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.
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Runs on the models you already trust
Built for sensitive internal knowledge
Scoped retrieval
By workspace, project and role
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
Start
begin
Read the inbox
agent · gmail_search
Sort by topic
categorize
Draft a reply
llm
Open an issue
agent · github_create_issue
Post to Slack
agent · slack_post
Draft with a prompt
Read yesterday's email, sort it by topic, and handle each kind.
Nothing is saved until you publish a version.
Build an agent you can actually look at
Steps, branches and loops on a canvas instead of one long prompt. Describe the job in a sentence and Ragenta drafts the flow for you to correct — and every version you publish is kept, so a change can be undone.
See how the canvas works →One knowledge base across every source
Drive, Notion, Slack, GitHub, your spreadsheets and your own database — indexed into a single knowledge base, or read live by an agent. Ragenta pulls the right context per question and shows which passages it used.
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.
Build an agent as a diagram: steps, branches and loops you can see, run and roll back.
One script tag puts a grounded assistant on your own website, answering from your knowledge base.
Open-weights model you can also run on your own hardware. A cheap default for high-volume, low-stakes answering.
Open-weights model with strong Vietnamese and Chinese. The economical choice for non-English workspaces.
Retrieval-tuned embedding model for English corpora where recall on long technical documents matters.
Speech-to-text for voice notes in chat and for indexing recorded calls and meetings.
Reads an answer back aloud, in chat or as a speech step inside a flow.
33 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 →Put it on your own website in one line
A chat bubble that answers from your knowledge base, on your domain. The allowed origins are checked on our server, every conversation is billed to your workspace, and you can read the questions visitors actually asked.
See what the widget reports →Do you deliver to Da Nang, and how long does it take?
Yes — Da Nang is next-day for orders placed before 4pm, and two days after that. Delivery is free above 500,000 ₫.
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Conversations today
94%
Answered from your documents
3,410
Credits spent
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.