Building Inflection AI's multi-speaker capabilities with NVIDIA Nemotron Speech open models. Oct 28, 2026 Inflection AI At Inflection AI, its goal is to make conversations with Pi feel as natural and seamless as possible. That means bringing real-time speech processing, multi-speaker understanding, and multilingual capabilities together into a unified system that better supports the way people naturally communicate. Real conversations rarely follow a perfect back-and-forth. People talk to Pi with friends, family, or coworkers around. They interrupt each other, finish each other's sentences, and sometimes speak at the same time. For Pi to navigate these interactions, it needs to understand not just what is being said, but who is speaking, even when voices overlap. Its engineering team has been working hard to bring these capabilities together, starting with Inflection AI's Conversational Interaction Stack and integrating NVIDIA Nemotron Speech open models with its own pipeline to improve future versions of the Pi user experience. Here's how Inflection AI, Inc. approached building a more capable multi-speaker conversational stack, step by step. Step 1: adding diarization with NVIDIA Streaming Sortformer. Its first step was implementing speaker diarization and identifying which speaker is talking at each moment in a conversation. Inflection AI, Inc. started by integrating NVIDIA Streaming Sortformer into Pi's speech pipeline, giving Pi an initial ability to distinguish between speakers. This established a working multi-speaker foundation that Inflection AI, Inc. could build on and refine as Inflection AI, Inc. tackled more complex conversational scenarios. Step 2: Speaker-conditioned ASR with NVIDIA Multitalker-Parakeet. Knowing who is speaking is only part of the challenge. Pi also needs to accurately capture what each person says, particularly when multiple people speak at once. Its existing automatic speech recognition (ASR) backend was fine-tuned on its data but transcribed audio without distinguishing between individual speakers. In overlapping conversations, this could result in words from different speakers being combined into a single transcript. To address this, Inflection AI, Inc. integrated NVIDIA Multitalker Parakeet (MT-Parakeet) into its existing ASR stack, enabling speaker-conditioned transcription. By using diarization output to guide the transcription process, Inflection AI, Inc. can now capture each speaker's words separately, even in overlapping speech. This marked an important step toward building a more complete understanding of multi-person conversations. With English-language transcription capabilities in place, its next focus was improving speaker diarization and expanding multilingual support. Step 3: improving speaker diarization and overlapping speech with Nemotron 3 Diarization. With speaker-conditioned ASR in place, its next priority was refining diarization accuracy. Because MT-Parakeet relies on diarization to identify individual speakers, getting those labels right is essential. In real-world conversations, where people interrupt one another or speak simultaneously, even small speaker-labeling errors can affect how Pi interprets and responds to a conversation. Building on its initial implementation with Sortformer, Inflection AI, Inc. integrated NVIDIA Nemotron 3 Diarization, which delivered a clear improvement in speaker diarization in its testing. Following are the results on AMI-SDM in terms of Diarization Error Rate (lower is better). One of the most valuable improvements was its ability to handle overlapping speech. When two people speak at once, Pi needs to distinguish between their voices and understand which words belong to each person. By combining Nemotron 3 Diarization with MT-Parakeet, Inflection AI, Inc. has strengthened Pi's ability to identify who said what, including during overlapping conversations. This brings Inflection AI, Inc. closer to a conversational experience where Pi can follow the natural flow of a group discussion, rather than relying on a sequence of individual turns. Step 4: expanding Pi's capabilities with Nemotron 3.5 Transcribe for multilingual conversations. Pi connects with people around the world, so supporting conversations across languages was another important part of building a more natural and accessible experience. NVIDIA Nemotron 3.5 Transcribe extends Pi's speech recognition across languages using a single multilingual model. The open weights also give Inflection AI, Inc. a path to adapt the model to Pi's needs. The checkpoint Inflection AI, Inc. tested performs offline transcriptions. Its early experiments applied it to shorter audio segments, and Inflection AI, Inc. is building the buffering and transcript-handling logic needed to integrate this approach into Pi's streaming pipeline. Its initial chunked-inference evaluations on AMI-SDM English benchmarks have shown promising results, as reflected in the word error rates (WERs; lower is better) below: Since the models MT-Parakeet and Nemotron 3.5 Transcribe are for different cases, the WER comparison is not like-to-like. However, these results show promising direction with Nemotron 3.5 Transcribe in Pi. An important part of this work is ensuring inference of the Pi ASR model is not very heavy. A single multilingual model could simplify how Inflection AI, Inc. deploy and maintain ASR across languages. Inflection AI, Inc. is comparing it with its current pipeline to measure improvements in transcription accuracy, response latency, and serving cost. Integrating multilingual transcription with its speaker-aware pipeline is ongoing work. These improvements represent the next phase of its work: bringing streaming, multi-speaker, and multilingual speech understanding into a unified system that allows Pi to participate in more natural conversations. What's next. Advancing Pi from a primarily single-speaker experience toward one that can better support dynamic, overlapping, multi-person conversations marks an important step in its work building human-centered AI with relational intelligence. NVIDIA Nemotron Speech open models have provided valuable building blocks along the way. Through its engineering work, Inflection AI, Inc. has integrated and refined these technologies into an increasingly sophisticated speech pipeline, from establishing speaker diarization with Sortformer to enabling speaker-conditioned transcription with MT-Parakeet and strengthening speaker diarization with Nemotron 3 Diarization. Now, with its ongoing work on Nemotron 3.5 Transcribe, Inflection AI, Inc. is focused on extending those capabilities across languages while maintaining the speed and responsiveness that natural conversation demands. The innovation lies not only in the capabilities of individual models, but in how Inflection AI, Inc. bring them together in Inflection AI's Conversational Interaction Stack to create an AI experience that feels more intuitive, responsive, and human-centered. Inflection AI, Inc. look forward to releasing this work in public releases of Pi and will be sharing more as this work progresses.

Alice collaborates with Inflection AI and AI21 to demonstrate NVIDIA Nemotron open models can be trained for frontier safety. Oct 15, 2026 Inflection AI NEW YORK, Oct. 15, 2026 (GLOBE NEWSWIRE) - Alice announced the results of two collaborations focused on aligning NVIDIA Nemotron models to company-specific safety and security policies. The results demonstrate how real-world threat data can be used to train open-source models for frontier safety performance. Open-source models give organizations greater control over AI behavior, allowing them to train policies into the underlying model rather than setting instructions as system prompts. To successfully train models on their policies, companies need adversarial data and expertise. Two projects demonstrate how NVIDIA's state-of-the-art open-source model can be aligned for safer human interactions, and more secure enterprise agents. "Powerful AI is being democratized. It's now available to companies everywhere, and they want to use it safely and predictably," said Noam Schwartz, CEO and co-founder of Alice. "These results show that with the right real-world adversarial data and training, we can teach open source models how to handle the threats and abuse they will actually face, across very different safety challenges, while keeping them as powerful. That's the bar now: frontier-grade safety that any organization running advanced AI can actually put into practice." Training safety for how people actually use AI. Inflection AI is building personal AI that empowers people and shifts interactions from transactional to relational. Allowing more natural interactions with conversational AI also increases risks which most benchmarks can't catch. In the process of building the next generation of personal intelligence, Inflection AI fine-tuned NVIDIA Nemotron 3 Super 120B-A12B, and turned to Alice to develop red-teaming datasets aimed at reducing sycophancy and preventing the model from reinforcing unhealthy attachment over long conversations. Fine-tuning on that data raised Pi's alignment score by 30% while keeping unnecessary refusals to just 1.6%. Alice created more than 20 realistic personas and used them in multi-turn red-team exercises where those risks could develop over time. The personas included vulnerable but benign users as well as adversarial users deliberately attempting to manipulate the model, drawing on Alice's real-world knowledge of harmful and adversarial behavior. "As AI innovation accelerates, safety has to advance rapidly as well. It's no longer enough to ask whether a model can identify and refuse a harmful prompt," said Sean White, CEO of Inflection AI. "We also need to understand how AI behaves across hundreds of interactions, whether it challenges rather than simply agrees, if it supports human agency rather than dependency, and ultimately if it leaves people more capable. This work with Alice and NVIDIA demonstrates that we can train those principles into powerful open models without sacrificing what makes them useful. That's fundamental to the kind of human-centered Personal Intelligence we're building with Pi." Security without sacrificing capability. Alice also worked with AI21, which has trained language models since 2021 and now offers lab-as-a service for specialized intelligence. The work with them focused on a very different problem: protecting enterprise agents against indirect prompt injection. As agents gain access to company systems, data and tools, attackers can attempt to manipulate them through information the agent encounters while doing its work. Training against those attacks therefore cannot focus only on whether the model refuses a malicious instruction. A secure agent also needs to recognize the attack while continuing to complete the legitimate task. Alice's reinforcement-learning environments were built to train and measure both. AI21 used them to post-train the recently released NVIDIA Nemotron 3.5 Super VL open model, increasing the model's resilience to indirect prompt injection by 41 points, competing with GPT-6.1 Sol. The result shows that security and capability are not necessarily competing objectives. With the right training, models can become significantly more resilient to adversarial manipulation while maintaining task performance. "For most companies, the next phase of AI adoption goes beyond frontier models, to open models they own, shape and control," said Ori Goshen, Co-founder and CEO of AI21. "Owning a model means owning how it fails, not only what it can do. We post-trained NVIDIA Nemotron 3.5 Super-VL on attack traces Alice sees in the wild, so it holds up against the attacks that actually reach enterprises while keeping the behavior it was built for." "NVIDIA Nemotron provides the open weights, recipes and tooling for organizations to post-train on their own data and optimize accuracy and performance for their specific workloads," said Joey Conway, senior director Nemotron and Software, NVIDIA. "Alice's work with Inflection AI and AI21 shows how post-training Nemotron on real-world adversarial data delivers powerful safety and security - exactly the kind of innovation open models are meant to unlock." Taken together, these collaborations demonstrate how real-world adversarial intelligence, safety datasets, expert red teaming and reinforcement-learning environments can turn open models into systems trained for the risks they will actually face. The broader opportunity is clear: powerful, customizable AI that companies can own and deploy with frontier-level safety built in. About Alice. Alice is an AI safety and security company helping model builders and enterprises train, evaluate, and deploy safer AI. Its adversarial intelligence combines continuously emerging threats from open and closed communities with expert research to power red teaming, evaluations, training data, RL environments, and enterprise guardrails. Alice works with 8 of the 10 leading foundation model labs and AI-native companies, while helping enterprises test and protect AI applications against real-world safety and security risk. About Inflection AI. Inflection AI is a Public Benefit Corporation with the mission to harness the power of AI to improve human well-being and productivity. Inflection AI is empowering people and brands with human-centered, emotionally intelligent AI that transforms interactions from transactional to relational. Inflection AI pioneered Personal Intelligence with Pi, the world's first Personal Intelligence partner, built to help people navigate their lives. Pi is powered by the Inflection AI stack orchestrated across custom fine-tuned models. About AI21. AI21 is an AI lab building frontier models and AI systems for the enterprise since 2017. Creator of the Jurassic and Jamba model families, AI21 today offers its lab-as-a-service for specialized intelligence: Forward-Deployed Researchers and a harness- and model-training platform that turn open foundations into models its customers own, delivered with the weights, recipes and evaluations that keep them theirs. AI21 has secured $336 million in funding from industry leaders, including Google, and Intel, reinforcing its commitment to advancing AI innovation.