AI stands for “Artificial Intelligence” and plays a pivotal role in augmenting our technology stack. It enables us to leverage AI for various purposes, such as data analysis, automation, and decision support.
Find out about API and RAGaaS options for your team
API FAQs
Common API questions
RAGaaS FAQs
About RAGaaS service
RAGaaS user guide
Step-by-step integration
AI Model API Access
If you need to utilize a large language model (LLM) for your team’s use case and enable generative AI for content processing, the API can provide the tools you need.
The API is available if your team has the technical expertise and capacity to build out the use case, including the ability to create your own user interface and integrate it securely with prototypes, applications, or even production projects.
RAGaaS access
RAGaaS (Retrieval-Augmented Generation as a Service) is designed to provide contextually relevant answers from your team’s dataset. RAGaaS can seamlessly and efficiently integrate into using your existing systems.
It’s suitable for any team, including those with minimal technical background or limited capacity to create their own custom experience.
API FAQs
What can employees expect in terms of training and support as AI technologies become a part of our daily operations?
We are committed to providing adequate training and support to ensure a smooth transition. Comprehensive training programs and resources will be available to help employees adapt to these new tools.
What impact do we anticipate AI will have on our company's overall performance and competitiveness?
AI is expected to boost our competitiveness by enabling smarter decision-making, increased efficiency, and more innovative approaches across various sectors within the company.
Will AI technologies replace human roles within the company, or are they meant to complement existing roles?
AI is designed to complement human roles, not replace them. It will assist employees by automating repetitive tasks and providing insights, allowing them to focus on more strategic and creative aspects of their roles.
RAGaaS FAQs
What is the RAGaaS (or) IT Managed Knowledge Base Pipeline?
RAGaaS (Retrieval-Augmented Generation as a Service) is WD’s IT-managed pipeline for enterprise knowledge base integration with AI systems. Key components likely include Document ingestion – Automated processing of internal docs, wikis, and knowledge sources Vector embedding – Converting content into searchable embeddings for semantic retrieval
What problems does this pipeline solve for RAG developers?
It eliminates redundant efforts in creating custom connectors for multiple data sources and ensures clear source attribution and RBAC-based permission control.
What is RAG and why is it important?
RAG (Retrieval-Augmented Generation) allows large language models to access trusted external knowledge sources, transforming them from generic AI into domain-specific expert systems.
What is MCP and how does it complement RAG?
MCP (Model Context Protocol) enables LLMs to interact with external systems and perform actions, making them capable of executing tasks beyond simple conversation. Together, RAG provides knowledge (“what”), and MCP enables action (“how”).
How does the pipeline ensure security and compliance?
It uses Role-Based Access Control (RBAC) and Privacy by Design principles to ensure that only authorized users can access specific knowledge contexts.
What are the key features of the IT Managed Knowledge Base Pipeline?
- Zero coding setup
- Full transparency with source attribution
- Permission-aware indexing and retrieval
- Simple KB chat for debugging
- Integration with enterprise systems like JIRA, SharePoint, OneDrive, Confluence, S3, static URL many more
What APIs are provided for developers?
The pipeline offers APIs for:
- Authentication and token generation
- Bringing your own data (BYOD)
- Indexing data
- Retrieving and generating responses
How can users bring their own data into the pipeline?
Users can upload files directly or request indexing through the IT Managed KB UI services, which handle ingestion and metadata tagging automatically.
What tools and services does the pipeline leverage?
It uses AWS services such as S3, Lambda, EKS, and S3Vector, along with IT-managed services for authentication, scheduling, and UI interaction.
