AISWare AI²-TAC MaaS

As an intelligent MaaS base and enterprise-level one-stop AI platform for the era of LLMs, AISWare AI²-TAC MaaS enables comprehensive lifecycle support to enterprises for the developing large/small models and deploying of LLM applications, and effectively bridges the gap between general LLM and industrial applications to eliminate the pain points in developing AI and service processes, such as silo service and O&M, and low resource utilization. It effectively promotes the evolution and upgrade of enterprise's digital systems from process/data-driven to cognitive-driven, enabling one-stop digital intelligence services.

Product superiority

Product value

01

Simple Entrance to Developing AI Services

  • Offer diverse AI modeling methods and intelligent modeling experiences, lowering the AI development barrier
02

Lower Cost on AI Intellectualization

  • Enable a one-stop development experience with a highly integration from runtime environment, plug-in deployment, to multi-specification computing engine.
03

Improved Quality of AI Services

  • Offer solutions that directly and quickly invoke AI capabilities according to customer needs, and build high-quality AI services fast to meet business needs.
04

Universal AI Services & Industrial Models

  • Accumulate excellent large/small models throughout various industries, and customers can fast access them through products, solutions and so on.

Application scenario

Enterprise's One-Stop LLM Training
  • Scenario-based guidance
  • One-stop training and assessment
  • Fast tuning with quick entrance
Enterprise's LLM Application & Empowerment
  • Unified management of large/small scale models
  • Reasoning and deployment
  • LLM application development
DevOps Integration of LLM Services
  • Continuous integration
  • Ongoing optimization
  • All-round operation
Lightweight AI Application Development
  • Plug-in capabilities
  • Lightweight deployment
  • Minimized implementation cost
AI Middle Office Upgrade
  • Fast convergence of LLMs
  • Smooth upgrade for LLM base
Model Training & Data Governance
  • Intelligent data generation
  • Data analysis
  • Data preprocessing
  • Data annotation

Customer success case

Empowering a Provincial Operator on Building a FMOps System Based on TAC MaaS

The customer leverages AISWare AI²-TAC MaaS to strengthen the foundations and expand capabilities of LLM, resulting in efficient unified management of heterogeneous resources, and a success in building a unified foundation for LLMs and FMOps systems. The Platform supports various processes for better decision-making, including data pre-processing, multi-task fine-tuning, domain knowledge enhancement, and model fusion. LLMs are applied to scenarios like intelligent Q&A, smart customer service, data collection, and intelligent O&M, improving business efficiency and experience.

  • 192 Managing heterogeneous GPU
  • 60% Increasing in computing utilization
  • 10+ Building LLMs
  • 10 Empowering application scenarios
Empowering a Provincial Data Intelligence Platform

With a digital intelligent Al middle platform based on the AISWare AI²-TAC MaaS, comprehensive Al development capabilities are built throughout the entire process, integrating data and data middle office for empowerment among B/O/M domains. This approach offers multiple intelligent methods and deployment options, avoiding traditional Al silo development such as non-unified data standards, service scheduling, permission management, disconnected end-to-end processes, unshared platform resources, and lack of data protection policies. By achieving comprehensive empowerment across various scenarios, the platform enables the full cycle of perception, cognition, and decision-making.

  • 9M Daily average call volume
  • 193 Online reasoning services
  • 180% Increasing usage frequency
  • 50% Decreasing services development work
Empowering a Group Customer on Building a Centralized Platform

The customer established a centralized platform by using AISWare AI²-TAC MaaS, achieving effective multi-tenant management. Through unified building, they avoided issues such as data silos among provincial companies, reduced the overall cost of developing machine learning capabilities, and enabled tenant-authorized models. This resulted in model sharing, viewing, usage, and recovery, reducing model exclusivity and increasing the popularity of excellent models.

  • 20+ Total tenants
  • 14k+ Model inference:
  • 357 Model trainings
  • 174 Model applications

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