Our Mission
To bring Equity to AI by making it Simple, Accessible and Affordable
The right people make all the difference
At the heart of Berrijam AI, our founders are true pioneers in the field of artificial intelligence. Their relentless pursuit of innovation has resulted in 10 patents for groundbreaking AI technologies and 15 influential research papers. Their expertise and dedication have driven the development of AI solutions, trusted and used by millions of people every day. We are immensely proud of their achievements and their unwavering commitment to advancing the capabilities of AI.
Ruth Mierowsky
Co-Founder
Head of AI &
Product
Seattle, USA
Avishkar Misra
Founder
CEO
Canberra / Sydney
Australia
Ashwin Srinivasan
Co-Founder
Head of Product Infrastructure
Sydney, Australia
Jag Misra
Co-Founder
Head of Marketing and Community
Canberra / Sydney
Australia
Experience matters
Explore our team's achievements in areas such as product recommendations, ad targeting, IoT data synthesis, Natural Language Processing (NLP) and medical image analysis to see how we can transform your business with AI.
Amazon Go
AmazonGo is a re-imagining of what a retail shopping experience should look like. Avishkar and team invented new solutions and capabilities. Computer vision, sensors, hardware and artificially intelligent algorithms blend into the background to create a magical shopping experience with AmazonGo.
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Patents:
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US9996818B1 - Counting inventory items using image analysis and depth information
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US10882692B1 - Item replacement assistance
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US10169677B1 - Counting stacked inventory using image analysis
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US10789483B1 - Image based inventory item counting
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US10949801B1 - Inventory item release apparatus and method
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US10671856B1 - Detecting item actions and inventory changes at an inventory location
Product Recommendations
"Once in a decade leap", was what Jeff Wilke (CEO of Amazon's Consumer Division) had called the algorithm created by our founder and CEO Avishkar Misra at his re:MARS 2019 keynote.
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Avishkar invented a new formulation for product recommendation which was 2x better than Amazon's state of art in recommendation.
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Video - Jeff Wilkie's Talk at re:MARS 2019
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Paper - The Effectiveness of Two-layer Neural Network for Recommendations
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Article - The History of Amazon’s Recommendation Algorithm
Patent:
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US10290040B1 - Discovering cross-category latent features
NLP-Powered Multilingual AI Features
Ruth, our co-founder built a multilingual knowledge-base recommendation system that utilized NLP techniques to provide real-time article and similar case suggestions. Additionally, Ruth and team developed a multilingual topic modeling feature to give customers a better understanding of their key feedback and problem areas. Furthermore, real-time language detection APIs were implemented for use in call-routing and sentiment analysis, enhancing the overall user experience and operational efficiency.
Medical Image Anlysis
High Resolution CT scans are a non-invasive way to help radiologists diagnose diseases. Yet going through 100s of images per patient can be time-consuming. LMIK project was looking at ways to automate the analysis of lung anatomy and diseases in scans, to help radiologists.
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Papers:
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Misra et al - Automatic lung segmentation: a comparison of anatomical and machine learning approaches
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Misra et al - Incremental learning for segmentation in medical images
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Massoptier et al - Combining Graph-cut Technique and Anatomical Knowledge for Automatic Segmentation of Lungs Affected by Diffuse Parenchymal Diseases in HRCT Images
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Massoptier et al - Automatic lung segmentation in HRCT images with diffuse parenchymal lung disease using graph-cut
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Rudrapatna et al - LMIK - learning medical image knowledge: an Internet-based medical image knowledge acquisition framework
Incremental Engineering
Software engineering of vision systems is challenging when data trickles in and the algorithms need to evolve over time. Avishkar used incremental knowledge acquisition techniques to provide ways to deal with the changes rapidly and in doing so speed up the development process, and accuracy over time.
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Papers:
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Misra et al - Incremental learning for segmentation in medical images
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Misra et al - Incremental learning of control knowledge for lung boundary extraction
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Misra et al - Incremental system engineering using process networks
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Misra et al - Incremental engineering of computer vision systems
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Misra et al - Impact of quasi-expertise on knowledge acquisition in computer vision
Aerial image and IoT sensor data synthesis for ML
Combining multi-spectral images (from planes, drones or satellite) with other sensor data, can help us track health of an individual tree in an orchard. Avishkar and team invented a method to align multiple images, so that we could use distributed compute on big data to analyse 100,000 acres in 15 minutes.
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Patent:
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US10089712B2 - System and method providing automatic alignment of aerial/satellite imagery to known ground features
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IoT sensors can produce valuable data, but can also expose user privacy. Avishkar and team invented an algorithm to synthesis IoT sensor data, which protects the original source data privacy, while allowing others to use it with machine learning algorithms to find patterns and make predictions.
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Patent:
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US11392850B2 - Synthesizing high-fidelity time-series sensor signals to facilitate machine-learning innovations
Ad Targeting using ML
Avishkar worked with the team that built the machine learning based display ad targeting platform at Amazon. Relevant ads means a better user experience and higher ROI for advertisers. In Q3, 2023, Amazon Advertisement generated 12 billion in revenue for the quarter.
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Patent: ​
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US11138631B1 - Predictive user segmentation modeling and browsing interaction analysis for digital advertising
Image Attribution: By SounderBruce - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=56922594 By Microsoft Corporation - Microsoft Corporation, Public Domain, https://commons.wikimedia.org/w/index.php?curid=94996985 By Pentagram Studio - https://amazon.com/, Public Domain, https://commons.wikimedia.org/w/index.php?curid=130197302 By Microsoft - Screenshot of the app, Fair use, https://en.wikipedia.org/w/index.php?curid=55234258 Pentagram Studio, Public domain, via Wikimedia Commons javatpoint, Public domain, via Wikimedia Commons