Business Context
In large organizations, information is fragmented across multiple systems, formats, and teams.AI Knowledge consolidates these data silos into a unified knowledge base that powers enterprise-grade AI assistants — while preserving data sovereignty, compliance, and performance visibility. It empowers teams to:
- Centralize and govern internal documentation.
- Enable contextual responses grounded in trusted enterprise sources.
- Continuously evaluate and improve AI accuracy through structured testing.
Key Capabilities
Upload, process, and indexenterprise documents (PDFs, Word, PowerPoint, or HTML) with automatic extraction, embeddings, and versioning.
Configure RAG parameters such as chunk size, overlap, and embedding models to optimize retrieval precision and recall.
Connect multiple users and administrators to the same knowledge workspace with role-based access control.
Evaluate knowledge quality and AI performance using built-in testing, scoring, and feedback metrics for human-in-the-loop evaluation.
Enable native tools and features such as Image Generation, Web Browsing, Deep Research, and the Code Interpreter.
Add your own custom tools or MCP servers to access real-time information or execute actions directly from your agents.
Learning Journey
1
Video 1 — Uploading Documents and Basic Settings
Objective of the use case:
Learn how to configure a knowledge base and upload your first documents to feed an enterprise agent.What you’ll see:
- Reviewing base settings: prompt, model, and creativity levels.
- Adjusting chunk size, overlap, and embedding configuration.
- Activating self-query and end-user query definitions for specific subjects.
- Managing users and permissions within the workspace.
- Uploading documents (example: “ticket guideline”) and tracking processing status.
- Viewing extracted document content and validating ingestion success.
- Asking domain-specific questions (e.g., “What are the steps for great ticket management?”).
- Checking sources to confirm citation integrity.
- Monitoring analytics for feedback, consumption, and token usage.
Enterprise users can establish a governed knowledge base that enriches agent responses with traceable, document-backed information.
2
Video 2 — Advanced Settings, Tools, and Webhooks
Objective of the use case:
Discover how to use advanced configuration options, integrate external tools via MCP, and connect your workspace through APIs and webhooks.What you’ll see:
- Adjusting advanced options: image generation, model filtering, compression, and clarification settings.
- Enabling advanced features such as Canvas, Code Interpreter, and Web Search.
- Adding external MCP tools (example: DeepWiki) and testing repository retrieval.
- Understanding how tool descriptions help AI detect intent and execute the right functions.
- Viewing tool usage directly in conversation sources.
- Exploring API and Webhook tabs: retrieving project IDs and API keys for integration with AI Builder or enterprise systems.
- Configuring webhook payloads for events like document.created, question.asked, or document.updated.
- Managing additional data sources for hybrid retrieval.
AI Knowledge becomes a fully extensible system, connecting external data and automation pipelines while maintaining strict governance and security.
3
Video 3 — Testing and Evaluation
Objective of the use case:
Learn how to validate and measure AI accuracy using the built-in testing and human evaluation features.What you’ll see:
- Navigating to the Testing Dashboard via the sidebar.
- Adding or importing test cases from Excel or other formats.
- Creating a question (“What is an issue ticket?”) with a reference answer.
- Running tests to evaluate AI responses against expected results.
- Reviewing answer score, context score, and reference comparison.
- Understanding AI evaluation metrics and detecting overperformance or gaps.
- Acting as a human-in-the-loop evaluator: rating answers, context, and faithfulness manually.
The testing feature allows enterprises to monitor AI response quality, measure relevance and faithfulness, and continuously improve their knowledge agents.
Practical Applications
| Department | Use Case | Description |
|---|---|---|
| Customer Support | Knowledge Base Integration | Build searchable, document-driven assistants for faster support resolution. |
| HR | Policy and Procedure Assistant | Automate employee queries using verified internal documentation. |
| IT / Operations | Technical Documentation Retrieval | Connect engineers to updated, contextual technical content instantly. |
| Legal & Compliance | Regulatory Knowledge Base | Store and retrieve policies with traceable source verification. |
| Quality Assurance | AI Evaluation Framework | Benchmark AI accuracy and maintain consistent response quality. |
Key Takeaways
- Centralized RAG platform for secure, compliant knowledge ingestion and retrieval.
- Extensible integration layer via tools, APIs, and webhooks.
- Continuous improvement loop through testing, analytics, and human evaluation.
- Ideal for regulated industries requiring precision, explainability, and auditability.