Building a sales AI platform: Key principles and lessons learned
Moty Fania shares his experience implementing a sales AI platform that handles processing of millions of website pages and sifts through millions of tweets per day. The platform is based on unique open source technologies and was designed for real-time data extraction and actuation.
| Talk Title | Building a sales AI platform: Key principles and lessons learned |
| Speakers | Moty Fania (Intel) |
| Conference | Strata Data Conference |
| Conf Tag | Making Data Work |
| Location | London, United Kingdom |
| Date | April 30-May 2, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
In today’s sales and marketing landscape, knowing your customer is everything. Traditionally, this would be achieved by dedicated sales agents covering accounts that they know very well. However, account coverage complexity grows since agents can’t transform the vast quantities of data about their accounts into trusted insights in a timely manner. Moreover, there will never be enough salespeople to cover hundreds of thousands accounts. Moty Fania shares his experience implementing a sales AI platform built by the advanced analytics team at Intel IT to support the full cycle of sales. It continuously extracts and interprets massive amounts of internal and external public data and applies AI reasoning for taking the relevant actions. By imitating humans’ reasoning capabilities and decisions, AI technology helps processing extensive, disparate data sources and converts them into actions or actionable insights for salespeople. This may allow effective coverage of a much larger number of accounts and to gradually provide autonomous coverage by automating end-to-end sales services and actions. To enable all of this at scale, the platform is based on a streaming microservices architecture with a message bus backbone. It employs cutting edge open source technologies such as RAY, Snorkel, TensorFlow, TensorFlow Serving, and Python Kafka streams and was optimized to be easily deployed with Docker and Kubernetes. The platform supports different kinds of data and knowledge representations including knowledge graph, search, and more. In addition, it enables online deep learning inference at scale for natural language understanding and recommender engines. The platform and related advanced analytic capabilities have increased Intel’s revenue by approximately USD$500 million in the past five years. Topics include:
