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𝗧𝗵𝗿𝗶𝗹𝗹𝗲𝗱 𝘁𝗼 𝘀𝗵𝗮𝗿𝗲 𝘁𝗵𝗮𝘁 𝘁𝗵𝗿𝗲𝗲 𝗼𝗳 𝗼𝘂𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗮𝗽𝗲𝗿𝘀 𝗵𝗮𝘃𝗲 𝗯𝗲𝗲𝗻 𝗮𝗰𝗰𝗲𝗽𝘁𝗲𝗱 𝗮𝘁 𝗘𝗠𝗡𝗟𝗣 𝟮𝟬𝟮𝟱 — 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱’𝘀 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗡𝗟𝗣 𝗰𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀! 🎉 This milestone reflects our commitment to building 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗮𝗿𝗲 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲, 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝗰𝗼𝘀𝘁-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 for real-world customer service applications. Our work spans: 1️⃣ 𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗯𝗶𝗮𝘀 in AI-generated summaries to ensure fair, factual, and compliant outcomes. 2️⃣ 𝗠𝗮𝗸𝗶𝗻𝗴 𝗔𝗜 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗮𝗻𝗱 𝗰𝗵𝗲𝗮𝗽𝗲𝗿, improving accuracy while cutting inference costs by over 90%. 3️⃣ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗤𝗔, delivering enterprise-level quality with nearly 50% lower operational cost. Huge congratulations to our incredible research team for pushing the boundaries of what’s possible in AI for customer experience. 🚀

𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗹𝗼𝗼𝗸𝗲𝗱 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲 “𝗲𝗮𝘀𝘆” 𝗟𝗟𝗠 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 𝗶𝗻 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗿𝘃𝗶𝗰𝗲. When LLMs first hit the market, every Customer Service vendor (CCaaS, CRM, and beyond) rushed to launch call summarization to boost agent productivity. Take the transcript → drop it into an LLM → get a summary. Easy, right? 👉 Not really. At 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝗰𝗮𝗹𝗲 (millions of calls, downstream workflows, compliance needs), the bar is much higher: • Accuracy & completeness • Speaker balance • Consistent linguistic style • Bias mitigation across dimensions That’s why our team built 𝗕𝗹𝗶𝗻𝗱𝗦𝗽𝗼𝘁 — a framework to uncover and mitigate hidden biases in AI-generated summaries across five key dimensions, ensuring outputs are accurate, fair, and business-ready. Another reminder that: 𝗗𝗲𝗺𝗼𝘀 𝗮𝗿𝗲 𝗲𝗮𝘀𝘆. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 𝗶𝘀 𝗵𝗮𝗿𝗱.

Most AI projects fail not because the model isn’t smart enough - but because it’s aimed at the wrong interactions. Learn how to build an AI strategy that goes beyond simple L1 tasks and addresses high-value, complex L2 and L3 issues, where customer experience and ROI are won or lost.

Legacy systems. Scattered data. Outdated knowledge bases. These aren’t blockers - they’re starting points. VoiceAI agents learn directly from real calls to automate support interactions and surface improvements, no perfect taxonomy required.

Recent research by IDC found that 𝟴𝟴% 𝗼𝗳 𝗣𝗢𝗖𝘀 𝗱𝗶𝗱𝗻’𝘁 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗶𝗻𝘁𝗼 𝘄𝗶𝗱𝗲𝘀𝗰𝗮𝗹𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, with CIOs struggling to define what POC success even looks like. Many 𝗖𝗜𝗢𝘀 𝗮𝗿𝗲 𝗻𝗼𝘄 𝗽𝗶𝘃𝗼𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗶𝗻-𝗵𝗼𝘂𝘀𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀. Why? Because while building a POC with a large language model (LLM) and some clever prompting is relatively straightforward, scaling it into a 𝗿𝗼𝗯𝘂𝘀𝘁, 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗯𝗮𝗹𝗹𝗴𝗮𝗺𝗲. Achieving 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆, 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗶𝗻𝗴 𝗰𝗼𝘀𝘁 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝘆 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 at scale requires more than just technical know-how — it demands deep domain expertise. This pattern is familiar in every new technology cycle. CIOs often believe it’s easy to build in-house, but as they dig deeper, they realize it’s far more complex than it seems. We are seeing the same trend in the Customer Service AI space, with many enterprises now returning to buying solutions after spending the last 12–18 months trying (and struggling) to build internally. 𝗙𝘂𝗹𝗹 𝗮𝗿𝘁𝗶𝗰𝗹𝗲: https://lnkd.in/gFRfYvce

Improving NPS is one of the hardest challenges Enterprises face. Thrilled to share how we partnered with Concentrix to help Pearson improve NPS by a massive 25 points leading to improvement in customer retention and Life time value. Pearson achieved this by transforming their contact center from a cost center to a strategic business center with a focus on improving CX and driving higher cross sells and upsells. They used Observe.AI to analyze 100% of the customer conversations to identify customer pain points, areas of opportunity to drive higher cross sell and agent performance and coaching opportunities. Read more about the joint partnership between the three companies Observe.AI Concentrix Pearson here - https://lnkd.in/gHXZCPg7