Voice Intelligence at Scale: From Call of Duty to Fraud Detection with Modulate AI
Token EngineeringShow Notes
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
Connect with Modulate:Website: https://www.modulate.ai/ LinkedIn: https://www.linkedin.com/in/carter-huffman-a9aba05b Velma: https://www.modulate.ai/velma
Connect with Neurometric:Website: https://www.neurometric.ai/ Substack: https://neurometric.substack.com/ X: https://x.com/neurometricai/ Bluesky: https://bsky.app/profile/neurometric.bsky.social
Hosts: Calvin Cooper https://x.com/cooper_nyc_ https://www.linkedin.com/in/coopernyc
Yash Sharma https://x.com/yash_j_sharma https://www.linkedin.com/in/yashjsharma/
More from Token Engineering
In this episode of Token Engineering, Cooper sits down with Yash, Head of AI Research at Neurometric AI, to break down TMax from AI2 (Allen Institute for AI)—a fully open dataset and training recipe that pushes small open-weight models like Qwen to near-frontier performance on terminal agent tasks. The paper argues that diversity and difficulty matter more than where the data comes from—even when that data is entirely invented by Gemini rather than pulled from real-world code.
In this episode, Cooper sits down with Neurometric CEO, Rob May, to talk through a big-news month: the show's rebrand from Inference Time Tactics to Token Engineering, the launch of tokenminning.com as a response to the "tokenmaxxing" trend, Neurometric's new token engineering platform and fundraising announcement. But the bigger conversation is about why Rob thinks "token engineering" is about to become a discipline every AI company needs—the same way DevOps or UX became non-negotiable roles—and why he's betting the company on getting there first.
In this episode of Inference Time Tactics, Cooper sits down with Yash Sharma, Head of AI Research at Neurometric AI, to break down the Pioneer Agent paper from Fastino Labs—a system that uses Claude Sonnet as an ML engineer in a box to build and improve small language models end-to-end. From cold start data curation to production failure diagnosis, the paper argues the real bottleneck in SLM creation isn't training—it's everything around it.
In this episode of Inference Time Tactics, Cooper sits down with Fletcher Wimbush, CEO of Discovered AI, to get into what’s actually broken about hiring—and how 13 years of bootstrapped recruiting, 10,000 interviews, and four peer-reviewed journal articles became the foundation for a platform now outperforming its biggest competitors. With hundreds of applicants flooding every open role and general AI tools hitting the ceiling at 70-80% resume screening accuracy, Discovered is combining behavioral science with fine-tuned small language models to get hiring right the first time.
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.