Conversational AI provider NLX raises $5M to enhance voice-driven customer support

Effective AI solutions should be built with a methodology that accounts for the infinite ways customers speak, not just the happy path of a given call type. In addition, a high resolution rate is only achievable with features that ensure reliability, scalability and security. Embedded enterprise measures include AI guardrails that protect caller data, high-reliability infrastructure and built-in redundancy to manage spikes in call volume. These features are essential to not only maintain a high resolution rate but prevent a solution from hallucinating, experiencing outages or harming your brand. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.

Companies empowered by this level of CX data can generate insights that help them on a variety of fronts. Doing so greatly increases the chance of sales success, moving people down the sales funnel from prospect to customer or from customer to repeat customer, Boyd adds. “We’re going to grow in ways that resonate with a more digitally forward consumer, and a key part of that will be embracing AI to help improve the member experience,” said WeightWatchers CEO Sima Sistani.

Cresta’s decision to market its tools directly through Zoom is a good indicator of how these tools are becoming more of a commodity. Instead of being a product on its own, the company is blending into the feature set of other platforms. The move is unsurprising as Zoom was one of the investors in Cresta’s $80 million series C round led by Tiger Global back in March.

What most customer support leaders don’t understand is that conversational AI is more than just a chatbot. Instead, it spans the entire customer support journey and can provide immediate ROI, retain customers, and keep agents happy. Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a powerful ecosystem of industry-leading digital media brands, with a reach of 15+ million elite tech professionals. The company’s new, proprietary theCUBE AI Video cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.

Conversational AI provider NLX raises $5M to enhance voice-driven customer support

DIY solutions could take nine months to a full year to get into production, and many never do. While every AI solution is deployable in theory, the proven success of a solution’s delivery team is crucial to give buyers full peace of mind. Look for solutions that offer support from experienced, onshore engineers who understand the nuances of conversational AI and are available when and where you need them. NLX provides organizations and technical decision-makers with a solution for automating customer support so that users can resolve problems quickly without needing to contact a support agent. The organizations’ flagship solution, Voice Compass, is a voice-driven self-service product that verbally guides customers through an onscreen journey to complete tasks, including everything from booking flights online to changing an account password.

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After seeing the full range of AI approaches play out firsthand, here are the principles of conversational AI buying I believe every leader must know. Now, machines can not only better understand the words being said, but the intent behind them, while also being more flexible with responses. “That means we can create much more sophisticated virtual assistants or customer care agents, whether they are text-based or voice-based,” Sutherland said. “\With the ability to create and manage all your call conversations in a central, low-code environment, and by leveraging multiple modalities in synchronization, Voice compass helps resolve inquiries that would normally require human support,” Papancea said. However, NLX is aiming to differentiate itself from taking a low-code approach that enables organizations to manage their AI-driven support strategy from a centralized location. Conversational AI platform Parloa has nabbed $66 million in a Series B round, a year after it raised $21 million from a swathe of European investors to propel its international growth.

Can the solution do that all in English, Spanish and Canadian French without being tripped up by loud background noise? The ability to manage complex, multiturn interactions and adapt to different contexts is vital for comprehensive AI support. Aside form lead investor Altimeter, Parloa’s Series B saw checks from EQT Ventures, Newion, Senovo, Mosaic Ventures and La Familia Growth. Today’s funding brings the company’s total capital raised to $98 million, following its $21 million Series A, which was led by EQT Ventures, in 2023. Parloa is well positioned to capitalize on the «AI with everything» hype that has hit fever pitch these past couple of years as companies seek new ways to improve efficiency through automation.

• Overall, the conversational AI market in the customer service space is divided into three key categories, Roberti explained.
• This allows AI agents to be contextually aware of how to resolve customer service needs, such as if a customer wants to know when a store is open, how to find directions or how to open a ticket for a return.
• Conversational AI can also be used to generate more sales or increase existing order values.

They remain focused on supplementing the agent seat model rather than overcoming it. They often focus on marginal improvements rather than comprehensive AI-driven transformations and can minimally reduce agent call volumes. Many of those solutions focus on routing or deflection versus full call resolution.

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Put your brand in front of 10,000+ tech and VC leaders across all three days of Disrupt 2025. Integration with other CRM, analytics, and related technologies boosts the success for companies using conversational AI, Hakim says. But, as with any modern CRM system or other business application, conversational AI cannot be used in a vacuum, experts agree. Conversational AI works best when it can pull information from and feed information to other business systems of record.

• Selecting an AI solution involves more than just ticking off a list of features.
• While many point solutions can show impressive demos, they don’t have depth, resilience or guardrails against hallucinations.
• The announcement comes as more enterprises are looking to AI-driven customer support to offer a compelling customer experience, with AI expected to power 95% of all customer interactions by 2025.
• If a customer expresses joy after a product purchase, AI can respond with an upsell offer and collect more acute and actionable feedback for future customer journeys.
• Agents are also designed to remain authentic and understanding even when customers are emotional.
• Download this white paper and gain insights into how to leverage Conversational AI in your contact center to drive better, more efficient experiences for customers and agents alike.

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Regularly review the performance of your AI and make adjustments based on user feedback and changing business needs. This benefits both your customer service team and your customers, creating a mutually advantageous situation. Don’t think of AI as a faceless, emotionless robot; envision it as a versatile tool that can tackle a wide array of customer service tasks with precision and scale. In essence, it’s like having a tireless, always-on-point customer service representative who doesn’t require coffee breaks or sick days. A PwC study reveals that 73% of individuals consider customer experience a vital factor in their purchasing decisions. Selecting an AI solution involves more than just ticking off a list of features.

When evaluating AI solutions, it’s crucial to focus on features that contribute to a high first-call resolution rate. This starts with accuracy and human-like experiences, which allow a solution to fully complete requests and prevent callers from escalating to an agent. Generative AI can maximize intent recognition and understand complex contextual utterances while also offering low latency and natural voices. Roberti cites two primary types of buyers in the market for conversational AI tools for customer service and support. First, there are buyers who own the contact center or customer-facing support systems.

Agentic AI vs generative AI: why the futures not just smarter its bolder

You give it a direction—“improve customer churn”—and it starts to act. It looks at retention data, cross-checks CRM logs, generates hypotheses, triggers outreach campaigns, and, crucially, updates its approach as new data rolls in. Agentic AI uses reasoning, decision-making algorithms, and environment-based data to act and adapt.

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However, public research on audio recognition and emotional audio generation remains limited. Get insights and exclusive content from the world of business and finance that you can trust, delivered to your inbox. The future of AI factories isn’t just technical—it’s democratized, collaborative and fundamentally human-centric in its design, ensuring that anyone with domain expertise can contribute to the agent economy regardless of their coding background. It’s this final capability—turning understanding into action—that makes agents the highest-order output of AI factories. When McKinsey projects that AI could add $4.4 trillion annually to the global economy, they’re not referring to passive intelligence or token production alone, but to the automated execution that agents enable across industries.

This is more than mere automation — it’s intelligent, proactive management and autonomous automation of complex tasks. Beyond invoice handling, AI agents can significantly ease tasks such as account reconciliations, audit preparation, fraud detection and cash flow forecasting. By automating these critical yet repetitive processes, accountants not only save time and resources, but they also dramatically reduce the risk of human error, gain real-time visibility into financial health, and enhance their responsiveness to financial anomalies. Ultimately, this means accountants can shift their focus from managing day-to-day operations to more strategic roles, offering deeper insights and advisory services that drive greater business value.

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Modern AI factories represent the culmination of this evolution—producing autonomous agents that convert intelligence and tokens into direct action. Unlike previous outputs that inform decisions, agents execute them, closing the loop between insight and outcome. This means implementing frameworks that monitor agent behavior, explain their decisions and maintain compliance with regulatory requirements. At any moment, this system should be able to produce reports that provide total transparency as to what their agents have done and why they took those actions. Underpinning any proper agent architecture is a comprehensive governance layer that must ensure all AI agents are closely tracked, fully auditable and completely secure. A bank could never tolerate an AI agent approving a loan for one person while declining the same loan for another person with largely the same application credentials.

• These research approaches are now out of university labs and are available in public domain for everyone to try in the form of new models.
• Imagine an operations department where AI isn’t just used in workflows but actively manages them.
• What’s needed now is global dialogue on standards, data governance, and sustainable implementation, and WHX Tech provides the ideal platform for that,” said Dr. Shah.
• If you’ve ever played around with any LLM like ChatGPT, try to ask it the same question twice and see what happens.

Regulatory compliance simply doesn’t have room for creative interpretation. This is precisely the risk holding back many AI agent deployments today. Finally, it must optimize those workflows as it moves through its processes. This means it should be able to detect when a better approach is possible on the fly and then implement that change—if and when a human approves. The same poll question found that 50% of respondents said they were researching and experimenting with the technology, while another 17% said that they had not done so, but planned to deploy the technology by the end of 2026 at the latest.

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“Technology only works when it fits into the everyday workflows of real people. Our studies show that nearly half of healthcare workers struggle to understand the tools meant to empower them. At WHX Tech, we’re championing inclusive design, digital literacy, and public-private collaboration to build trust and scale adoption. AI-powered agents are capable of taking on a diversity of roles.

• What’s missing is an intelligent orchestration layer to ensure all agents are working together, acting in the organization’s best interests instead of freelancing as they see fit.
• By updating its virtual assistant’s core natural language processing engine to the latest GPT models, Air India achieved 97% automation in handling customer queries, significantly reducing support costs and improving customer satisfaction.
• This progression isn’t about discarding earlier outputs but integrating them.
• AI agents’ use of natural-language processing also changes the equation.
• Integrated seamlessly into familiar workflows, AI agents will quietly amplify efficiency and effectiveness while minimizing complexity for users.

These agents, when applied to consumer use cases, start giving us a sense of a future where everyone can have a personal Jarvis-like agent on their phones that understands them. Want to book a trip to Hawaii, order food from your favorite restaurant, or manage personal finances? The future of you and I being able to securely manage these tasks using personalized agents is possible, but, from a technological perspective, we are still far from that future. A majority of 1,100 tech executives (82%) responding to a recent survey from consultant Capgemini indicated they intend to integrate AI-based agents across their organizations within the next three years — up from 10% with functioning agents at the current time.

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And that’s just the tip of the iceberg when it comes to other security threats companies are dealing with in 2025. We’re going to take a look at the current security threat landscape on this episode of Today in Tech. It’s about giving them back the 40% of their day they spend nudging, chasing, checking, and… sighing. Such advanced capabilities are driving rapid growth in the AI agent market, expected to expand from $5 billion today to approximately $47 billion by 2030, according to a study by ResearchAndMarkets.com. We continue to hear about the latest and greatest model launches from usual suspects like OpenAI, Cohere, Anthropic and Mistral.

«Currently, to automate a use case, it first must be broken down into a series of rules and steps that can be codified,» the McKinsey team said. NVIDIA continues to lead the charge in AI infrastructure, with predictions indicating a shift towards quantum computing and liquid-cooled data centers. Quantum computing advancements, particularly in error correction techniques, promise to enhance computational power and efficiency, addressing instability issues that currently limit quantum hardware. Performance and cost efficiency are further amplified by NVIDIA TensorRT-LLM optimizations, now applied to popular Meta Llama models on Azure AI Foundry. These include Llama 3.3 70B, 3.1 70B, 8B, and 405B, delivering immediate throughput and latency improvements—no configuration required. Imagine a future where an AI agent not only books your next vacation but also helps provide a shopping list based on your destination, weather forecast, and the best deals from around the web.

And when you hand off autonomy, even partially, you’re entering a zone that demands trust and control. Agentic AI is no longer just a concept; it’s quietly proving its worth across industries, paving the way for a future where technology doesn’t just assist but acts. These research approaches are now out of university labs and are available in public domain for everyone to try in the form of new models.