Tinder

Building Effective Voice Agents — Toki Sherbakov + Anoop Kotha, OpenAI

Jul 20, 2025 · 17:17

Toki Sherbakov and Anoop Kotha from OpenAI argue that speech-to-speech models have reached a 'good enough' tipping point for production voice agents, highlighting the shift from chained architectures (transcription + LLM + TTS) to the real-time API's speech-to-speech approach. They detail trade-offs across latency, cost, accuracy, UX, and integrations depending on use case—consumer apps prioritize low latency and expressiveness, while customer service demands accuracy and tool integration. Key design patterns include delegating complex tasks to smarter models like O4 mini, prompting for voice expressiveness and tone, and starting with few tools. For evals, they recommend starting with observability, using SMEs for labeling, then transcription-based LLM-as-judge evals, audio evals with GPT-4 Audio to assess tone and pacing, and synthetic conversations between two real-time clients. Guardrails should run async with a configurable debounce period (e.g., every 100 characters). Examples from Lemonade (early focus on evals and guardrails) and Tinder (customization for brand realism) illustrate successful approaches.

AI Frontiers in Trust and Safety Combatting Multifaceted Harm on Tinder at Scale: Vibhor Kumar

Dec 2, 2024 · 14:36

Vibhor Kumar, senior AI engineer at Tinder, explains how the company uses open-source LLMs and LoRAX to detect a long tail of trust and safety violations at global scale. Facing challenges like content pollution and automated fraud from generative AI, Tinder leverages pre-trained models such as LLaMA and Mistral, fine-tuning them with LoRA and QLoRA on hybrid datasets generated by GPT-4 and manually verified. They serve dozens of fine-tuned adapters on a single GPU using LoRAX, achieving real-time inference (tens of QPS, ~100ms latency) for categories including hate speech, pig butchering scams, and underage users. The approach yields near 100% recall on simpler tasks and significant improvements over baselines, with better generalization that resists adversarial evasion. Future directions include visual language models for explicit image detection and automating retraining pipelines.