Frequently Asked Questions

Video Emotion Recognition works by first extracting frames from a video and detecting, cropping, and normalizing faces. These frames are then processed with proprietary deep learning models to capture subtle facial cues such as smiles or frowns. Finally, temporal patterns across consecutive frames are analyzed to identify how emotions evolve.

Imentiv AI is an advanced emotion AI tool and  API s that read human emotions from facial expressions, voice tones, and text. Unlike single-channel tools (just text or just video), it combines all signals—like humans do—for more accurate insights by using LLMs. Since most customer decisions are driven by emotion, Imentiv AI helps enterprises understand their audience, design empathetic experiences, and make smarter, data-driven decisions.

Imentiv AI supports .mp3, .wav, and .aac audio formats. You can upload audio in any sample rate, as the system automatically resamples it to 16 kHz mono, the required format for accurate processing.

Imentiv AI supports videos in .mp4, .m4v, .mov, .flv, .mkv, and .webm formats. Videos can also be analyzed directly from a YouTube URL.

The system detects multiple faces in a single image. It analyzes each face individually to identify emotions and also provides an overall emotional summary for the group.

Imentiv AI is developed with privacy and data protection in mind, following the principles of major global regulations such as GDPR and CCPA. For enterprise deployments, the platform can be hosted in a customer’s preferred region to support data residency needs, and consent mechanisms are built in to help guide responsible data use. Formal compliance evaluations are ongoing as we continue to strengthen our framework.

Yes. Integration is done through our API with custom code to connect your chosen system. Pre-built connectors aren’t available yet, but the API gives you full flexibility.

The system focuses only on the speech signals that carry emotions. Audio is split into vocal emotion analysis (tone, pitch, rhythm) and transcript emotion analysis (spoken words). While background noise is not analyzed for emotions, it is removed during transcript generation, ensuring the system captures only the meaningful emotional cues.

The system handles slang fairly well when enough emotional context is present. Informal language is usually interpreted accurately, as the model is trained on a variety of conversational data. Idioms can be more challenging since their meaning often depends on cultural or non-literal context, which may lead to lower accuracy. Even so, the system can still capture the general emotional tone in most cases. Ongoing development continues to improve performance in all three areas.

Imentiv AI resolves ambiguous or overlapping emotions by:

Multimodal fusion – combining signals from video, audio, image, and text for a balanced view

Probability scoring – assigning confidence levels to each detected emotion

Psychological mapping – aligning results with established emotion models to distinguish blends and overlaps

Imentiv AI stands out with multi-modal fusion—analyzing video, audio, text, and images together for a full emotional picture. Imentiv AI then uses large language models to analyze this multi-modal emotions data and provides deeper insights and answers questions about the media to help businesses make better decisions. While many competitors offer primitive single-modality tools like basic text sentiment or simple surveys, our approach captures far richer insights. Each analysis is also validated by an in-house psychologist, adding reliability and trust.

A dedicated ML monitoring pipeline is in place to continuously sample customer data. This process monitors for concept drift, measures system performance, and uses these insights for ongoing retraining and improvement of the models.

Public case studies (some are already on https://imentiv.ai/case-studies/) and white papers are currently in development. We are building reference stories with early design partners to demonstrate measurable business impact.

Yes. The system uses  speaker diarization  to identify each speaker and divide the audio into segments based on who is talking and when. This way, Imentiv AI can analyze every segment separately, capturing the emotional expressions of each speaker throughout the conversation.

Imentiv AI has been applied across major industries where emotional insight directly influences outcomes and revenue.

These include:

- Sales Call Analysis : The solution has been utilized by companies to analyze sales calls, helping to assess the emotional dynamics between the representative and the client.

- Candidate Vetting: A company successfully used the solution to analyze video resumes submitted by job candidates.

-  Advertising and Media: The platform has been applied in branded video ad analysis to uncover emotional impact.

- Bias Analysis : The solution has been applied to analyze legal and courtroom media, revealing the underlying emotional biases.

- Research and Analysis :  The tool is utilized by  professors from various parts of the globe for research studies that require sophisticated emotional data analysis.

Yes. Imentiv AI supports fine-tuning with proprietary enterprise datasets, allowing the models to be adapted to your organization’s specific needs and domain context.

Two distinct methods are supported:

- Batch Processing (currently available): Used for analyzing large volumes of pre-recorded data via a bulk API, with processing done in a delayed manner.

- Real-Time Streaming (in development): Used for live analysis, where a video feed is processed instantly via WebRTC to return moment-by-moment emotional data.

User consent is obtained before any data is uploaded or analyzed. Individuals are informed about how their data will be used and can withdraw consent at any time, including requesting deletion of their data.

No, the model is specifically trained to recognize and analyze human emotions only. It does not detect or interpret emotions in animals, objects, or other non-human subjects.

Yes. The system is designed to handle both short messages and long-form content such as articles, reports, or multi-paragraph text. While we don’t currently support direct file uploads, you can:

Copy & paste text—whether it’s a quick message or a longer document—directly into our dashboard for analysis.

Provide a YouTube URL, where the system automatically extracts the transcript and performs text emotion analysis.