The AI workspace that turns your knowledge intoanswers
Grounded chat and autonomous agents over your own documents, apps and databases.
Annual enterprise contracts carry a 30-day refund window from the invoice date. After that, credits roll to the next term instead of being refunded.
3 sources
Runs on the models you already trust
Built for sensitive internal knowledge
Permission-aware retrieval
Inherited from every source
Private deployment
Your VPC or on-premise
Never trained on
Your content stays yours
Auditable answers
Sources, scores, full trace
Agents turn questions into cited answers
Ask in plain language. Ragenta plans the retrieval, picks the right tools, checks its own work, and answers with every claim linked back to the passage it came from.
Running 3-step plan
Works across every kind of knowledge work
Support, legal, sales, internal ops — describe the outcome and Ragenta assembles the right agent for it. No fixed templates. Or wire each step by hand when you need exact control.
One knowledge base across every source
Drive, Notion, Confluence, Slack, your ticket system, your warehouse — indexed into a single knowledge base that keeps each source's own permissions. Ragenta pulls the right context per question, so the model reasons over everything the asker is allowed to see.
Models, tools & connectors
Everything an agent can be given
Long-context reasoning model. The default for agents that plan multi-step work and read large documents end to end.
General-purpose chat model with strong function calling. A good fit for tool-heavy agents and structured extraction.
Multimodal model that reads scanned PDFs, screenshots and diagrams alongside text in the same conversation.
Multilingual embedding model covering Vietnamese and English in one vector space — no separate index per language.
Cross-encoder reranker that reorders retrieved chunks by true relevance before they reach the model's context.
Open-weight model you can run inside your own VPC when data must never leave your network.
Open-weight model with strong Vietnamese and Chinese performance at a low cost per token.
High-recall embedding model tuned for long technical documents and code.
Transcribes meeting recordings and call logs into the knowledge base with speaker turns preserved.
Layout-aware OCR for scanned contracts and forms — tables and headers survive the conversion to text.
Pulls tables out of PDFs and spreadsheets as structured rows so the agent can compute over them, not just quote them.
Splits documents on meaning rather than character count, so a clause is never cut in half across two chunks.
26 in the catalogue · more added regularly
Models and tools, always current
Ragenta is not tied to one provider. Pick a model per agent, swap it without rewriting a prompt, and give agents the tools and connectors they need — including any MCP server you already run.
Browse the catalogue →From the teams running it every day
“We had three separate search tools and none of them could answer a question. Ragenta replaced all three in a month, and the citations are the reason people actually trust it.”
Head of Operations
Logistics, 900 staff
“Support deflection went from a slide in a strategy deck to a number on a dashboard. The retrieval trace is what let us debug it ourselves instead of filing tickets.”
Customer Support Lead
B2B SaaS
“Permission-aware retrieval was the whole deal for us. Legal would not sign off on anything that indexed HR files into one bucket.”
IT Director
Financial services
“Our documentation is half Vietnamese, half English. One index handles both and an English question still finds the Vietnamese paragraph.”
Engineering Manager
Fintech, Ho Chi Minh City
“The agent does not just retrieve — it decides what to look up next. Watching it re-query after a weak first result was the moment this clicked for the team.”
Head of Data
Healthcare group
“We swapped the underlying model twice in a quarter without touching a single prompt. That flexibility is worth more than any single model benchmark.”
Principal Engineer
Enterprise software
From the blog
Read the blog →Chunking decides your RAG quality more than your model does
Teams reach for a bigger model when answers go wrong. Nine times out of ten the retrieval never surfaced the right passage in the first place.
An answer without a citation is a guess with good grammar
Citations are not a nice-to-have on top of a RAG product. They are the only mechanism a reader has to decide whether to trust what they just read.
When an agent beats a workflow — and when it does not
Agents are the right answer when the steps depend on what the previous step found. For everything else, a fixed pipeline is cheaper, faster and easier to debug.