Generative AI for Telecommunication
By revolutionizing telecommunications with cutting-edge generative AI solutions, we enhance efficiency, connectivity, and user experiences for a seamless digital futures.
Overview
This page is focused on consolidating the most innovative, impactful content relative to solutions for Telco customers using Generative AI. We have prioritized a series of Hackathons to jump-start the artifact creation and collection process. As we grow this site’s content, we will include links to blogs, code repositories, Whitepapers, Workshops, Immersion Days, videos, and many other document and media types to enable teams to leverage what has been built already, and extend to even more solutions for Telco customers. Please keep security as Job Zero when using the content within this site.
Data Synthesizer
Many companies need sample data for use cases in AI/ML. Two examples include realistic database rows for training data, and audio files for call center use cases. Creating those sample data sets is a problem for telcos — customer data is often extremely sensitive, and contains personally-identifiable information (PII) such as names and addresses. Data also often needs to be realistic to be useful. As such, there is limited adoption for some AI/ML use cases, whenever companies can’t safely derive data from their existing data sets. We propose a solution which uses large language models (LLMs) and text-to-speech software, to fabricate this training data at scale as per the descriptions of a schema, or a description provided by a solutions architect.
Dynamic IVR
Many companies, including telcos, find it expensive to have human operators answer customer support calls. As such, they try to use interactive voice response (IVR) bots, which interact with users and guide them to their answer automatically and without human involvement, or help direct their call to the human operator. Today, IVR bots are designed manually. It is difficult for companies to know (1) what issues IVR flows should be created to solve, and for any particular customer issue: (2) what information is needed to solve the issue, (3) what the most efficient IVR flow is to solve a particular issue, (4) what specific phrasing an IVR bot should use to prompt a user for information and help direct them through an IVR flow.
Note
This is an internal site focused on enabling our teams to learn and leverage from the content, but you own the ultimate decision of what is appropriate / approved for disclosure to customers. Do not assume that any artifact, link, playground portal is automatically approved to share with customers. If you are uncertain, reach out to Chris Featherstone or Ross McWalter or Chad Hendren to help evaluate.
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Testimonials
Engineering Manager
Alice Howard
AIGreenSolutions is a trailblazer in the realm of emerging solutions and AI use cases. As a forward-thinking professional constantly seeking innovative approaches to enhance efficiency and productivity.
Interior Designer
Nathan Marshall
From the very onset, AIGreenSolutions distinguished themselves with their commitment to staying on the cutting edge of technology. Our team of experts exhibited a profound understanding of emerging solutions and artificial intelligence, seamlessly integrating these advancements into practical use cases that addressed your unique business challenges.
Architect
Ema Romero
Equally commendable is AIGreensolution's emphasis on transparency and collaboration. Throughout our engagement, we maintained open lines of communication, keeping you informed at every stage of the process. This collaborative spirit instilled trust and confidence in our capabilities, making the entire journey smooth and enjoyable.
Manager
Ann Smith
What sets AIGreenSolution apart is its unwavering commitment to ongoing innovation. In an ever-evolving technological landscape, we remain at the forefront, continuously exploring new possibilities and refining solutions to stay ahead of the curve. This proactive approach instills confidence and assures clients that we are not just investing in today's solutions but in the promise of tomorrow's advancements.
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