Velma API by Modulate

Velma API by Modulate is an advanced audio-native voice analytics solution designed to interpret emotion, tone, and intent in spoken conversations. Unlike conventional tools that are limited to text transcription, this model processes acoustic signals directly. This allows it to capture critical linguistic nuances often lost during text conversion, transforming audio into actionable data on user behavior.

The tool's core technology is based on the Ensemble Listening Model (ELM). This approach enables artificial intelligence to analyze the full spectrum of a conversation, identifying moods ranging from frustration and anxiety to calmness or confidence. Thanks to its architecture, Velma API can process both live audio streams and stored recordings, integrating seamlessly with third-party applications and workflows.

One of the most powerful features of Velma API by Modulate is its capacity for risk and fraud detection. The tool is trained to identify threats such as identity theft and the use of deepfake voices or call spoofing. When the system detects an anomaly, it generates an escalation alert that includes the type of threat detected and a specific confidence level, facilitating a rapid response from security teams.

Key capabilities include:

• Sentiment and emotion analysis: Accurate identification of the speakers' emotional tone.
• Conversation summaries: Generation of reports detailing topics discussed, key behaviors, and intent metrics.
• Risk escalation: Automated notifications for potential fraudulent behavior or policy violations.
• Synthetic identity detection: The ability to differentiate between real human voices and AI-driven manipulations.

This API is particularly useful in sectors such as customer experience (CX) centers, financial services, technical support, and gaming or social media environments. In these contexts, it helps improve decision-making, ensure user safety, and optimize interactions with intelligent voice agents.

To achieve the best results with Velma API, high acoustic quality is essential, as the model relies on the clarity of audio signals. While it is a robust tool for behavioral analysis, users should keep in mind that factors such as overlapping voices or a lack of external context can influence the accuracy of results, making it an ideal complementary solution for expert human supervision.