee Award for contact center solutions.

Pros:

• Comprehensive QA automation

• Customizable workflows

• Proven track record with major brands

Cons:

• Focused solely on QA and training

• Pricing not publicly available

• May be complex for smaller teams

Things to Consider when Implementing Voice AI Agents in Call Centers

Implementing AI agents in call centers can dramatically cut costs while boosting efficiency. But these AI assistants, despite their sophistication, aren’t human and can make serious mistakes. We’ve seen this from the earliest chatbot attempts - like Microsoft’s Tay, which turned racist and inflammatory within hours of launch after learning from Twitter users’ responses.

More recent failures include a Chevrolet dealership chatbot that was tricked into agreeing to sell a 2024 Tahoe for $1, and Air Canada’s support bot that fabricated refund policies and gave out incorrect discount information.

These incidents show that while AI has come a long way, it isn’t perfect. Many customers still prefer talking to human agents for complex issues or when they need that personal touch that AI just can’t replicate.