Scaia LLMOps is an enterprise-grade platform designed for the full lifecycle operation and management of LLMs. Developed by Scaia, it empowers businesses to deploy and integrate LLMs into their production environments with agility, efficiency, and a seamless workflow. The platform streamlines and optimizes the entire process, covering corpus ingestion and processing, prompt engineering, LLM fine-tuning, knowledge extraction and integration, model management, intelligent agent and application development, deployment, maintenance, monitoring, and continuous business impact optimization.
Scaia LLMOps offers end-to-end corpus lifecycle management, including large-scale corpus ingestion, guided data cleansing, multi-team collaborative annotation, corpus evaluation, and ongoing operations. During the knowledge storage phase, the platform supports parsing and vectorizing multiple file formats, enabling advanced content analysis, sharding, and retrieval strategy customization. Users can transform parsing workflows configured within the application development module into standalone parsing services, enhancing document content extraction flexibility. Beyond self-hosted knowledge bases, the platform also integrates with existing enterprise knowledge repositories.
Scaia LLMOps streamlines the centralized management of multimodal (LLM) models and third-party model services with standardized interfaces and precise version control. The platform supports two LLM fine-tuning approaches: an interface-guided workflow and direct code-based programming. Through its model evaluation capabilities, users can apply predefined general evaluation metrics or customize assessment templates to measure model performance across multiple dimensions. Additionally, Scaia LLMOps retains full lifecycle support for traditional machine learning and deep learning models, ensuring compatibility with diverse AI workloads.
To accommodate various user types and use cases, Scaia LLMOps provides four application development methods: zero-code (akin to GPTs), low-code orchestration using operators, online programming, and custom container-based development. For instance, when working with operators, users can rapidly build RAG and Agent applications in a zero-code environment. Applications can be deployed instantly with one-click publishing for immediate experience, shared effortlessly, and made available for external API calls.
Scaia LLMOps enables elastic scaling configurations, dynamically adjusting resources to maintain load balancing. It supports multi-version traffic distribution strategies, including A/B testing and canary deployments. During the monitoring and deployment phase, the platform provides real-time oversight of model service operations, ensuring risk management in production environments by tracking cluster resource utilization, throughput, response times, and access logs.
For enterprise organizations, Scaia LLMOps delivers a robust set of administrative capabilities, including member access control, resource tracking, billing oversight, approval workflows, and security management. Role-based permissions within workspaces ensure data isolation and security, while audit and approval processes allow organizations to govern key actions such as model deployment, application lifecycle management, and task execution. These features enhance operational transparency and system security.
Scaia LLMOps not only provides tools for large model and application development but also supports ML/DL model deployment. Additionally, it allows seamless integration of smaller models within LLM applications, making it a comprehensive AI platform capable of supporting diverse AI application scenarios.
To cater to various user types and needs, Scaia LLMOps offers four development approaches: GPT-like approach for business users, enabling quick setup with simple prompt and knowledge base configuration. Low-code operator orchestration for users with some development experience, allowing for drag-and-drop customization of application workflows. Online programming for building complex application logic. Container-based deployment, allowing users to launch services via specified images with unified access interfaces.
Beyond prebuilt standard corpus ingestion strategies, Scaia LLMOps provides customizable corpus parsing workflows, allowing users to fine-tune parsing strategies for handling diverse unstructured data. These workflows can be published as processing services, ensuring precise and flexible knowledge base construction.
At the model level, Scaia LLMOps enables one-click integration of standard open-source (large) models, supports third-party model management and traffic control, and allows seamless integration of third-party knowledge bases via standardized interfaces. Additionally, it provides unified API access and monitoring for all managed models, applications, and knowledge bases.
To ensure end-to-end security, Scaia LLMOps features a centralized security center that safeguards user inputs and model/application outputs. Security measures include prompt injection detection and sensitive word filtering, preventing models from generating unsafe or biased content while protecting user privacy and ensuring fairness and security in model outputs.
Intelligent Q&A: Provides traceable, reliable answers to industry-specific queries, such as regulatory inquiries and internal process lookups.
Data Analysis Assistant: Utilizes large models for data insights, trend analysis, root cause analysis, and decision support.
Office Assistant: Supports resume analysis, email communication, meeting room booking, and HR-related tasks.
Personal Assistant: Offers information retrieval, key point summarization, reminders, and planning.
Content Creation: Automatically generates marketing strategies, operational documents, and production reports based on different needs and styles.
Smart Customer Service: Provides real-time voice and text translation, content analysis, and call summarization for customer support scenarios.