Findability Sciences Launches Findability.Inside to Help Traditional Enterprises Quickly Become AI-Powered

Findability Sciences, a global provider of enterprise AI solutions, announced the launch of Findability.Inside, a suite of easily embeddable, white-labeled, ready-made AI solutions that ensure agile and repeatable deployments. With Findability.Inside, traditional enterprise companies can easily integrate superior AI technology into their existing products and solutions, while unlocking new data-driven insights and business efficiencies.

As traditional businesses undergo digital transformation, many companies understand the importance of AI but struggle with the adoption and deployment. Properly and efficiently embedding AI into existing infrastructures requires companies to custom-build AI integrations, which can be a paralyzing challenge, even for some of the largest enterprises in the world. Outsourcing one-off solutions has sustained enterprise companies so far, but the demand for a quickly deployable and repeatable solution continues to increase as more and more automated and data-focused business approaches are introduced every day. AI will be critical for businesses to remain relevant and profitable as the technology arms race heats up, and Findability.Inside is the superpower that grants a huge advantage.

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Findability.Inside will allow enterprises to embed the AI-core of the company’s proprietary flagship offering, Findability.AI, into their existing products. Using the Findability.AI-Core, which harnesses Computer Vision, Machine Learning (ML), and Natural Language Processing (NLP), Findability.Inside will power companies with AI at the back end in order to deliver a smarter, enhanced, and seamless experience in the solution’s native environment for end-users. 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, and more. The configurable and customizable solutions can create a capability differentiator for enterprises, gaining them an AI-edge over their competitors and peers.

Findability.Inside features include but are not limited to:

• ML-powered predictions and forecasting
• NLP-driven auto-summarization for scanned documents at industrial scale
• ML-driven insights from edge-IoT devices
• NLP auto-summarization of video meeting recordings
• ML-driven efficacy in online advertising

“Our advancements in technology and partnerships with leading global brands puts us in a unique position to effectively address exponentially growing market and customer needs,” said Anand Mahurkar, Founder & CEO of Findability Sciences. “We look forward to what today’s enterprises will achieve tomorrow with AI powered by Findability.Inside. It’s a game changer for expanding digital transformation for companies large and small.”

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Industry-leading enterprises are already adopting Findability.Inside with the following solutions:

Online Advertising

Mobile DSP operators can improve the real-time bid-optimization process. A Silicon Valley-based client embedded its DSP with Findability.Inside’s predictive AI technology to predict the propensity-to-click and optimize the bid price for impressions. The client can now offer competitive rates for CPC and CPM and the DSP users witnessed a 20% increase in CTR.

Intelligent Document Processing

Solutions created from Findability.AI’s extractive NLP summarization algorithm can generate automatic summaries from text-heavy documents. The technology is able to analyze keywords and context to extract relevant excerpts for end-users. Findability.Inside built an NLP-powered, add-on application that provides text summarization of scanned documents for a global leader in document imaging products to integrate with its scanner software through API.

Conversational AI

India’s leading manufacturer and distributor of FMCG products turned to Findability.Inside for their CSR initiative in providing English education to children via voicebots. Findability Sciences provided a voicebot with the voice of an 8-year-old child by applying advanced ML models trained using voice samples of the child. NLP and NLU algorithms enabled the voicebot to decipher alternative pronunciations and mispronunciations of audio and provide dictionary meanings in brand voice of child that caller could instantly relate to.

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