Pipeworx vs Contextual AI
grounded answers from live public sources vs grounded RAG agents over enterprise data
grounded, citable answers from 877 live authoritative sources via one MCP gateway.
enterprise RAG agents — retrieval pipelines and grounded generation tuned on your organization's data.
Contextual AI builds RAG agents for enterprises: retrieval, reranking, and grounded generation engineered as one pipeline over an organization's own data, with accuracy as the headline. Pipeworx shares the grounding thesis but applies it to a different substrate — the world's live public and proprietary data sources, exposed to any agent over MCP. There's no pipeline to build: an agent connects to one gateway URL, asks in natural language, and gets structured answers carrying source, fetch timestamp, and a stable pipeworx:// citation. Enterprise-corpus accuracy is Contextual's problem; public-data currency and verifiability is ours.
Side-by-side
| Pipeworx | Contextual AI | |
|---|---|---|
| What gets grounded | Live public + proprietary world data (877 sources) | Your enterprise data via engineered RAG pipelines |
| Setup | Connect to one MCP URL — zero pipeline | Build/configure retrieval + generation for your corpus |
| Refusal behavior | ask_pipeworx_grounded returns answer:null + refusal_reason when data doesn't support an answer | Groundedness controls within the RAG pipeline |
| Citations | Stable pipeworx:// URIs resolvable by anyone | Attributions into your corpus |
| Consumer | Any MCP agent (Claude, ChatGPT, Gemini, Cursor…) | Your application via their platform/APIs |
When to use which
Use Contextual AI if
- You're building a production RAG system over enterprise documents and want the retrieval stack engineered for you
- Accuracy on YOUR data, with your domain's structure, is the core requirement
Use Pipeworx if
- The questions are about the world — companies, markets, regulation, science — not your file shares
- You need answers grounded in records anyone can verify, fetched live
- Your agent already speaks MCP and you want grounding without standing up infrastructure
Connect Pipeworx in one line
Add this to your MCP client (Claude Desktop, Cursor, VS Code, Claude Code, etc.) — no API keys required for public data sources.
{
"mcpServers": {
"pipeworx": {
"url": "https://gateway.pipeworx.io/mcp"
}
}
}Common questions
Is ask_pipeworx_grounded the same idea as Contextual AI's grounded generation?
Same goal — don't let the model invent — applied to different data planes. Contextual engineers groundedness into a RAG pipeline over your corpus. Pipeworx routes the question to an authoritative live source, then extracts the answer strictly from what that source returned, with the verbatim quote and an explicit refusal path. No corpus, no ingestion, no pipeline.