How AI Insights Turns Complex Emotional Signals into Clear Answers

The foundational value of a large language model is less about generating answers and more about its capacity to address the right questions. Since those questions vary dramatically, shifting by role, industry, and the immediate context, we developed AI Insights, a powerful agentic feature within Imentiv AI that moves beyond the capabilities of a standard LLM. This feature is available across video, audio, image, and text modalities. AI Insights autonomously leverages emotional data points extracted from the media to deliver precise responses to any user query. This comprehensive analysis of the full emotional landscape ensures the AI captures essential context and drives the analytical process. Whatever the question, AI Insights delivers context-specific, actionable insights that truly matter.

 

What is the AI Insights feature?

AI Insights is an exclusive feature within Imentiv AI that helps users get direct and contextual answers, turning complex emotion data into actionable insights from their analyzed media content. 

 

The ultimate aim of the AI Insights feature is to help anyone move from data to clarity, faster and with more context.

Once a video, audio, image, or text is processed on Imentiv AI’s platform, users can ask questions, like 

 

“During the training video, how engaged was Trainer Lisa (Face 4) when explaining the workflow?”  

 

“In this support call, when did John (Speaker 1, the representative) sound the most empathetic?”

 

“Who in the audience looks confused versus excited in this snapshot?”

 

“What emotion drove the strongest reaction in this ad campaign?”

 

“Which lines in the survey response reflect the most positivity?”

 

These sample questions give a sense of what you can explore, but the real discoveries come from the questions only you can ask.

Unlike traditional analytics that deliver generic outputs, AI Insights acts like a smart assistant, providing actionable, fact-backed insights drawn directly from your media and its emotional signals.

According to Gartner, by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from zero percent in 2024.

 

AI Insights in Action: Real-World Examples

Now, let’s see AI Insights in action through a few real-world examples. 

 

 

This is a  sample video  that showcases a brief, entry-level job interview for a kitchen role between Resource Manager Susan Thompson and applicant Mary Hanson. It's a professional exchange that sets the stage for analyzing behavioral and emotional data during a hiring process.

 

Q1. Did Mary show any hesitation or stress when explaining why she left her last job?

 

Yes — mild hesitation and emotional strain were evident, particularly through her vocal tone and facial expressions.

• Audio indicators : During that segment, Mary’s fear level averaged 3.6% and sadness 0.8%, both higher than her usual emotional baseline, while happiness dropped below 25%. This slight dip in vocal brightness and rise in tension markers suggests self-consciousness or anxiety when addressing that topic.
• Facial indicators : Her valence averaged only +0.10 and arousal 0.20, indicating low positivity and moderate physiological activation — a pattern consistent with internal stress despite maintaining verbal composure.
• Text analysis : Emotion keywords showed small but distinct increases in nervousness (0.12%) and remorse (0.02%) when discussing her past role.

 

Decision Insight: The combination of reduced vocal warmth, neutral facial affect, and subtle nervous word patterns suggests that Mary was being truthful but uneasy — likely cautious about how her reason for leaving would be perceived.

Q2. At what points did Mary seem most genuinely engaged or motivated during the conversation?

Mary was most engaged when talking about her kitchen experience and teamwork, reflected consistently across all modalities.

 

• Audio: Happiness averaged 27% and surprise 5.6%, both peaking when she elaborated on specific kitchen scenarios. Her tone became more energetic and fluid, signaling enthusiasm.
• Facial: Arousal (0.20) and intensity (0.58) were among the highest values observed — clear signs of emotional activation and genuine involvement.
• Text: Strongest emotional keywords were admiration (14.6%) and mild optimism (0.37%), both notably higher than stress-related terms.

Decision Insight : Mary’s emotional alignment — expressive voice, lively tone, positive facial cues — indicates authentic motivation when discussing work tasks she enjoys, a positive indicator for role fit.

 

Q3. How did Susan, the interviewer, emotionally respond throughout — was she receptive, neutral, or disengaged?

 

Susan remained consistently receptive and emotionally composed, showing professionalism with moments of warmth.

 

• Facial indicators : Her expressions were 67% neutral and 27% happy, with minimal sadness (4%) or anger (1%). Average valence (+0.06) and arousal (0.07) suggest calm attentiveness rather than emotional detachment — typical of an interviewer maintaining control while staying positive.
• Audio cues : Her tone carried 30% happiness and 42% neutrality, with very low boredom (5.6%) or disgust (3.4%) — indicating active listening and professional engagement.
• Text emotion : Words linked to approval (23.6%) and admiration (13.1%) outnumbered negative cues like annoyance (1.3%), reflecting verbal encouragement and psychological safety for the candidate.

 

Decision Insight: Susan’s calm and positive behavior aligns across all signals, confirming she was receptive and empathetically neutral — neither overly expressive nor emotionally distant.

 

 

Here’s another example that shows a team meeting, with AI Insights answering questions about team members’ emotional reactions to manager feedback.

 

Look at this image: five people celebrating a happy moment. Questions about their smiles and engagement were asked, and this is the response AI Insights delivered, shown in the screenshot below.

 

 

AI Insights is not limited to video or images; you can ask queries on other media types like audio and text , and get insightful responses the same way.

 

 

What makes asking questions through AI Insights more effective?

 

The AI Insights feature adapts to how each user approaches problem-solving. Some come with a clear goal in mind; they know what kind of media they’ve analyzed and what they want to discover from it. Others prefer to explore freely, letting the AI surface new angles they might not have considered. Both approaches work.

When users choose to add context, the AI Insights feature adjusts its focus based on what the user wants to find out. It uses the existing understanding of the media but aligns its responses more closely with the user’s intent, whether that’s understanding audience emotion shifts during a webinar, evaluating brand recall in an ad, or measuring speaker authenticity in an interview. This helps Imentiv AI Insights deliver sharper, more goal-driven insights that match what truly matters to the user.

For those who’d rather jump straight into exploration, the feature instantly offers question prompts relevant to their media type. It could surface queries like “ Which part of my ad triggered the strongest emotional response? ” or “ What moments in my webinar reflect peak engagement? ” These predefined questions help users uncover insights without needing to frame complex analytical queries.

In both cases, AI Insights removes the friction between curiosity and clarity. Whether users choose to guide the AI with context or let it guide them through exploration, the outcome is the same — actionable, objective, and deeply relevant insights that keep decision-making fast and data-driven.

AI Insights isn’t just another feature of Imentiv AI — it’s your shortcut from tangled emotional data to clear, confident decisions. By cutting through the noise and providing direct, fact-backed answers to your most pressing questions, it moves you swiftly from raw data to confident, strategic action. This agentic intelligence bridges the gap between deep emotional analysis and practical decision-making, ensuring that every insight you gain is immediately relevant and actionable.

Upload any media today to get clear, actionable AI-driven insights!