Artificial Intelligence—Beijing Sinodata Technology Co., Ltd

Artificial Intelligence Business Module of Sinodata

Sinodata--A leader in utilizing artificial intelligence technology to explore business scenarios and drive business transformation and innovation. Established in 2003, the company has been exploring and applying artificial intelligence technology since its inception, successfully implementing it in the banking sector, accumulating abundant cases, and laying a solid technological foundation. The company’s headquarters is in Beijing, with AIGC research and development centers in Beijing and Hangzhou. It gathers outstanding talents in various disciplines such as large model algorithms, NLP algorithms, AIGC algorithms, speech algorithms, computer vision algorithms, AI model engineering, etc. We’re committed to the successful application of AIGC technology in various sectors, aiming to empower industries with AIGC technology. Sinodata has independently developed industry-specific multimodal large models, creating multiple artificial intelligence-related services and products suitable for vertical markets such as financial services, smart automotive cabins, live commerce, etc. The company has also independently developed end-to-end voice interaction capabilities (VAD, ASR, TTS, etc.), and simultaneously deployed foundational computational infrastructure at the underlying computational power center. Our vision is to reduce costs and increase efficiency for enterprise clients, improve service quality, and help enterprises create new business models. In the future, Sinodata will continue to strengthen the continuous development of engineering, algorithms, and related applications concerning artificial intelligence, promoting the deep application of artificial intelligence technology in more specific fields, and providing users with more intelligent products and services.

Introduction to Sinodata’s Artificial Intelligence Technology and Capability

Industry-specific large models
Enterprise-level Large Model Customization Service
Digital Human
Computational Power Center

Based on the open-source Transformer architecture, Sinodata has collaborated with industry partners to deeply explore practical scenarios and independently developed multiple industry-specific large models, covering banking, e-commerce, automotive, etc. We’ve dived into the specific large models of niche market and tailored to the knowledge structure and user cognition of specific scenarios. Our products and services exhibit significant advantages in language understanding, problem-solving, and recommendations within particular specific scenarios.

Sinodata’s industry-specific large models are pre-trained models with 13 billion parameters, using segmented industry data as training corpora. Through extensive training on massive text and images, the models manage to grasp language patterns, semantic logic, and creative characteristics in different specific domains. As a result, they can rapidly generate creative content that aligns with specified styles, scenes, and length requirements, possessing the ability to generate content in text, speech, images, and multiple modalities with logical and semantically coherent outputs.

Sinodata provides enterprise users with one-stop comprehensive solutions, including model adaptation and fine-tuning, private deployment, intelligent application development, and expert guidance to accelerate implementation across the enterprise. It allows enterprises to enjoy the cost reduction and optimization benefits of large models while avoiding the problems associated with using general large models, such as lacking industry-specific capabilities, understanding of private data, timely knowledge, and resulting in high costs. It also ensures the isolation of enterprise private data, achieving the security standard of “data not leaving the enterprise”.

Sinodata divides the creation of digital human into personality systems, attention systems, cognitive & thinking systems, expression systems, emotion systems, and learning & growth systems. To support different systems, the company possesses three major capabilities in digital human creation: voice capability, image capability, and ecological capability. In terms of voice capability, it includes scenario engines, proactive communication, instant language understanding, emotional speech, and other technical abilities. Regarding digital human’s image and driving capability, it incorporates 3D real-time rendering technology, supporting customizations of appearance, facial expressions synchronized with speech, and more. In terms of ecological capability, it supports third-party voice control and has scenario presetting capabilities.
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Sinodata’s digital human platform offers low-threshold, compact, and easily integratable 3D and 2D digital humans, allowing customers to seamlessly incorporate the capabilities of digital human into their businesses.

The Development of Private Computational Power Center

To meet the confidentiality requirements of core data and algorithm models, Sinodata can, based on customer demands, plan and develop an exclusive GPU computational power platform within its data center environment to ensure the secure isolation of core assets and critical workloads.

Cloud Computing Power Deployment

Sinodata collaborates with various cloud service partners, including Microsoft Azure, Alibaba Cloud, Tencent Cloud, Huawei Cloud, and others. Additionally, Sinodata strategically cooperates with a domestic AI computational power center, adopting cloud computing to provide computing services to customers. Cloud deployment facilitates the rapid deployment and debugging of AI businesses.

Self-developed Computational Power Center

Sinodata is actively advancing the planning and development of a domestically self-developed core computational power center. Through strategic partnerships and joint independent development with partners, it is gradually perfecting the layout of domestically owned GPU computational power and building its own computational power center. This is to provide infrastructure support for the accelerated implementation of more AI products.

Renovation of Computational Power Center Room

With the progress of artificial intelligence and large language model, Sinodata conducts an in-depth exploration into AI smart computing center business by leveraging accumulated system integration experience. Sinodata studies the characteristics of system including architecture and space, cooling systems, power systems, weak electrical networks in traditional data centers, based on the actual business needs of clients such as banks, governments, and financial institutions. The goal is to put forth highly efficient optimization and renovation solutions. We use advanced liquid-cooling technology to improve the cooling system of data centers and InfiniBand network technology to boost training efficiency in multiple computers and cards scenario. The goal is to efficiently optimize and transform traditional data centers into AI smart computing centers at a lower cost, supporting various AI business scenarios, including large language model distributed training, AIGC image generation training, online service inference, etc.

Computational Power Optimization Services

Sinodata provides users with optimization services, automated optimization tools, and basic software ecosystem construction in aspects such as systems, networks, storage, frameworks, etc. This includes but is not limited to foundational software frameworks like ROCm, deepspeed, Megatron, Colossal-AI, mindspore, paddlepaddle, PyTorch, TensorFlow, TensorRT, modelscope, as well as optimization tools such as aiacc-llm, LMDeploy, Medusa, vLLM, fastllm, AOE, Cuda operator-level optimization services, etc.

Products Corresponding to Sinodata Business Lines

Industry-Specific Models
Enterprise-level Large Model Customization Service
Computational Power Center

Banking Industry Large Models: Sinodata trains industry-specific large models for the banking sector by independently building datasets and corpora. The company utilizes a combination of “large models + small models”, leveraging existing AI achievements in banking to ensure performance and control over resources. It integrates internal banking system data, document data, and knowledge base data for rapid real-time training and learning, generating knowledge entries. Through a unified API, we can embed the models into the banking channel system, enabling customized programming. Specific scenarios include intelligent customer service responses, smart outbound services, digital wealth management consultation, intelligent digital tellers, smart navigation assistants, AI data platforms, and AI credit ratings. Sinodata’s banking-specific large models fully tap into and enhance the infinite value of enterprise data, significantly elevating the professional and intelligence capabilities of the banking industry.

E-commerce Industry Models: With a decade of deep involvement in the e-commerce industry, Sinodata has accumulated over 1 billion data resources, including 21 e-commerce language databases for specific products. The company’s data resources cover data concerning user behavior, products, transactions, etc. Starting from November 2022, the company has utilized these accumulated resources to prepare e-commerce industry-specific large models. These models support high-precision virtual image real-time driving, background customization for livestreaming, backend Q&A repositories, multimodal interactions, and customized solutions for client enterprises. The models can also engage in businesses like autonomously learning enterprise CRM customer data, product property data, and marketing case studies, iteratively updating large models in real-time. In an education-related e-commerce case, Sinodata combines digital human and large models to enhance courseware production efficiency, reduce costs, generate video courseware rapidly. In courseware we embed Q&A sessions, where digital humans provide real-time answers to students’ questions. We collected students’ learning tracking data and utilized big data and machine learning algorithms to optimize courseware design. Leveraging digital humans to replicate top-notch sales scripts and experiences, we conducted 24/7 online consultations and personalized course recommendations, reducing customer acquisition costs and increasing sales conversion rates. Real-time analysis of user behavior data was performed to further optimize sales strategies. We provided personalized services to each student to enhances course completion rates and satisfaction.

Automotive Industry Models: Sinodata’s automotive industry-specific large models focus on automotive cockpit scenarios and are tailored to the knowledge structure and user cognition of particular scenarios. Adapted to specific scenarios, we use domain-specific professional knowledge corpora and historical data for training. As a result, these models possess strong language understanding, generation capabilities, adaptability across multiple domains, and robust context awareness. They demonstrate powerful reasoning and quick response capabilities, enabling more intelligent, personable, and efficient semantic dialogue interactions with users. Specifically, they can monitor the state of all vehicle sensors, explain fault alarm reasons and solutions to passengers, introduce car features and performance, suggest adjustments to driving styles, connect various apps to provide passengers with one-stop services for life, entertainment, work, and shopping, and drive equipment such as mirrors, seats, air conditioning, entertainment systems, and navigation systems through a naked-eye 3D digital human on in-vehicle infortainment.

Enterprise-level Large Model Customization Service: Sinodata’s enterprise-level large models offer an all-in-one solution that seamlessly connects data, models, and scenarios. Based on a comprehensive understanding of different enterprise vertical scenarios, the models undergo scenario segmentation and are connected with the enterprise’s proprietary data. This integration enhances large models with vectorized database capabilities, enabling task distribution and data aggregation. Through the closed loop of data, models, and scenarios, there is continuous iteration, allowing enterprises to truly achieve digital transformation coupled with intelligent automation.

The Development of Private Computational Power Center: To meet the confidentiality requirements of core data and algorithm models, Sinodata can, based on customer demands, plan and develop an exclusive GPU computational power platform within its data center environment to ensure the secure isolation of core assets and critical workloads.

Cloud Computing Power Deployment: Sinodata collaborates with various cloud service partners, including Microsoft Azure, Alibaba Cloud, Tencent Cloud, Huawei Cloud, and others. Additionally, Sinodata strategically cooperates with a domestic AI computational power center, adopting cloud computing to provide computing services to customers. Cloud deployment facilitates the rapid deployment and debugging of AI businesses.

Self-developed Computational Power Center: Sinodata is actively advancing the planning and development of a domestically self-developed core computational power center. Through strategic partnerships and joint independent development with partners, it is gradually perfecting the layout of domestically owned GPU computational power and building its own computational power center. This is to provide infrastructure support for the accelerated implementation of more AI products.

Renovation of Computational Power Center Room: With the progress of artificial intelligence and large language model, Sinodata conducts an in-depth exploration into AI smart computing center business by leveraging accumulated system integration experience. Sinodata studies the characteristics of system including architecture and space, cooling systems, power systems, weak electrical networks in traditional data centers, based on the actual business needs of clients such as banks, governments, and financial institutions. The goal is to put forth highly efficient optimization and renovation solutions. We use advanced liquid-cooling technology to improve the cooling system of data centers and InfiniBand network technology to boost training efficiency in multiple computers and cards scenario. The goal is to efficiently optimize and transform traditional data centers into AI smart computing centers at a lower cost, supporting various AI business scenarios, including large language model distributed training, AIGC image generation training, online service inference, etc.

Computational Power Optimization Services: Sinodata provides users with optimization services, automated optimization tools, and basic software ecosystem construction in aspects such as systems, networks, storage, frameworks, etc. This includes but is not limited to foundational software frameworks like ROCm, deepspeed, Megatron, Colossal-AI, mindspore, paddlepaddle, PyTorch, TensorFlow, TensorRT, modelscope, as well as optimization tools such as aiacc-llm, LMDeploy, Medusa, vLLM, fastllm, AOE, Cuda operator-level optimization services, etc.

Solutions and Cases

Banking Industry Large Models

Strong distributed computing framework that supports large-scale model deployment.
Data security protection technology effectively that prevents leakage of critical information.
Independently-customized isolation environment that facilitates private domain training, enhancing model professionalism and adaptability.
Continuous autonomous that learns mechanism, quickly adapting to changes in business.
Detailed access control and security protection system that comprehensively safeguards core data assets.

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Car Intelligent Cabin Agent

A super assistant for car cabins that provides both passive responses and proactive services. It introduces short-term memory and long-term memory, tailoring to each user based on their past interactions, viewpoints, and attitudes. It accurately understands user intent when responding to demands and can provide proactive services by instantly capturing environmental information.
It is designed with five major functional levels:
1. Integrating with the AI central control platform to control various services such as entertainment, navigation, communication, and vehicle control.
2. Connecting with the intelligent cabin sensor system.
3. Integrating with the automotive industry chain upstream and downstream.
4. Connecting with local life services.
5. Connecting with household terminal devices to form a comprehensive coverage of AIoT (Artificial Intelligence of Things) in the Internet of Things.

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Live Commerce Digital Human (2D/3D)

Supports eight major livestreaming platforms and multi-channel promotion, facilitating merchants in achieving omnichannel marketing.
Offers over 100 rich avatars and voices, providing a customized experience.
With 30+ network anchors, it provides an ultimate real-time interactive experience with 24/7 uninterrupted live streaming.
Vivid and rich scene effects effectively guide the target audience into the live stream, increasing fan coverage.
Accurate semantic and emotion recognition, strong interactive capabilities, easily achieving a quick start for lightning-fast live streaming.
User-friendly operation, leading the industry with a high level of product intelligence.
Handheld product interaction.

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