Ragie
A managed context engine for agents that need reliable retrieval over company knowledge.
What is Ragie?
Ragie is a context engine for agents, assistants, and AI apps. It handles document ingestion, parsing, chunking, retrieval, and connectors so teams can add retrieval-augmented generation to products or internal workflows without rebuilding the whole knowledge pipeline.
Tools for building, hosting, testing, observing, connecting, and giving memory or computer access to AI agents.
See the full Agent Infrastructure guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
Giving a support agent grounded answers from docs, tickets, and PDFs
Building an internal research assistant over company knowledge
Syncing SaaS sources into a retrieval layer for AI workflows
Replacing fragile one-off embeddings scripts with managed ingestion
Fit to evaluate
Software teams adding RAG to customer-facing AI features
Operations teams that need agents grounded in documents
Founders comparing managed RAG infrastructure with custom vector stacks
Product teams that need connectors, ingestion, and retrieval quality fast
Business fit
Right for you if retrieval quality is blocking an AI assistant or agent rollout. Ragie saves engineering time when the hard part is connectors, document processing, and keeping context fresh. It is less necessary if your knowledge base is tiny, static, or already lives in a platform with good native AI search.
How to evaluate Ragie
Use this category when a business wants agents that do work across tools, APIs, browsers, and data sources.
Confirm the exact workflow
Map Ragie to one concrete workflow first, such as giving a support agent grounded answers from docs, tickets, and pdfs. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare tool-calling, memory, browser automation, evals, observability, and deployment controls.
Compare practical alternatives
Compare Ragie with other Agent Infrastructure vendors before committing to a contract or migration.
Validate cost and rollout effort
Ragie publishes a pricing page with a free starting tier and paid plans. Evaluate total cost by documents, pages, connectors, retrieval volume, team needs, and whether production support or higher limits are required. Also confirm implementation time, support needs, and whether the technical setup matches your team.
Compare Ragie with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | Giving a support agent grounded answers from docs, tickets, and PDFs, Building an internal research assistant over company knowledge |
|---|---|
| Best-fit team | Software teams adding RAG to customer-facing AI features, Operations teams that need agents grounded in documents |
| Implementation effort | Technical setup and maintenance profile |
| Pricing check | Free plan + paid plans |
| Closest alternatives | Other Agent Infrastructure tools |
Ragie pricing
| Model | Free plan + paid plans |
|---|---|
| Snapshot | Ragie publishes a pricing page with a free starting tier and paid plans. Evaluate total cost by documents, pages, connectors, retrieval volume, team needs, and whether production support or higher limits are required. |
| Checked |
Common questions about Ragie
What is Ragie?
Ragie is a context engine for agents, assistants, and AI apps. It handles document ingestion, parsing, chunking, retrieval, and connectors so teams can add retrieval-augmented generation to products or internal workflows without rebuilding the whole knowledge pipeline.
What is Ragie used for?
Common use cases: Giving a support agent grounded answers from docs, tickets, and PDFs; Building an internal research assistant over company knowledge; Syncing SaaS sources into a retrieval layer for AI workflows; Replacing fragile one-off embeddings scripts with managed ingestion.
How much does Ragie cost?
Ragie publishes a pricing page with a free starting tier and paid plans. Evaluate total cost by documents, pages, connectors, retrieval volume, team needs, and whether production support or higher limits are required.
Who is Ragie best for?
Ragie fits Software teams adding RAG to customer-facing AI features, Operations teams that need agents grounded in documents, Founders comparing managed RAG infrastructure with custom vector stacks, Product teams that need connectors, ingestion, and retrieval quality fast. Right for you if retrieval quality is blocking an AI assistant or agent rollout. Ragie saves engineering time when the hard part is connectors, document processing, and keeping context fresh. It is less necessary if your knowledge base is tiny, static, or already lives in a platform with good native AI search.