Gyld vs RAG
A managed business context layer vs. a retrieval architecture you build yourself
What is RAG?
RAG (retrieval-augmented generation) is a technique for grounding an LLM in external data: you chunk and embed documents into a vector store, retrieve the most relevant chunks at query time, and pass them to the model as context. RAG is an architecture pattern, not a product — you build and operate the pipeline yourself.
Gyld vs RAG: how they compare
RAG and Gyld solve the same problem — giving an AI real, specific knowledge instead of generic answers — at different layers. RAG is the do-it-yourself architecture. Gyld is a managed business context layer that implements retrieval over your company data and serves it to any agent through the Model Context Protocol (MCP), so you do not build or maintain the pipeline.
| Gyld | RAG | |
|---|---|---|
| What it is | A managed product (company brain + MCP servers) | An architecture pattern you implement |
| Data ingestion | Built-in connectors for Slack, Gmail, Notion, Drive, CRM, and more | You build each connector and sync job |
| Chunking & embeddings | Handled and tuned for you | You choose, build, and maintain |
| Access control | Per-company isolation + private/team/company visibility | You design and enforce it yourself |
| How agents use it | Any MCP agent (Claude Code, ChatGPT, Codex) plugs in via one URL | You wire retrieval into each app |
| Time to value | Connect apps and go — minutes | Weeks of engineering to production |
When to choose Gyld
- You want company context in the agents you already use without building a retrieval stack
- You need ingestion from many SaaS apps, with permissions and multi-tenant isolation, out of the box
- You want to expose knowledge to external agents over MCP, not just inside one app
When to choose RAG
- You are building a product with bespoke retrieval needs and want full control of the pipeline
- Your data and ranking logic are highly custom and a managed layer would not fit
- You already operate a mature in-house RAG platform
Frequently asked questions
Is Gyld a RAG tool?
Gyld uses retrieval-augmented generation under the hood — semantic search over your indexed company data — but it is a managed product, not a framework. You connect your apps and Gyld builds and operates the RAG pipeline, then serves the results to any MCP-compatible agent.
Do I still need a vector database with Gyld?
No. Gyld includes the full retrieval stack — ingestion, chunking, embeddings, vector search, and access control. You do not provision or manage a separate vector database.
Can I use Gyld instead of building my own RAG pipeline?
Yes. Gyld replaces the work of building a RAG pipeline for company knowledge: connectors, embeddings, retrieval, permissions, and serving it to agents. You keep control of what gets indexed and who can see it.
Give your agents real company context
Gyld is the business context layer for AI — connect your apps, build your company brain, and plug it into any agent over MCP.