Parloa’s Post

This is what it actually takes to put AI to work in enterprise customer service — not just demos, but production-grade reliability at scale. Proud of what the team at Parloa is building. Worth a read 👇

🚀 A must-read for everyone building AI agents in enterprise customer service. The new OpenAI x Parloa article gives a behind-the-scenes look at how scalable, multilingual, enterprise-grade AI agents are actually built, and why reliability matters more than hype. ➡️ Link: https://lnkd.in/g7P6AJrg What makes this especially interesting is the focus on real-world production environments: 🔹 AI agents are simulated before deployment 🔹 Customer conversations are stress-tested at scale 🔹 Models are evaluated with LLM-as-a-judge systems + deterministic checks 🔹 Voice AI performance is continuously optimized for latency, accuracy, and trust 🔹 Enterprise teams can build and manage AI agents without heavy coding For pharma & life sciences, this is highly relevant. 💊🧬 Patient engagement, medical information services, adherence programs, and healthcare customer support require AI systems that are: ✅ Reliable ✅ Compliant ✅ Multilingual ✅ Safe for sensitive conversations ✅ Trusted by patients and healthcare professionals One of the strongest takeaways from the article: Enterprise AI success is not defined by benchmark scores alone, it’s defined by consistent performance in real customer interactions. Also impressive: Parloa’s AI Agent Management Platform (AMP) already supports millions of conversations across industries including retail, travel, and insurance. This is where the future of customer experience, healthcare communication, and enterprise AI is heading. #Parloa #OpenAI #CustomerService #ConversationalAI #GenerativeAI #VoiceAI #Pharma #LifeSciences #HealthcareAI #DigitalHealth #PatientExperience #EnterpriseAI #AI #CX #HealthcareInnovation #AITransformation #MedTech #FutureOfWork

Awesome to see our work with OpenAI get recognized on their website. Their models power millions of customer conversations for our clients — global brands, customer-centric enterprises — who are reimagining their customer service experiences.

OpenAI just published a story on how we're building our AI Agent Management Platform (AMP) on top of frontier models like GPT-5.4, and what it takes to make them reliable for real-time, multilingual, enterprise-grade customer conversations. Our AI agents power millions of conversations across retail, travel, insurance, and other industries. We need to make sure that each one performs reliably from the first second to the last, because in customer service, every interaction counts. Our approach: We continuously evaluate and stress-test new model iterations in production-like environments before rolling them out to live customer interactions. That means simulating real customer calls before agents go live, evaluating every interaction with a mix of LLM-as-a-judge scoring and deterministic checks, and only deploying models that hold up under realistic conditions, not just on abstract benchmarks. #Parloa #AIInnovation #CustomerExperience

How long does your post-production workflow take you? For us, it was a 3-hour cycle of manual adjustments, mismatched thumbnails, and fragmented handoffs. In Part 3 of our series on operationalizing AI, co-hosts Jenny (JCK) Cotie Kangas and Julia Levy break down the ultimate rule of business automation: Document before you automate. Watch as we take a messy, verbal data-dump of our current state and use Claude to build an objective map of our workflow. By identifying our exact friction points, like the infamous "ID-10-T" manual error, we establish a clean foundation to build smart, automated instructions that actually stick. 💡 The Missing Manual Takeaway: Don't give AI a better version of a bad process. Use AI to improve what you are doing, not just repeat it. Watch the clip below to see the current state mapping in action! 👇 The full episode link is pinned in the comments below. #TheMissingManual #AIForBusiness #Operations #AutomationStrategy #WorkflowOptimization #ContentOps

We’ve all seen the recent cycle in the headlines: massive tech companies rushing to replace their support floors with AI agents, only to quietly backpedal and rehire when customer satisfaction drops. It turns out people still want to talk to people when things actually go wrong. The real value of AI isn't in taking the wheel. It's in being the "Co-Pilot". ThinkOwl operates silently in the background. It extracts the data, runs the sentiment analysis, and surfaces the right policies instantly. Your team doesn't get replaced. They just permanently escape the data-entry loop so they can focus on resolving complex issues with actual empathy. Swipe through the framework. #CustomerExperience #AI #ThinkOwl #Support #CXstrategy

71% of STR hosts are already using AI in their business. 🤖 The question isn't whether to adopt it. It's whether you're being left behind. In 2026, the hosts winning on revenue are the ones leveraging: 📊 Dynamic pricing tools that adjust rates in real time 🤖 AI-generated listing copy that converts browsers into bookers ⚡ Automated guest messaging that responds in seconds not hours Properties using dynamic pricing alone are seeing 15 to 30% more revenue than those on static rates. The tech gap between hosts is widening every month. And the ones leaning into AI and automation are pulling further ahead. 📈 Are you using AI in your STR business yet? What's your favorite tool? 👇 #ShortTermRental #STR #ArtificialIntelligence #AirbnbHost #VacationRental #PropertyManagement #DynamicPricing #PropTech #PassiveIncome #HostingTips

🚨 Lessons from Price Comparison Engines Are Insurers Losing Customer Contact Again? We’ve seen this before. Price comparison platforms pushed insurers into the background — weakening direct customer relationships. Now, LLMs risk doing the same — at a much larger scale. AI agents can compare, recommend, and decide in seconds. If insurers aren part of that ecosystem, they risk becoming invisible. 📊 Already, 44% of users prefer AI over traditional search. 👉 In an AI-driven world, owning the customer interface is everything. What can insurers do? Structure content and products so that LLMs can easily “find”, interpret, and compare them. Provide their own AI agents and APIs instead of leaving the customer interface to third parties. Use specialized AI-native platforms to make both front office and back office AI-enabled. #Insurance #AI #ConversationalAI #InsurTech #CustomerExperience

We’ve been integrating AI into more of our workflows at Creative Arc - like many teams, we’ve seen the expected gains in speed and efficiency. But one unexpected benefit: it’s helping us uncover problems we didn’t know existed. While building an API integration for invoicing, Claude helped surface a rule in our ticketing system that was automatically archiving timesheets after seven days. On the surface, that seemed fine.. except those archived timesheets weren’t accessible to our invoicing process. Whoops. That’s the kind of issue that quietly creates gaps, and can go unnoticed for a long time. We fixed it, of course. But it was a good reminder: AI isn’t just about doing things faster. Sometimes, it helps you see what’s been hiding in plain sight.

This is a great articulation of how we think about AI at Crescendo. Not just as a tool for efficiency, but as a foundation for continuously improving customer experience. Settling for basic automation feels like undershooting the moment. Strong POV from Tod: https://lnkd.in/eicf73Dp