Tools & Platforms
AI Flow by TeleAI Recognized as a Breakthrough Framework for AI Deployment and Distribution by Omdia
SHANGHAI, July 11, 2025 /PRNewswire/ — AI Flow, the innovative framework developed by TeleAI, the Institute of Artificial Intelligence of China Telecom, has been recognized as a key role in the intelligent transformation of telecom infrastructure and services in the latest report by Omdia, a premier technology research and advisory firm. The report highlights AI Flow’s exceptional capabilities in addressing the edge GenAI implementation challenges, showcasing its device-edge-cloud computing architecture that optimizes both performance and efficiency as well as its groundbreaking combination of information and communication technologies.
According to the report, AI Flow facilitates seamless intelligence flow, allowing device-level agents to overcome the limitations of a single device and achieve enhanced functionality. The same communication network can connect advanced LLMs, VLMs, and diffusion models across heterogeneous nodes. By facilitating real-time, synergistic integration and dynamic interaction among these models, the approach achieves emergent intelligence that exceeds the capabilities of any individual model.
Lian Jye Su, Chief Analyst at Omdia, remarked that AI Flow has demonstrated sophisticated approaches to facilitate efficient collaboration across device-edge-cloud tiers and to achieve emergent intelligence through connective and interactive model operations.
The unveiling of AI Flow has also drawn great attention from the AI community on global social media. AI industry observer EyeingAI said on X “It’s a grounded, realistic take on where AI could be headed. ” AI tech influencer Parul Gautam said on X that AI Flow is pushing AI boundaries and ready to shape the future of intelligent connectivity.
Fulfill the Vision of Ubiquitous Intelligence in Future Communication Networks
AI Flow, under the leadership of Professer Xuelong Li, the CTO and Chief Scientist of China Telecom and Director of TeleAI, is introduced to address the significant challenges of the deployment of emerging AI applications posed by hardware resource limitations and communication network constraints, enhancing the scalability, responsiveness, and sustainability of real world AI systems. It is a multidisciplinary framework designed to enable seamless transmission and emergence of intelligence across hierarchical network architectures by leveraging inter-agent connections and human-agent interactions. At its core, AI Flow emphasizes three key points:
Device-Edge-Cloud Collaboration: AI Flow leverages a unified device-edge-cloud architecture, integrating end devices, edge servers, and cloud clusters, to dynamically optimize scalability and enable low-latency inference of AI models. By developing efficient collaboration paradigms tailored for the hierarchical network architecture, the system minimizes communication bottlenecks and streamlines inference execution.
Familial Models: Familial models refer to a set of multi-scale architectures designed to address diverse tasks and resource constraints within the AI Flow framework. These models facilitate seamless knowledge transfer and collaborative intelligence across the system through their interconnected capabilities. Notably, the familial models are feature-aligned, which allows efficient information sharing without the need for additional middleware. Furthermore, through well-structured collaborative design, deploying familial models over the hierarchical network can achieve enhanced inference efficiency under constrained communication bandwidth and computational resources.
Connectivity- and Interaction-based Intelligence Emergence: AI Flow introduces a paradigm shift to facilitate collaborations among advanced AI models, e.g., LLMs, vision-language models (VLMs), and diffusion models, thereby stimulating emergent intelligence surpassing the capability of any single model. In this framework, the synergistic integration of efficient collaboration and dynamic interaction among models becomes a key boost to the capabilities of AI models.
See AI Flow’s tech articles here:
https://www.arxiv.org/abs/2506.12479
https://ieeexplore.ieee.org/document/10884554
AI Flow’s First Move: AI-Flow-Ruyi Familial Model
Notably, TeleAI has just open-sourced the first version of AI Flow’s familial model: AI-Flow-Ruyi-7B-Preview last week on GitHhub.
The model is designed for the next-generation device-edge-cloud model service architecture. Its core innovation lies in the shared intermediate features across models of varying scales, enabling the system to generate response with a subset of parameters based on problem complexity through an early-exit mechanism. Each branch can operate independently while leveraging their shared stem network for computation reduction and seamless switching. Combined with distributed device-edge-cloud deployment, it achieves collaborative inference among large and small models within the family, enhancing the efficiency of distributed model inference.
Open-source address:
https://github.com/TeleAI-AI-Flow/AI-Flow-Ruyi
About TeleAI
TeleAI, the Institute of Artificial Intelligence of China Telecom, is a pioneering team of AI scientists and enthusiasts, working to create breakthrough AI technologies that could build up the next generation of ubiquitous intelligence and improve people’s wellbeing. Under the leadership of Professor Xuelong Li, the CTO and Chief Scientist of China Telecom, TeleAI aims to continuously expand the limits of human cognition and activities, by expediting research on AI governance, AI Flow, Intelligent Optoelectronics (with an emphasis on embodied AI), and AI Agents.
For more information:
https://www.teleai.com.cn/product/AboutTeleAI
Photo – https://mma.prnewswire.com/media/2729356/AI_Flow.jpg
Tools & Platforms
DXC Technology’s AI-Powered Tendia Solution Slashes Bid Writing Time for Ventia
DXC Technology Company (NYSE:DXC) is one of the cheap IT stocks hedge funds are buying. On July 3, DXC Technology announced the deployment of an AI-driven bid writing solution called Tendia for Ventia. Ventia is one of the largest essential infrastructure service providers in Australia and New Zealand.
The new platform significantly reduces the time required to draft initial bid responses for major infrastructure contracts, cutting it from days to minutes, thereby enhancing Ventia’s ability to quickly respond to complex and high-value tenders. The Tendia solution was developed in collaboration with DXC and was deployed in just 4 months.
An IT security specialist inspecting a corporate network server for any malicious activity.
It works by automating the time-consuming process of sourcing and synthesizing information from extensive document libraries. Tendia allows their teams to focus on higher-value work, deliver more accurate proposals, and respond more quickly to multi-million-dollar tenders.
DXC Technology Company (NYSE:DXC) provides IT services and solutions internationally.
While we acknowledge the potential of DXC as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you’re looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock.
READ NEXT: 30 Stocks That Should Double in 3 Years and 11 Hidden AI Stocks to Buy Right Now.
Disclosure: None. This article is originally published at Insider Monkey.
Tools & Platforms
Retail accelerates investments in generative AI
Dive Brief:
- Over half (56%) of retail organizations have upped their generative AI investments compared to last year, according to a report by Capgemini.
- Retail is among the top five industries most advanced in adopting AI agents, with 18% having implemented AI agents or multiagent systems, according to the report.
- Across industries, around 40% of organizations tracking ROI expect to achieve positive returns from AI within one to three years.
Dive Insight:
Generative AI has dominated the retail landscape with its various use cases from content creation to consumer-facing tools and more. As companies like Walmart and Target lean further on generative AI, the tech is making its mark in both how customers interact with retailers and behind-the-scenes workflows.
“Gen AI and agentic AI have unique capabilities, making them suitable for specific, non-overlapping tasks,” Sahil Chandratre, head of strategy, analytics and consumer insights for Reliance Retail, said in a statement. “For example, Gen AI is capable of addressing front-end tasks like customer communication and scheduling, and agentic AI is great at handling backend and complex activities such as billing and reconciliation. Systematically deploying the two in relevant areas can lead to synergies and streamlined workflows.”
When H&M introduced an AI-powered HR agent to streamline recruitment and candidate experience, it reduced time-to-hire by 43%, the report found. Additionally, employee attrition decreased by 25%.
Deploying different uses for AI is a balance Walmart and Amazon, among other companies, have attempted to strike.
Walmart’s generative AI shopping assistant Sparky, announced last month, can summarize reviews and help shoppers plan purchases. Amazon continues to push its own generative AI, including by introducing its next-generation Alexa+ assistant in February. Overall, shoppers are increasingly buying from generative AI’s product recommendations.
Meanwhile, companies like Visa and Mastercard are racing to create agentic AI tools that will perform as personal shopping assistants.
The shopping journey has become far more automated than some consumers may prefer. A recent KPMG report found that some shoppers may not fully trust AI or be comfortable with advanced shopping technology, like allowing AI to analyze personal customer data.
Tools & Platforms
Can AI and tech help streamline prior auths? The current pain points
For many physicians, the prior authorization process is one of the most frustrating parts of practicing medicine. What was intended as a cost-control measure has become a significant administrative burden, taking time away from patient care and adding layers of complexity to routine clinical decisions. Physicians often find themselves spending hours navigating insurer requirements, tracking down documentation, and enduring delays that can negatively impact patient outcomes.
But artificial intelligence may be part of the solution.
We spoke with Brad Boyd of BDO USA about how new technologies are being developed to streamline and even automate parts of the prior authorization process. From reducing paperwork to identifying which procedures are likely to be approved, AI tools are showing promise in helping practices manage approvals more efficiently—and with less stress.
AI systems can now integrate with electronic health records to gather relevant clinical data, match it against payer criteria, and generate documentation in real time. Some platforms can flag missing information or alert staff to likely denials before they happen. Others can help determine when prior authorization isn’t required at all, eliminating unnecessary steps.
While not a silver bullet, AI offers the potential to reduce the time and cost associated with prior authorizations. And as regulations continue to push for greater transparency and speed in the process, many in the industry believe AI will play a critical role in helping practices adapt.
In this episode, Boyd discusses the biggest pain points of the prior authorization process.
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