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Sukhbir Benipal

Solr Consulting + Big Data Hadoop Architect

Sukhbir Benipal is our CEO. He is the brain behind the heuristic learning search algorithm used by the benipal shopping search engine and envisaged the social e-commerce platform, local and wholesale WANT Marketplaces.

Sukhbir created our B2B and B2C, O2O and C2C Marketplaces - WANT and Benipal. Live on the Android Play Store now.

He is in technology due to an epiphany, caused partly by sufficient quantities of argentinian wine on a cold winter night in New York. Having infamously flunked his M.A Economics exam, Sukhbir went on to run a Commercial Real Estate Finance company in Manhattan where he helped arrange over $ 500M in financing for different Hotels and Office Buildings.

He is responsible for code deployment across platforms and maintains our Hadoop / HBase, Search, Database and API backend clusters at our U.S datacenter.

Benipal went on to further create a self healing neural network with advanced image recognition and search for the benipal shopping search engine and built his own 10 TFlops supercomputer with 12TB RAM to help run it. For the shopping search engine, he built a ms response, massive multi PB scale image storage and delivery system. He also built his own 10G Router in 3 hours after finding out in the datacenter that Cisco would be too expensive.

He enjoys driving in the upper Himalayan ranges and likes building CentOS servers, having built his first one from scratch in two hours after reading an online how-to. His current server build / disassemble time stands at 45 minutes. Knows nothing about writing code and has an opinion on pretty much everything.

Thinks supercomputing is fun and Proud of his failure(?) with Artificial Intelligence.

Skills: CentOS, Lucene, SOLR, Hadoop HDFS, Yarn, MapReduce, HBase, mySQL, Storm, Spark, Kafka, Redis, MongoDB, Nodejs, Spring, Tomcat, HAProxy, Nginx, Android, iOS, Java, Parse, Firebase, geolocation, Linux, DataCenter, Networking, Elasticsearch, Solr Consulting in New York.

Marketplaces: Product Development for B2B Wholesale Marketplaces and B2C, O2O and C2C Mobile Shopping Marketplaces built around a Social Network and live Messaging with Photo and Video Sharing, Private Group Buying and Selling plus location based Local Users, Groups, Products and Deals Discovery. For United States and Global markets. Status – Launched on Play Store.
● WANT - Global B2B shopping
● Benipal - United States B2B marketplace
● WANT - B2C Marketplace in United States
● want local - O2O shopping marketplace
● beni - C2C shopping marketplace

Logistics: Stealth Mode Product Development for a pan United States plus hyper local logistics service to complement “Newco” Shopping Marketplace Shipping and Delivery. ● Created algorithm based optimum routing, Unique 10 digit ID based delivery location, live map delivery status, client initiated re routing, Image recognition based trusted recipient for delivery acceptance and live package plus payment confirmation.

Shopping Search Engine: 300 Million Products. 1.2B Titles and Descriptions. 1B+ Images. 12,000 Merchants.
● Voice Search and Image matching, recognition and search.
● Highly scalable infrastructure with average response times under 100ms.
● Contextual + Relational, neural network based Shopping Search Engine able to understand user queries and provide exact results for “ Blue Bedspread by Martha Stewart from Walmart or Macy's.com or around me for under $500”.
● High Volume Search + Big Data Infrastructure allowing Products and Search Queries to reflect most recent state.
● Built and Managed 40 High Performance Servers in Datacenter with 10G uplinks.

Search Engine Architect / AI Researcher / Supercomputing ● Added partial NLP to search, letting the computer “understand” the query.
● Created a self-healing, self-learning “Auto Product categorization” algorithm that can automatically analyse and categorize products in any of over 30,000 available categories. Successfully used and demonstrated success rate of around 85%.
● Built a 20 Tflops CPU based Super Computer with over 12TB RAM, 640 Cores and 1 PB Storage. Easy to add GPUs to increase total Floating Point computation.
● Theorized and Worked on Computer Vision with small GPU based cluster to provide a better understanding of how neural networks can understand images and “see” Videos.
● Theorized and Researched on Artificial Intelligence using various current open source projects and how integrated usage could provide a better understanding of neural networks and their application to live real world data scenarios by providing computers with the ability to “understand” different datasets, “see” images and videos and roughly match their interconnects.