Imply
![Imply Logo Imply Logo](/files/vendor/imply669befcf2c789_1.jpg)
Overview
HQ Location
United States
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Year Founded
2015
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Company Type
Private
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Revenue
$10-100m
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Employees
51 - 200
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Website
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Twitter Handle
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Company Description
Imply is a company of developers, innovators, problem solvers, and mavericks, passionate about helping developers drive the next generation of analytics innovation. We are backed by Thoma Bravo, Andreessen Horowitz (a16z), Bessemer Venture Partners, Tiger Global Management, Khosla Ventures, and Geodesic Capital.
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Case Studies.
Case Study
AI-Driven Analytics Revolutionizing the Power Industry: A Case Study on Innowatts
Innowatts, an AI-enabled SaaS platform, was facing the challenge of managing and analyzing data from 40 million meters worldwide to provide near-real-time energy analytics and actionable business intelligence to utilities and retailers. The company needed to aggregate meter level data to create reports and recommendations for customers. The goal was to help energy providers be more predictive, proactive, and connected, unlocking grid edge opportunities, increasing customer value, and accelerating the transition to sustainable energy solutions. The challenge was not only to manage the massive data sets but also to provide insights and recommendations based on usage patterns, such as suggesting better electricity plans or products like electric vehicle battery storage.
Case Study
TrueCar Employs Imply Cloud for Enhanced Self-Service Analytics
TrueCar, a leading automotive digital marketplace, was facing challenges in analyzing real-time clickstream data to detect anomalies in user activity. The latency from its existing data warehouse and business intelligence stack was higher than desired, and the cost of scaling to support analytics on large and growing amounts of streaming data was a concern. TrueCar wanted to make analytics available not just to analysts, but also to business users in diverse functions such as marketing and finance. They sought to achieve this without the time and risk associated with building an end-to-end analytics capability from scratch.