Building a RAG system is an infrastructure project you didn't sign up for
Most teams that want a RAG system don't want to think about vector databases, embedding models, chunking strategies, or hybrid retrieval. They want to give their LLM access to their documents and get accurate answers back.
But RAG from scratch means making a long chain of infrastructure decisions before you can test whether any of it works. Which vector database? Which embedding model? How do you chunk a contract versus a policy document versus a court ruling? How do you handle Arabic? How do you monitor when retrieval quality degrades?
These are real engineering problems. They're just not the problems you actually want to be solving.
RAGista takes that stack off your plate.
What RAGista is
RAGista is a hosted RAG platform — RAG as a service. You bring your documents. We handle everything else: ingestion, chunking, embedding, retrieval, and the admin panel to manage it all.
You don't need to know what vector database is running underneath. You don't need to configure an embedding model. You get an API endpoint that takes a query and returns grounded, cited answers drawn from your document corpus.
The admin panel lets your team manage the document corpus — upload new files, remove outdated ones, see what's been ingested and when — without touching infrastructure or code.
How it works
Step 1 — Ingest your documents
Upload through the admin panel or push via API. RAGista handles parsing (PDF, DOCX, plain text, structured HTML) and chunking. Chunking is structure-aware: it respects headings, clause boundaries, and paragraph breaks rather than slicing at arbitrary token counts. This matters more than most people expect — bad chunking is a common root cause of retrieval failures that look like model failures.
Step 2 — Query
Your application sends a query to the RAGista API. Under the hood, RAGista runs hybrid retrieval — combining dense semantic search with sparse keyword search — and re-ranks results before passing them to the LLM. The response includes the answer and the source passages it was drawn from.
Step 3 — Monitor
The admin panel surfaces retrieval quality metrics so you can see if something is degrading — for example, when a new document type is added that the retrieval pipeline wasn't tuned for. For deeper evaluation, RAGista integrates with BEval Studio.
Arabic support
RAGista was built from the start to handle Arabic-language documents — a case that most off-the-shelf RAG tools handle poorly, if at all.
Arabic text creates specific challenges for retrieval: different tokenization behavior, right-to-left structure, dialect variation, and legal or formal registers that don't match the training distribution of general-purpose embedding models. RAGista's chunking and retrieval pipeline is tuned for Arabic document types — legal texts, regulatory documents, enterprise agreements — as well as English and mixed-language corpora.
Judger, our Arabic legal chatbot for a Jordanian law firm, runs on RAGista. The retrieval pipeline handles Jordanian court rulings and statutes in Arabic, with dialect-aware chunking and source citation on every answer.
Pricing
$100 / month — admin panel access, up to 1 million tokens ingested, hosted retrieval API. You don't need to think about infrastructure. Scale up as your corpus grows.
This is the right starting point for teams who want a production RAG system without the overhead of building and maintaining the stack themselves.
The alternative: own the code
If your team wants to run the infrastructure yourselves — full control over the stack, self-hosted, no monthly platform dependency — we offer a separate product: a $600 one-time project kickstart.
That gets you a production-ready FastAPI RAG server, configured for your document types, handed off to your team to deploy and own. No monthly fee. No dependency on us after handoff. This is a different product from RAGista — it's for teams who want to build on top of a solid foundation rather than use a managed service.
If you have a document corpus and need accurate retrieval on top of it, book a scoping call. We'll tell you in 30 minutes whether RAGista is the right fit or whether the code kickstart makes more sense.