RAG.
Right out of the box.

Meet Ragie, the complete RAG service for all your AI applications.

Free 30-day
Pro Trial
No card required.

Built for simplicity

Ragie streamlines data ingestion, chunking, and multimodal indexing of structured and unstructured data. Connect directly to your own data sources, ensuring your data pipeline is always up-to-date.

Optimized for results

Built-in features such as summary index, chunk reranking, and flexible vector filtering help you deliver state-of-the-art generative AI.

Designed for scale

Seamless integration with agentic systems injects accurate and actionable knowledge into your workflows. No coding is required, saving precious engineering time and resources.

“Ragie helped us deliver in 3 weeks instead of 3 months.”

Evan Owen
CEO and Co-Founder, Glue

Led by Craft Ventures

Here’s how Ragie works.
And what makes it special.

curl -X POST https://api.ragie.ai/documents \

  • -H "Content-Type: multipart/form-data" \
  • -H "Authorization: Bearer $RAGIE_API_KEY" \
  • -F 'metadata={"category":"podcast"}'
  • -F 'file=@/path/to/your/file'

curl -X POST https://api.ragie.ai/retrievals \

  • -H "Content-Type: application/json" \
  • -H "Authorization: Bearer $RAGIE_API_KEY" \
  • -d '{
    1. "query": "what does chamath think about davos",
    2. "rerank": true,
    3. "filter": {"tags": {"$in": ["HR", "policies"]}}

}'

Ingest

The first step in a RAG pipeline is to ingest the relevant data. Use Ragie’s APIs to upload files directly, or take advantage of our built-in connectors to ingest data from sources like Google Drive, Notion, and Salesforce. Ragie handles multi-modal data, including images, PDFs, and PowerPoint presentations. Ragie keeps your data up to date by continuously syncing with the source.

curl -X POST https://api.ragie.ai/documents \

  • -H "Content-Type: multipart/form-data" \
  • -H "Authorization: Bearer $RAGIE_API_KEY" \
  • -F 'metadata={"category":"podcast"}'
  • -F 'file=@/path/to/your/file'

Chunking and indexing

The next step is to prepare the data for LLM processing. Ragie automatically chunks your data and embeds it into vectors using the latest multi-lingual LLMs. These vectors are then stored in a highly scalable vector database. Out of the box, Ragie builds multiple vector indexes, including chunk, summary, and keyword indexes.

Retrieval

The final step is to retrieve relevant chunks for your semantic search query. Use Ragie’s retrieval API to obtain the top_k relevant chunks, along with advanced retrieval features such as LLM re-ranking, top_k documents, document summaries, and hybrid semantic and keyword search. This ensures you get the most accurate and relevant results for your queries.

curl -X POST https://api.ragie.ai/retrievals \

  • -H "Content-Type: application/json" \
  • -H "Authorization: Bearer $RAGIE_API_KEY" \
  • -d '{
    1. "query": "what does chamath think about davos",
    2. "rerank": true,
    3. "filter": {"tags": {"$in": ["HR", "policies"]}}

}'

Connectors.
Direct to the source.

Connectors let you sync your data from popular sources like Google Drive, Notion, and Salesforce. With automatic syncing, your data stays up-to-date, ensuring your pipeline delivers accurate and reliable information.

Web scraper
coming soon
google Drive
SALESFORCE
coming soon
Notion
coming soon

We focus on RAG
so you don’t have to.

Building your own RAG pipeline can be complex, costly and time-consuming. With Ragie, you’ll benefit from deep experience as our team stays at the forefront of AI research and development, utilizing best-in-class techniques. Simply put, this is our core focus.

Focus on what matters the most – developing your applications – while we handle the rest.

No matter what you're building,
know what you're paying

Ragie offers simple, straightforward pricing without setup fees, hidden costs, or surprises.

Free 30-day
Pro Trial
No card required.