Sanguine AI provides AI models that extract fine‑grained acoustic cues such as micro‑instabilities and speech rhythms from audio, even in noisy environments. The models are accessible via a low‑latency API and SDKs, enabling developers to add precise speech understanding to voice assistants, call‑center analytics, and safety‑critical control systems.
Funding
Funding not disclosed
Founders
Product
Problem
Voice-enabled systems often struggle to interpret subtle vocal nuances such as micro‑instabilities and natural speech rhythms, especially in noisy environments, leading to reduced accuracy and less natural human‑machine interaction.
Solution
Sanguine AI builds specialized artificial‑intelligence models that analyze fine‑grained acoustic cues in human speech. Their technology extracts micro‑instabilities and rhythmic patterns from real voices, maintaining high fidelity even when background noise is present. By providing these detailed speech representations, Sanguine AI enables downstream applications—such as virtual assistants, call‑center analytics, and safety‑critical voice controls—to understand user intent more reliably. The models are delivered via an API and SDK, allowing developers to integrate advanced speech perception into existing pipelines with minimal latency.
Target Audience
Primary customers are developers and product teams building voice assistants, conversational AI platforms, and safety‑critical voice control systems that require precise speech understanding.
Features
- Deep‑learning models trained on diverse, real‑world speech data to capture micro‑instabilities and natural rhythms
- Robust acoustic feature extraction that operates effectively in high‑noise conditions
- Real‑time inference API for low‑latency integration into voice‑enabled applications
- SDKs for popular development environments (Python, JavaScript) to simplify deployment
- Compatibility with standard audio formats and streaming protocols