Quantiphi, an AI and Big Data start-up founded 6 years ago with more than 1,000 employees today, has raised $20 million to deepen market presence and invest in research & development. The investment comes from Multiples Alternate Asset Management, a leading private equity platform from India with $1.5 billion under management.
Quantiphi provides ‘Applied AI and Big Data’ solutions to global Fortune 500 companies across sectors a variety of sectors including consumer packaged goods (CPG), insurance, healthcare, retail, media & entertainment, EdTech, and technology. Quantiphi’s solutions have helped these enterprises “create tangible business value” by building smarter products, achieving frictionless customer experiences, automating complex processes, and managing risks.
Quantiphi was founded by four friends – Asif Hasan, Reghu Hariharan, Ritesh Patel and Vivek Khemani. The company has more than 1,000 employees today, with offices in Boston, Princeton, Toronto, Mumbai and Bengaluru.
“We are ecstatic to back Quantiphi, who is doing cutting-edge artificial intelligence work and having some of the world’s largest companies as their clients,” said Renuka Ramnath, founder and CEO of Multiples. “This is the result of a powerful combination of four founders bringing in complementary skills, a highly skilled and customer-obsessed team of 1,000+ engineers, and an impeccable client roster. This excited us to partner with Quantiphi, having chased the artificial intelligence thesis for the last two years.”
AWS Data & Analytics Competency
At the same time, Quantiphi has achieved the Amazon Web Services (AWS) Data & Analytics Competency status. This recognition would further substantiate Quantiphi’s expertise in large scale data analytics and machine learning deployments by using the tools, techniques, and technologies of working with data productively, at a large scale on AWS solutions.
Achieving the AWS Data & Analytics Competency would differentiate Quantiphi as an AWS Partner Network (APN) proficient in:
- ImplementIng “scalable, adaptable and robust” data lake instances to store all business data in one place that can manage multiple source data.
- Designing and implementing data migration strategies at scale, as well as set up destination server architecture.
- Extracting business value using Big Data to harness data and applying machine learning concepts to huge quantities of data, and business analytics.
- Creating end to end ingestion workflows and ETL pipelines for batch or streaming processes with architectures using different technologies.
- Implementing a distributed data warehouse using enterprise distributions.
- Building cost-effective, scalable data lake cloud platforms by setting up an intake pipeline with security protocols and real-time insights.
- Configuring security measures of policies, controls, and procedures to protect data and infrastructure.