Voice Intelligence at Scale: From Call of Duty to Fraud Detection with Modulate AI

Voice Intelligence at Scale: From Call of Duty to Fraud Detection with Modulate AI

Every day billions of voice conversations happen across games, customer service calls, and financial transactions. Almost none of them are understood by machines. In this episode of Inference Time Tactics, Calvin Cooper and Yash Sharma sit down with Carter Huffman, CTO and co founder of Modulate, to explore the AI systems that can finally understand voice conversations in real time.   Modulate’s model Velma 2.0 powers voice intelligence across industries. From moderating voice chat in games like Call of Duty to detecting fraud in financial calls and analyzing customer support conversations, their system uses ensembles of specialized models to capture tone, intent, emotion, and conversational dynamics. Instead of relying on giant foundation models, Velma orchestrates over 100 specialized models to deliver higher accuracy at dramatically lower cost.   We talked about:   * The challenge of processing a trillion hours of annual global voice traffic. * Scaling real-time moderation for massive platforms like Call of Duty. * Capturing nuance, tone, and sarcasm beyond basic text transcripts. * Ensemble architecture utilizing over 100 specialized models. * Orchestration layers that trim compute costs by identifying optimal model subsets. * Achieving order-of-magnitude cost savings compared to large foundational models. * Applying "exploration vs. exploitation" optimization to shifting conversation data. * Future development of "context graphs" to map participant intent and causality. Resources Mentioned: NeuroMetric Audio Leaderboard: https://leaderboard.neurometric.ai/?leaderboard=audio [https://leaderboard.neurometric.ai/?leaderboard=audio]  Connect with Modulate: Website: https://www.modulate.ai/ [https://www.modulate.ai/]  LinkedIn: https://www.linkedin.com/in/carter-huffman-a9aba05b [https://www.linkedin.com/in/carter-huffman-a9aba05b]  Velma: https://www.modulate.ai/velma [https://www.modulate.ai/velma]  Connect with Neurometric: Website: https://www.neurometric.ai/ [https://www.neurometric.ai/]  Substack: https://neurometric.substack.com/ [https://neurometric.substack.com/]  X: https://x.com/neurometric/ [https://x.com/neurometric/]  Bluesky: https://bsky.app/profile/neurometric.bsky.social [https://bsky.app/profile/neurometric.bsky.social] Hosts: Calvin Cooper https://x.com/cooper_nyc_ [https://x.com/cooper_nyc_]  https://www.linkedin.com/in/coopernyc [https://www.linkedin.com/in/coopernyc]   Yash Sharma https://x.com/yash_j_sharma [https://x.com/yash_j_sharma]  https://www.linkedin.com/in/yashjsharma/ [https://www.linkedin.com/in/yashjsharma/]

From GPU Scarcity to GPU Waste: Solving the Utilization Crisis

In this episode of Inference Time Tactics, Cooper and Byron sit down with Charlie and Anil from Rapt AI to tackle one of the industry's most expensive problems: GPU underutilization. With half a trillion dollars invested in GPU infrastructure running at just 20-30% utilization, Rapt AI is building AI-powered orchestration that automatically analyzes workloads and matches them to the right compute resources—no guesswork required.   We talked about:   * Why half a trillion dollars in GPU infrastructure runs at only 20-30% utilization—and how a 5% drop costs $200,000 per $2M investment.  * How Rapt AI's platform continuously analyzes workloads and auto-optimizes GPU allocation, letting customers run 4-14 models per GPU.  * Real results: moving workloads from H100s to A100s at 40% of the cost, and reducing GPU footprints from 184 to under 50 while improving performance.  * Why 2026 becomes the year of inference as agentic workloads create unprecedented infrastructure chaos.  * The shift from supply problems to optimization problems—and why abstraction layers matter across multi-vendor environments.  * Power as the next crisis: tokens-per-watt emerging as the critical metric alongside tokens-per-dollar.  * How intelligent orchestration frees up data scientists and ML ops teams from infrastructure tuning to focus on AI innovation. Connect with Rapt AI: Website: https://www.rapt.ai/ [https://www.rapt.ai/]  LinkedIn (Anil Ravindranath): https://www.linkedin.com/in/anilravindranath [https://www.linkedin.com/in/anilravindranath]  LinkedIn (Charlie Leeming): https://www.linkedin.com/in/charlieleeming/ [https://www.linkedin.com/in/charlieleeming/]  Connect with Neurometric: Website: https://www.neurometric.ai/ [https://www.neurometric.ai/]  Substack: https://neurometric.substack.com/ [https://neurometric.substack.com/]  X: https://x.com/neurometric/ [https://x.com/neurometric/]  Bluesky: https://bsky.app/profile/neurometric.bsky.social [https://bsky.app/profile/neurometric.bsky.social] Hosts: Calvin Cooper https://x.com/cooper_nyc_ [https://x.com/cooper_nyc_]  https://www.linkedin.com/in/coopernyc [https://www.linkedin.com/in/coopernyc]   Byron Galbraith https://x.com/bgalbraith [https://x.com/bgalbraith]  https://www.linkedin.com/in/byrongalbraith [https://www.linkedin.com/in/byrongalbraith]

Lessons from the Leading Edge: What 420 AI Deployments Reveal About Enterprise Success

In this episode of Inference Time Tactics, Rob, Cooper, and Byron sit down with Shawn Rogers, CEO of BARC US to unpack fresh data from 421 organizations actively deploying AI in production. Shawn shares what separates the 20% of AI leaders from everyone else, why cost surprises are hitting harder than expected, and how the pressure to "just do AI" is causing companies to skip critical foundations—often to their detriment. We talked about:   * Why multi-model strategies and small language models are becoming essential for enterprise AI. * The seven foundational areas that help AI leaders deploy twice as many projects as everyone else.  * Why 51% of deployments face unexpected cost overruns—and which expenses hit hardest.  * Data quality jumping to the #1 challenge, affecting 44% of production deployments.  * The IT satisfaction paradox: top resource at the start, lowest satisfaction scores at scale.  * How responsible AI priorities shifted as human-in-the-loop dropped from 36% to 21%.  Resources Mentioned: Lessons from the Leading Edge: Successful Delivery of AI/GenAI https://barc.com/research/successful-ai-genai-delivery/ Connect with BARC: Website: https://barc.com/ [https://barc.com/]  LinkedIn (Shawn Rogers): https://www.linkedin.com/in/shawnrogers/ [https://www.linkedin.com/in/shawnrogers/]  Connect with Neurometric: Website: https://www.neurometric.ai/ [https://www.neurometric.ai/]  Substack: https://neurometric.substack.com/ [https://neurometric.substack.com/]  X: https://x.com/neurometric/ [https://x.com/neurometric/]  Bluesky: https://bsky.app/profile/neurometric.bsky.social [https://bsky.app/profile/neurometric.bsky.social] Hosts: Rob May https://x.com/robmay [https://x.com/robmay]  https://www.linkedin.com/in/robmay [https://www.linkedin.com/in/robmay]   Calvin Cooper https://x.com/cooper_nyc_ [https://x.com/cooper_nyc_]  https://www.linkedin.com/in/coopernyc [https://www.linkedin.com/in/coopernyc]   Byron Galbraith https://x.com/bgalbraith [https://x.com/bgalbraith]  https://www.linkedin.com/in/byrongalbraith [https://www.linkedin.com/in/byrongalbraith]

The Thinking Algorithm Leaderboard: Why No Single Model Wins

In this episode of Inference Time Tactics, Cooper and Byron break down NeuroMetric's Thinking Algorithm Leaderboard and what it reveals about building production-ready AI agents. They share why prompt engineering with a single model won't cut it for enterprise use cases, explore the impact of inference-time compute strategies, and discuss what they learned from testing 10 models across real CRM tasks—from surprising token inefficiency to catastrophic failures in SQL generation.   We talked about:   * Why NeuroMetric built the first leaderboard combining models with inference-time compute strategies.  * How Salesforce's CRMArena-Pro reflects real multi-step business tasks better than pure reasoning benchmarks.  * The jagged frontier: no single model or technique dominates across all tasks.  * Why GPT 20B was surprisingly token inefficient—twice as slow as GPT 120B for similar accuracy.  * How GPT-5 nano's conversational style broke SQL generation tasks completely.  * Trading accuracy for speed: two-model ensembles versus five, and saving 20+ seconds per task.  * Throughput constraints as a hidden bottleneck when scaling to production volumes.  * Future directions: LLM-guided search, task clustering, and compression to specialized small models. Resources Mentioned: CRMArena-Pro from Saleforce: https://www.salesforce.com/blog/crmarena-pro/ [https://www.salesforce.com/blog/crmarena-pro/] Thinking Algorithm Leaderboard:  https://leaderboard.neurometric.ai/ [https://leaderboard.neurometric.ai/]  Connect with Neurometric: Website: https://www.neurometric.ai/ [https://www.neurometric.ai/]  Substack: https://neurometric.substack.com/ [https://neurometric.substack.com/]  X: https://x.com/neurometric/ [https://x.com/neurometric/]  Bluesky: https://bsky.app/profile/neurometric.bsky.social [https://bsky.app/profile/neurometric.bsky.social]   Hosts: Calvin Cooper https://x.com/cooper_nyc_ [https://x.com/cooper_nyc_]  https://www.linkedin.com/in/coopernyc [https://www.linkedin.com/in/coopernyc]   Guest/s: Byron Galbraith https://x.com/bgalbraith [https://x.com/bgalbraith]  https://www.linkedin.com/in/byrongalbraith [https://www.linkedin.com/in/byrongalbraith]

Benchmarking Generalization: How AI Learns Beyond Training Data

In this episode of Inference Time Tactics, Rob and Cooper from Neurometric sit down with Yash Sharma, an AI researcher whose work is reshaping how we understand model generalization. Yash recently completed his PhD at the Max Planck Institute for Intelligent Systems and has held research roles at Google Brain, Meta AI, Amazon, Borealis AI, and IBM Research. His studies on compositional generalization, adversarial robustness, and long-tail benchmarks reveal when and why models succeed—or fail—at reasoning beyond their training data. If you’re designing inference-time systems, building agents that need reliability, or just want to understand what “generalization” actually means in practice, this conversation bridges deep theory with actionable insight—clear, technical, and strategically grounded. Key Topics * What it really means for AI systems to generalize beyond their training data * Why large language models still fail in novel or unpredictable scenarios * How inference-time compute can both amplify and reveal generalization limits * What these limits mean for building reliable, agentic AI systems * How to benchmark generalization in real-world settings * Yash’s “Let It Wag!” benchmark for testing long-tail and under-represented concepts * Why genuine scientific breakthroughs (like curing cancer) require more than scaling test-time compute Connect with Yash Sharma: * Yash Sharma [https://www.yash-sharma.com/] * Let It Wag! Benchmark [https://arxiv.org/abs/2404.04125] * Paper: Pretraining Frequency Predicts Compositional Generalization of CLIP (NeurIPS 2024 Workshop) [https://arxiv.org/abs/2502.18326] Connect with Neurometric: Website: https://www.neurometric.ai/ [https://www.neurometric.ai/]  Substack: https://neurometric.substack.com/ [https://neurometric.substack.com/]  X: https://x.com/neurometric/ [https://x.com/neurometric/]  Bluesky: https://bsky.app/profile/neurometric.bsky.social [https://bsky.app/profile/neurometric.bsky.social]   Rob May https://x.com/robmay [https://x.com/robmay]  https://www.linkedin.com/in/robmay [https://www.linkedin.com/in/robmay]   Calvin Cooper https://x.com/cooper_nyc_ [https://x.com/cooper_nyc_]  https://www.linkedin.com/in/coopernyc [https://www.linkedin.com/in/coopernyc]