Video Analytics
Extract rich insights from your video & audio libraries for information such as spoken words, written text, faces, speakers, celebrities, emotions, topics, brands, and scenes. Access the data within your application or infrastructure, make it more discoverable, and use it to create new over-the-top (OTT) experiences and monetization opportunities by developing cutting edge user engagement.
Our Approach
AI-based analysis operations, including speech transcription
Audio transcription: A transcript of the spoken words with timestamps. Multiple languages are supported.
Speaker indexing: A mapping of the speakers and the corresponding spoken words.
Speech sentiment analysis: The output of sentiment analysis performed on the audio transcription.
Keywords: Keywords that are extracted from the audio transcription.
Extracts insights (rich metadata) from both audio and video, and outputs a readable file format
Face tracking: The time during which faces are present in the video. Each face has a face ID and a corresponding collection of thumbnails.
Visual text: The text that's detected via optical character recognition. The text is time stamped and also used to extract keywords (in addition to the audio transcript).
Keyframes: A collection of keyframes extracted from the video.
Visual content moderation: The portion of the videos flagged as adult or racy in nature.
Annotation: A result of annotating the videos based on a pre-defined object model
Describes the settings to be used when analysing a video to detect all the faces present.
Outcomes
Keywords extraction: Extracts keywords from speech and visual text.
Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).
Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.
Artifacts: Extracts rich set of “next level of details” artifacts for each of the models.
Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.
Advanced Analytics
Approach
Our Iterative & hybrid approach follows the following methodology-
Building analytics models that learn is a process of experimentation. By partnering with Cognilytic, you can tactically test solutions in on-prem, cloud or hybrid environments without the pressure of long-term investments or commitments.
We’ll show you how to streamline your data assets to make it a truly transformational asset. We’ll simplify your advanced analytics journey by leveraging leading Microsoft technologies such as Cognitive Services and Azure Databricks. And we’ll ensure you have the data management, governance and policies in place to realize the potential your data offers.
Outcomes
Business all around are looking to tap into the potential of predictive analytics, artificial intelligence (AI), machine learning and cognitive computing to build competitive edge, speedy decision-making, enhanced efficiency, and increased customer retention. However getting started on this path can be a very daunting step. Cognilytic can be the most suited partner to take measured steps on this track by starting with your data maturity assessment and custom analytics roadmap based on the otucomes.
Increased profitability
Customer insights
Business Process Optimization
Marketing effectiveness and ROI
Minimized fraudulent activities
Identify new revenue sources
Enhanced customer satisfaction
Improved employee retention
Reduced equipment outages
Efficient forecasting
Use Cases
Informative Rich Learning Content (Education Industry)
Efficiently manage your media library by video indexing and information extraction.
Evolved Customer Engagement
Apply rich extracted metadata to your videos for providing customers with enhanced information.
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Whether you are planning or are already in the midst of your transformation journey,our experts can help you accelerate your transformation initiatives.
