AI solutions for an intelligent future
Gramener AI Lab fuels innovation by leveraging the latest AI trends and applying them to business consumption of data. We comb through massive datasets and construct Artificial Intelligence solutions with insightful storytelling capabilities.
At AI Labs, we use applied research to help businesses achieve a competitive advantage by leveraging the latest innovations in the field. We undertake pilot projects to extract additional intelligence from data in the areas of face/speech recognition and image/video classification to develop AI products.
Natural Language Processing, Intent Detection, Topic modeling, and Sentiment Analysis
Crowd counting, facial recognition, biodiversity species classification and object detection
Speaker diarization, voice clustering, speech recognition, and emotion & gender detection
We partner with universities and enterprise analytics labs to collaborate in areas of applied research.
We attend and speak at AI conferences and forums to share knowledge and stay abreast with ongoing research in AI and ML.
This AI software for Legal Document Analysis helps lawyers and practitioners to focus on more important works than reviewing monotonous documents.
This application leverages Deep Learning to process images captured from satellite, enriched with census data, and offers insights about rise in urbanization and poverty, and anomalies in census-measured factors like literacy, employment, and healthcare.
Identify voices with this AI. This Deep-learning application automatically recogizes Gender and Emotions from an audio file.
Using this Machine Learning-driven application you can count any number of objects, animals, or people in the crowd.
Which words were used most during corporate earnings calls? How has this changed over time? This artificial intelligence solution analyses data from earning call transcripts.
When a caller calls a call center, or dials in on a phone conversation, this deep learning solution can identify the person.
Active learning, seeks to select the most informative data points, with the objective of reaching a performance comparable to that of passive supervised learning algorithms, using fewer training instances.