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.