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Software development can often be a lengthy and costly process, with enterprise organizations losing an average of $66 billion annually through inefficiencies in IT and software project scoping.
"Today’s enterprise organizations are constantly searching for ways to optimize the software development lifecycle. Platforms and tools to support this lifecycle are hyper focused on all the aspects of planning projects but fail to address how to execute on them. How do I build my software? With who? And where?" said Iba Masood, co-founder and CEO of Tara AI, a company that uses artificial intelligence and machine learning to speed the software development of products.
"Today, the process of scoping is a completely manual process, wasting time and capital on enterprises’ most critical software deliverables. IT departments lack talent transparency to appropriately assign development resources for product owners where it is mission critical to match the right skill set for your most challenging and time sensitive projects."
What Tara AI does is takes the data that enterprise companies already have, thanks to their project management systems, git version control and workforce directories, and uses it to predict how to best build the software, to determine how long projects should take and who should be the ones to execute them.
The company revealed that it has raised a $10 million Series A funding round led by Aspect Ventures, with participation from Slack Fund. Existing seed investors Y Combinator and Moment Ventures also participated in the round. This new funding brings Tara AI’s total amount raised to $13 million.
"Tara AI is the smart scoping platform, leveraging machine learning to speed the software development of products. Tara AI leverages machine learning to help enterprise organizations scope, allocate resources and build cutting-edge software products, enabling product teams to be on-time, on-budget and on-roadmap. Our engine analyzes open source repositories and code residing on the open web to structure, scope and build software projects for product owners across a variety of industries," said Masood.
Artificial intelligence is important, said Syed Ahmed, the company's co-founder and CTO because, "Most of the evolution in the software development space involving AI have been around engineering in general."
"From improved feature flagging to understanding product feedback. The remainder of the space has been tackling issue tracking and generic tools that are not specific to software product development in general. The software project management and development space has always been fragmented with a large focus on AI enabled tools aimed towards engineers. This puts us in a unique space, as we believe that SPM (Software Product Management) needs to be an inclusive and creative process.”
The typical customer for Tara AI is a Fortune 500 company that is "looking to improve their process of product development," Masood explained.
"Their teams are able to answer questions around which programming language or framework to utilize for a given project, how long the software development life cycle will run, and which people/resources to allocate to a given project."
Tara AI’s enterprise customers include Cisco, Orange Silicon Valley and Mower Digital. To date, its AI technology has ingested millions of data objects from publicly available data, and is growing at a rate of 20 percent.
In terms of return on investment, applications built with the Tara AI platform have saved product teams at least 40 percent of their initial scoping time in their product development. In some cases, teams have seen a 2x improvement in time to production due to quicker team allocation with Tara AI’s resource suggestions.
While Masood notes that, "The smart scoping space within product management software is currently a green field opportunity and we're excited to be creating a new category," there are others in the space that Tara is competing against, including Jira. But, unlike those others, which "are focusing on generalized project management software," Tara is focused "primarily on enabling teams to make better predictions and data-driven decisions around the PDLC (product development life cycle)."
With this new funding Tara, which grew its team from eight to 16 in the past few months, plans to double again to 32 team members in the next quarter. The company's founding team is primarily Google and Apple engineers.
"We’ve made a number of notable recent hires, including David Keith as Engineering Lead, Melita D'Souza as Frontend Engineer (former Apple maps team software engineer), Derek Wong as Product Management Lead (former Medallia and Elementum lead product manager), Silas Simon as Head of Sales and Customer Success (Former Director of Corporate Sales at Druva), and a slew of other back-end and front-end engineering hires from Google and Apple," said Masood.
Ultimately, Tara's goal is "to build a system of decision making that helps product teams become more productive as they use the platform."
"Today we’re helping teams understand the different pathways certain features and software can be built. In the near future we aim to become a central part of the SPM process where teams and leadership come together to keep track of their software product lines, make key decisions and maintain a streamlined process."
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Tara AI is the smart scoping platform, leveraging ML to speed the software development of products. Their engine analyzes open source repositories and code residing on the open web to structure, scope and build software projects for product owners across a variety of industries.
Today, the process of scoping is a completely manual process; enterprise companies are losing an average of $66Bn through inefficiencies in IT and software project scoping leading to cost and timeline overruns. However, F500 companies are sitting on a treasure trove of data in their project management systems, git version control and workforce directories. We connect to this data via an API, and using ML models, predict how to build their software (technical tasks), how long projects should take (timeline) and who will execute (team) when starting a new project.
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