Findability Sciences has unveiled Findability.Inside, an AI solution that ensures agile and repeatable deployment designed especially for traditional enterprises.
Findability.Inside targets to transform traditional enterprise companies to seamlessly integrate with superior AI technology into their existing tech platforms while unlocking new data-driven insights and business efficiencies.
Commenting on the launch, Anand Mahurkar, Founder & CEO of Findability Sciences said, “Our new offering – Findability.Inside, aims to provide a unique solution that has been carefully structured, specifically for traditional enterprises, keeping in mind the tech bottlenecks that they experience during their digital transformation journey. Our goal has always been to drive digital transformation in traditional enterprises by making them data superpowers, and it is with this intent we focus on simplifying their challenges to increase the effectiveness of data-driven solutions.”
One of the major challenges that traditional enterprises face while undergoing automation and digital transformation journey is to crack the ‘know-how’ of deploying AI solutions.
Given that the opportunities and advantages of using AI are tremendous and known, enterprises have long struggled to source a dependable solution that can be embedded seamlessly without disrupting the existing tech infrastructure.
Findability.Inside offers a superior suite of services like Machine Learning (ML), Natural Language Processing (NLP) and Computer Vision to deliver smarter results and increase efficiency.
The aim is to help traditional enterprises to reduce their dependency and adapt to a new user interface to re-imagine routine processes.
Enterprises can use Findability.Inside to add AI innovation to their offerings for price optimization, prediction and forecasting, segmentation and targeting, sales prospecting, customer service., etc. Findability. Inside features include but are not limited to:
- ML-powered predictions and forecasting
- NLP-driven auto-summarization for scanned documents at an industrial scale
- ML-driven insights from edge-IoT devices
- NLP auto-summarization of video meeting recordings
- ML-driven efficacy in online advertising