March 2026 Summaries

Advancements in medical AI are revolutionizing healthcare by transforming drug discovery, diagnostics, and clinical documentation, albeit often without making headlines. Notable achievements include AlphaFold 3's Nobel Prize-winning ability to predict protein interactions, Insilico Medicine's AI-designed drug showing Phase IIa success, and the widespread adoption of documentation tools like Microsoft's DAX Copilot across over 150 health systems, which save physicians significant time on paperwork. Despite high benchmark scores, many medical AI models, like Google's Med-PaLM 2, face a gap between research performance and clinical deployment due to regulatory hurdles. The field is divided between research models with exceptional benchmark performance and practical tools integrated into healthcare systems that focus on measurable benefits. While AI-discovered drugs have yet to receive FDA approval, the establishment of regulatory frameworks and ongoing clinical trials suggest potential future approvals. As AI continues to gain traction in healthcare, the challenge lies in translating research achievements into validated, safe clinical applications.

ElevenLabs' interruption handling capabilities are explored in the context of their application in call centers, where effective voice AI requires the ability to manage interruptions, or "barge-in," amidst challenging audio conditions. The article highlights that while ElevenLabs can manage basic interruption scenarios, it lacks the customization needed for complex environments, such as those with overlapping speech or diverse accents requiring fine-tuned voice activity detection (VAD) and speech-to-text (STT) accuracy. The text discusses the importance of a reliable full-pipeline interruption handling system that includes low-latency streaming, model-driven turn-taking, and noise-adaptive VAD, especially in noisy and high-concurrency settings. It contrasts ElevenLabs' platform with Deepgram's Voice Agent API, which offers more robust solutions for high-volume call centers through integrated STT, orchestration, and TTS with model-driven turn-taking. The article emphasizes the necessity of testing interruption performance under realistic conditions to ensure effective deployment and highlights the trade-offs between using a general platform like ElevenLabs and developing custom STT infrastructure for sophisticated call center needs.

ElevenLabs offers a comprehensive voice agent platform designed to handle speech-to-text (STT), large language models (LLM), and text-to-speech (TTS) within a single session, providing quick deployment and quality voice expressiveness. While the platform is suitable for moderate-volume deployments with standard audio conditions, its effectiveness may be limited by factors such as concurrency limits, lack of on-premises deployment options, and potential latency issues in high-volume or noisy environments. The platform's STT layer, particularly the Scribe v2 Realtime model, delivers high accuracy but may struggle with endpointing, which is crucial for responsive interactions. For contact centers with complex audio needs, decoupling STT from TTS can offer greater control, particularly in multilingual or compliance-heavy scenarios. ElevenLabs' platform is best suited for controlled environments where fast deployment and voice quality are critical, while applications requiring high concurrency and nuanced audio handling might benefit from integrating ElevenLabs' TTS with a specialized STT provider like Deepgram.

Penguin Solutions has announced a strategic collaboration with Deepgram and Dell Technologies to develop an optimized AI inference infrastructure aimed at enhancing enterprise voice AI applications. This partnership utilizes Dell PowerEdge servers and NVIDIA RTX PRO 6000 Blackwell GPUs to deliver robust, low-latency voice experiences crucial for sectors like healthcare and retail. The initiative focuses on addressing the growing demand for reliable and scalable AI systems as enterprises increasingly adopt generative AI technologies. By integrating Deepgram’s advanced voice AI models with Penguin Solutions' expertise in AI infrastructure and Dell's high-performance technology, the collaboration promises to support complex real-time voice applications with high efficiency and accuracy. This joint effort underscores the potential of AI-driven voice solutions to transform customer and patient engagement, backed by a resilient infrastructure that meets strict performance and data governance standards. Attendees of the NVIDIA GTC AI Conference can learn more about this innovative solution at Dell’s booth, highlighting the synergy between these leading tech companies.

ElevenLabs offers a speech-to-text service called Scribe, which functions within their voice synthesis platform and shares resources with other services like text-to-speech (TTS). Scribe provides two main products: Scribe v2 for batch processing with diarization and Scribe v2 Realtime for streaming without it, prioritizing low latency over speaker labeling. The shared credit system means that heavy TTS usage can impact STT capacity, making concurrency and compliance key concerns, especially for enterprises needing HIPAA compliance, which requires a sales-negotiated Business Associate Agreement. While Scribe is advantageous for those already utilizing ElevenLabs' ecosystem, dedicated STT providers like Deepgram may be preferable for applications where transcription is central, as they offer clearer scalability and pricing structures. Evaluating ElevenLabs' STT involves considering its performance under realistic conditions, shared capacity issues, and compliance requirements, with potential trade-offs depending on how integrated your needs are with ElevenLabs' other services.

ElevenLabs faces significant compliance challenges when it comes to handling audio data, particularly for companies embedding its technology into production voice products. The company's data retention policies and privacy controls vary across plan tiers, with the Enterprise tier offering more robust options such as Zero Retention Mode, which minimizes data retention but excludes certain workflows like voice cloning. For non-Enterprise users, data is often used for model training by default, unless proactively opted out, and even then, past data may still be used. ElevenLabs positions itself as a data processor, shifting key compliance responsibilities, including consent and biometric data classification, onto its users, creating potential compliance gaps under laws like GDPR and Illinois BIPA. The article emphasizes the importance of understanding ElevenLabs' data handling practices, from subprocessor notifications to HIPAA compliance conditions, and offers strategies for reducing privacy exposure through careful architecture and consent management, particularly for B2B2B companies whose users' voices constitute the data being processed.

AI contact centers leverage technologies like automatic speech recognition (ASR), natural language understanding (NLU), and intent classification to accurately detect caller intent, which is crucial for routing efficiency and customer satisfaction. These technologies enable contact centers to achieve faster, more accurate first-contact resolutions, which can significantly reduce operational costs and improve customer retention. Integrating sentiment analysis adds emotional context to intent detection, enhancing the prioritization of customer needs. Customizing domain-specific models can further improve accuracy in recognizing industry-specific terminology. Organizations implementing AI-powered intent-based routing have reported substantial improvements, including up to 3x better first-contact resolution rates and millions in annual savings. Evaluating the performance of intent detection systems involves considering accuracy benchmarks, latency requirements, and cost-effectiveness, with real-world testing being essential to ensure systems meet the specific needs of high-volume call environments.

The guide offers comprehensive insights into building, integrating, and scaling AI voice agents for enterprises, emphasizing the importance of production-tested infrastructure to avoid significant accuracy drops. It outlines key components such as Automatic Speech Recognition (ASR), Language Model Management (LLM), and Text-to-Speech (TTS), and discusses their integration with telephony and data infrastructure to ensure efficiency and compliance. The document also covers the challenges and solutions related to latency, compliance (such as HIPAA and PCI), scaling, performance, and cost management. It suggests best practices for designing robust call flows, handling edge cases, and ensuring smooth operation across various industries like healthcare and financial services. Additionally, it provides guidance on vendor evaluation based on production metrics, deployment options, and cost models while addressing common integration pitfalls and troubleshooting techniques.

Deepgram's speech-to-text (STT) technology is now integrated into Together AI, allowing users to manage STT, large language models (LLM), and text-to-speech (TTS) from a single platform. This integration facilitates real-time voice agent operations by reducing latency and improving accuracy, as audio data remains within the same environment for processing. Deepgram's capabilities are particularly suited for real-world audio settings such as contact centers and healthcare, enhancing the user experience by minimizing misunderstandings and awkward conversational pauses. The collaboration offers a streamlined approach, eliminating the need for multiple dashboards and support systems, and is designed to improve the efficiency and effectiveness of voice agents in production environments. Users can either integrate Deepgram within the existing Together AI setup or employ Deepgram's Voice Agent API for more customized deployments, ensuring compliance and multi-region architecture needs are met.

ElevenLabs, when scaled beyond demo usage, reveals critical constraints such as concurrency limits, unpredictable credit burn rates, and compliance challenges, particularly for HIPAA requirements, which necessitate Enterprise-tier contracts. These constraints manifest in various operational failure modes, including session rejections, latency spikes, and potential overages that add significant costs. Runtime-based billing presents a more predictable cost model compared to character-based billing, especially for applications involving high concurrency and variable conversation lengths. For organizations operating in sectors like healthcare, compliance and deployment constraints can lead to extended sales cycles and the need for customized solutions. As ElevenLabs' platform limits can create architectural constraints, especially for real-time voice agents, careful planning and testing are crucial to ensure production readiness, including cost modeling, latency benchmarking, and resilience testing to handle failure scenarios gracefully.

Noise-robust speech recognition techniques face significant challenges when transitioning from benchmark to production environments, primarily due to acoustic variability, latency constraints, and scalability issues. Techniques like preprocessing and multi-condition training are essential for maintaining accuracy in noisy, real-world conditions, as systems optimized for clean audio often suffer 5-10 times worse performance in production. Preprocessing methods such as spectral subtraction and beamforming help manage noise within real-time constraints, while multi-condition training reduces data requirements significantly by leveraging pre-trained models and domain-specific fine-tuning. The article emphasizes that training-based approaches tend to outperform preprocessing methods in achieving noise robustness, especially in environments with unpredictable noise patterns. Evaluation of production-ready systems requires metrics beyond word error rate, including latency percentiles and confidence scoring, to ensure reliable performance under varying noise conditions. Moreover, runtime adaptation techniques face scalability challenges at high concurrency levels, and production systems are increasingly favoring stateless architectures to maintain consistent performance. The article advises on evaluating vendors based on their ability to generalize across unseen noise types and manage latency and concurrency effectively, while also highlighting the importance of testing systems with actual production audio to validate their readiness for deployment.

International Women's Day at Deepgram highlights the essential role of women in shaping the future of artificial intelligence (AI) and the importance of diverse representation in technology. Through personal stories from women at Deepgram, the article emphasizes that increasing women's participation in STEM is vital for creating inclusive, effective, and innovative AI systems. It stresses that various backgrounds contribute to AI development, from engineering and sales to design and operations, underscoring that there is no single path into tech careers. The women at Deepgram, such as Ingrid Elise Dorai-Rekaa and Caitlin Coscoluella, highlight how diverse perspectives lead to more thoughtful and inclusive technologies, especially in areas like voice AI that directly interact with people across different cultures and languages. The narrative encourages young women to explore STEM fields, driven by curiosity and the willingness to try, as their involvement is crucial for unlocking the full potential of future technologies.

The guide offers an in-depth analysis of evaluating Automatic Speech Recognition (ASR) systems, emphasizing the discrepancy between benchmark scores and real-world performance in production environments. It highlights that benchmarks like FLEURS often fail to predict production accuracy due to their reliance on controlled conditions, such as read-speech and clean audio, which do not reflect the spontaneous, noisy, and diverse language conditions of actual enterprise environments. The guide suggests focusing on six metrics beyond Word Error Rate (WER) to predict deployment success, including keyword recall, entity accuracy, latency, speaker diarization, punctuation, and semantic preservation. It advises structuring vendor evaluations around real production audio samples, accounting for specific business needs, language distribution, and conditions like background noise and domain-specific terminology. Additionally, it underscores the importance of factoring in total costs, including integration and ongoing tuning, and recommends continuous performance monitoring and vendor re-evaluation to ensure ASR systems meet production standards effectively.

Deepgram has achieved the top position in German speech recognition accuracy, outperforming competitors in a benchmark of 26 hours of real-world production audio, with a 29% reduction in word error rate compared to the next best competitor. This success is attributed to its Nova-3 model, which excels in both batch and streaming transcription, maintaining low error rates across substitution, deletion, and insertion categories in challenging real-world conditions. Deepgram's commitment extends beyond accuracy, offering flexible deployment options that meet stringent data residency and compliance requirements in Germany, making it an attractive choice for industries such as logistics, finance, and healthcare. The company's strategic focus on the German market and its investment in multilingual model development underscore its leadership in the enterprise-grade voice AI sector.

Flux has introduced a new feature that allows real-time configuration updates during conversations, enabling automatic speech recognition (ASR) systems to adapt to evolving contexts without requiring disconnection or reconnection. This on-the-fly configuration is facilitated through a Configure message in the Flux v2 /listen WebSocket API, allowing users to update keyterms and end-of-turn thresholds dynamically. This innovation addresses the limitations of static configurations by enabling more precise control over task-critical phrases and turn detection, improving the accuracy and relevancy of ASR systems in dynamic conversational environments. As a result, users can maintain a single connection with dynamic behavior, enhancing the capability of voice agents to respond effectively to shifting conversational contexts.

ElevenLabs custom voice technology, while promising in demos, can face significant challenges in production due to concurrency caps, latency issues, and non-deterministic audio quality. These constraints often necessitate architectural adjustments and enterprise-level negotiations to manage high simultaneous call volumes, as standard tiers support limited concurrent requests. Real-world latency is notably higher than vendor claims, impacting time-to-first-byte (TTFB) and potentially degrading user experience. The quality and consistency of voice outputs heavily depend on the training audio, with variations becoming more evident under increased traffic. Production systems must prepare for retry storms and latency spikes, which can escalate costs and lead to voice instability. Moreover, multilingual models might inadvertently switch languages or accents mid-generation, posing a risk for certain applications. Production teams should rigorously evaluate ElevenLabs under peak conditions, considering alternatives if high concurrency and tight latency are critical requirements.