This feature is only available on Chainloop’s platform paid plans.
Overview
Ask Chainloop is a native natural language interface embedded directly in the Chainloop Web UI. It lets you query your supply chain data, check compliance status, explore artifacts, and get answers about your organization’s security posture — all through conversational prompts, without leaving the dashboard. Ask Chainloop uses the same tooling as the Chainloop MCP server, but requires zero local configuration. It runs entirely within the platform using your existing web session.Why Ask Chainloop
Consuming and correlating supply chain data typically requires navigating the Web UI or using the API. The MCP server provides a powerful integration for AI clients, but requires setting up a local client and configuring connections. Ask Chainloop removes that barrier:- Zero configuration — no local binaries, API keys, or MCP client setup. It uses your existing session.
- Contextual awareness — the assistant knows which page you’re viewing and can tailor responses to your current context.
Prerequisites
Ask Chainloop requires an AI provider to be configured in your organization. Configure an AI provider in your organization’s Integrations page before getting started.How to Use
Open the Ask panel from anywhere in the dashboard:- Click the Sparkles icon in the top navbar
- Press Cmd+K (or Ctrl+K) to toggle the panel
Tips for Better Results
- Be specific — include project names, version numbers, or framework names. “Show compliance for the backend project against SLSA” works better than “show compliance.”
- Start with action verbs — “List,” “Show,” “Compare,” “Describe” help the assistant understand what you need.
- Add scope — narrow your question to a project, product, or version. The more context you give, the more precise the answer.
- Ask follow-ups — the assistant remembers your conversation. Start broad (“list my products”) then drill down (“show the compliance status for the latest version of that first one”).
- Use the page context — the assistant knows which page you’re on. If you’re viewing a project, you can ask “what policies apply here?” without repeating the project name.
Limitations
- Read-only — Ask Chainloop can query and retrieve data but cannot create, modify, or delete any resources (projects, policies, contracts, workflows, etc.).
- Responses may be inaccurate — AI-generated responses can contain mistakes. Always verify critical information before acting on it.
- Scoped to your organization — the assistant can only access data within your current organization and respects your existing permissions.
- No conversation history — each session starts fresh. Conversations are preserved while navigating within the same organization, but there is no way to save or revisit past conversations.
Capabilities
Ask Chainloop has access to the following tools to answer your questions:Organization and Discovery
- View account details and organization information
- Find evidence or materials by SHA256 digest, with full relationship tracing
Products, Projects, and Versions
- List and inspect products with their version history
- List and inspect projects
Compliance
- Evaluate compliance against security frameworks (SLSA, CRA, SSDF, and custom frameworks)
- Get aggregated compliance status across products with per-project breakdowns
- List and describe available frameworks and their requirements
Evidence and Artifacts
- List evidence artifacts with filtering by kind (SBOM, SARIF, container image, JUnit XML, and more)
- Download evidence content by digest
- View policy evaluation results for specific versions
Components
- List software components and packages with filtering by project or product
- Get detailed component information with dependency relationships
Policies and Contracts
- List policies (custom and built-in) and policy groups
- List and describe workflow contracts with their full schema
Related Resources
- How to use the Chainloop MCP server — connect your own AI clients to Chainloop
- LLM Support — configure AI providers for your organization
- LLM-driven policies — use AI to evaluate evidence against natural language policies
