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Why ResearchStudio Is the Reason EasyRAG Exists

Discover how ResearchStudio inspired the creation of EasyRAG, shaping its mission and features.

Vlad Racoare
8 min read
LaunchProductResearchStudio

Why Research Studio Is the Reason EasyRAG Exists

How a real research product shaped EasyRAG - and why the two now grow together

EasyRAG didn’t start as a generic “RAG-as-a-Service” idea.

It started as a real problem inside Research Studio.

Research Studio was built to help teams collect, analyze, and synthesize large amounts of research data - interviews, documents, notes, reports, and insights. Very quickly, one question kept coming up:

How do we let people actually work with all this data, instead of just storing it?

AI seemed like the obvious answer.
But practical AI turned out to be much harder than expected.

The moment RAG stopped being theoretical

On paper, Retrieval-Augmented Generation sounds simple:

  • embed documents
  • store vectors
  • retrieve relevant chunks
  • pass them to an LLM

In practice, inside Research Studio, it meant:

  • slow ingestion pipelines
  • fragile indexing logic
  • complex permissions
  • expensive experimentation
  • AI answers that couldn’t be trusted

Most existing solutions were either:

  • too abstract (great demos, weak foundations), or
  • too low-level (powerful, but heavy and slow to build on)

We didn’t need another chatbot.
We needed infrastructure that behaved like a product.

That’s when EasyRAG started taking shape.

Building EasyRAG as if it were a research tool

EasyRAG was designed by working backwards from Research Studio’s needs:

  • Research data changes constantly
  • Datasets must stay queryable in near-real time
  • Responses must be grounded, not confident guesses
  • Access must be scoped and secure
  • Frontend teams shouldn’t depend on backend complexity

Every architectural decision in EasyRAG comes from those constraints.

What emerged was not a Research Studio feature - but a standalone RAG foundation that could power it cleanly.

That foundation became https://easyrag.com.

How EasyRAG supports Research Studio today

Today, EasyRAG acts as the retrieval and reasoning layer behind Research Studio’s AI capabilities.

It enables:

  • AI-assisted synthesis over research artifacts
  • fast querying across large qualitative datasets
  • dataset-scoped access via secure tokens
  • streaming responses suitable for real research workflows

Most importantly, it allows Research Studio to evolve without re-engineering its AI core every time research workflows change.

RAG is no longer a fragile feature - it’s a stable layer.

Why EasyRAG is a separate product

At some point, it became clear:

the problem Research Studio was solving with RAG wasn’t unique to research tools.

Many teams face the same challenge:

  • product documentation
  • internal knowledge bases
  • user research repositories
  • support content
  • private datasets

So EasyRAG was extracted, hardened, and turned into a product that any team can build on, not just Research Studio.

Research Studio remains its most demanding real-world use case - and that’s exactly why EasyRAG stays honest.

A partnership rooted in real usage

This is not a “tool integration” partnership.

It’s a shared origin story.

Research Studio pushes EasyRAG with real constraints.
EasyRAG gives Research Studio the freedom to innovate faster.

Both products share the same belief:

AI should support understanding, not replace it.

Looking forward

As Research Studio grows, EasyRAG evolves alongside it:

  • better retrieval strategies
  • faster indexing
  • more control over datasets
  • stronger guarantees around trust and relevance

EasyRAG exists because Research Studio needed it.
And it keeps improving because Research Studio keeps demanding more from it.

If you’re building anything where AI needs to reason over your data, not generic knowledge - EasyRAG was built for that.

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