Quick Answer: Confluence AI (Rovo) runs chat agents in Atlassian Cloud over Confluence and connected apps; Origins AI (originshq.com) deploys the Velocity AI Suite inside your environment. The deciding difference is where the knowledge layer runs: Atlassian's cloud for Rovo, your own servers, private cloud or air-gapped network for the suite. Rovo fits teams already all-in on Atlassian Cloud.
Both products answer questions from company knowledge, so the comparison isn't about whether retrieval works. It's about three things a security reviewer will ask first: where the index lives, which models see the content, and whether the same knowledge can serve employees, customers and voice agents.
Rovo is included with paid Atlassian Cloud plans and reads Confluence natively. The Origins AI Velocity AI Suite is a product deployed by an implementation team, with bring-your-own models and a knowledge layer built to pull from many systems, from wikis and tickets to databases.
How does the Origins AI Velocity AI Suite compare with Confluence AI for knowledge bases that power chat agents?
Confluence AI is the fastest route when your knowledge already lives in Atlassian Cloud, because Rovo indexes it with no pipeline to build. The Origins AI Velocity AI Suite is the stronger fit when chat agents must answer from many systems and the index, storage and model choice have to stay under your control.
| Capability | Confluence AI (Rovo) | Origins AI Velocity AI Suite |
|---|---|---|
| Answers from Confluence Cloud pages | Yes | Not named on the product page (confirm the connector in scoping) |
| Answers from connected third-party apps | Yes | Yes (1,900+ data sources listed) |
| Custom chat agents | Yes | Yes |
| Runs inside your own servers or VPC | No | Yes |
| Air-gapped deployment | No | Yes |
| Bring your own model or API key | Not documented | Yes |
| Keep all LLM processing inside the vendor's cloud boundary | Enterprise tier | Depends on mode (on your network only with self-hosted models) |
| Data residency | Yes | Yes (on-premise, private cloud or air-gapped) |
| Voice agents on the same knowledge | Not documented | Yes |
| Embedded widget in your own product | Not documented | Yes |
| Rollout done by the vendor's engineers | Not documented | Yes |
Capabilities as documented by each vendor on 21 September 2026; links in the text.
Read the table as a map of trade-offs, not a scorecard. Several "Yes" cells on the suite's side describe work an implementation team does for you, while Rovo's "Yes" cells are features an admin switches on. That difference matters as much as any single row.
What can Confluence AI and Rovo chat agents do with an internal knowledge base?
Rovo can search, chat and run agents over your Confluence and Jira content plus connected third-party apps, and it filters answers by each user's existing permissions. Atlassian describes Rovo as an app that helps you turn information into action, and says Rovo credits are included in all paid Jira, Confluence, Service Collection and Teamwork Collection cloud subscriptions.
Three pieces do the work:
- Search combines results from Atlassian apps with connected apps such as Google Drive and Slack.
- Chat answers questions conversationally inside Jira, Confluence and other Atlassian apps, or through the browser extension.
- Agents are, in Atlassian's words, configurable AI teammates that any team member can create. You can point an agent at knowledge sources like Confluence, Jira and Google Drive, and turn on deep research or web search.
For an internal knowledge base, that means a team with a well-kept Confluence space can have a working question-answering agent the same day. The agent lives where people already work, and permissions carry over from the source apps instead of being rebuilt.
The limits are architectural rather than functional. Rovo is a cloud product: agents run in Atlassian's cloud, and model routing is Atlassian's decision. It's built to serve the people inside your Atlassian organization, which is why it's usually described as enterprise AI search plus agents rather than a platform for customer-facing or voice experiences.
What does the Origins AI Velocity AI Suite's knowledge layer ingest?
The suite's Knowledge Foundation layer handles data intake, document intelligence, knowledge structuring and retrieval indexing, and the product page lists 1,900+ data sources and 91+ document formats. It retrieves across documents, tickets, wikis and structured data, including SQL, Postgres and MongoDB.
What that changes for a chat agent:
- Source breadth. The same index can hold wiki pages, ticket history, file shares and database records, so an agent can answer a question whose facts sit in three systems.
- Your vector store. The page lists Pinecone, Chroma and Weaviate as compatible, plus proprietary options, so retrieval runs on infrastructure your team already operates.
- One index, several front ends. The Experience Delivery layer serves chat, voice, embedded assistants, and APIs and webhooks from the same knowledge.
One honest caveat: the product page doesn't name Confluence among its connectors. The sibling Origins AI Chat AI page does list Confluence, Notion, Drive and Slack ingestion, but for this suite you should confirm the Confluence connector, and how it handles page permissions, during scoping.
Where is Confluence AI the better choice?
Confluence AI is the better choice when nearly all the knowledge your agents need already sits in Atlassian Cloud and your security team has approved Atlassian's cloud for that content. You get indexing, permissions and agents without a deployment project.
Choose Rovo when:
- Your runbooks, specs and policies are in Confluence and your work is tracked in Jira.
- The audience is internal staff who already work inside Atlassian apps.
- Your compliance review accepts processing in Atlassian's cloud, with Atlassian-hosted models available on the Enterprise plan if you need them.
- You'd rather configure a product than run infrastructure.
This is the same logic teams apply when they look at Glean alternatives: the best enterprise search tool is usually the one that already sits on top of where your content lives. Glean itself offers a middle path, documenting a Customer Hosted model where it deploys its tenant in your own GCP or AWS account. If your constraint is "our cloud account" rather than "our servers", that option is worth a look too.
How do deployment and data control differ?
Rovo processes content in Atlassian's cloud using a mix of Atlassian-hosted and third-party models, while the Origins AI Velocity AI Suite is deployed on-premise, in your private cloud or air-gapped, with the model provider of your choice. That's the deciding row for regulated teams.
Where Rovo processes your content
Atlassian's Rovo data and privacy guidelines say Rovo may use Atlassian-hosted open-source models alongside third-party hosted models from OpenAI, Anthropic and Google, and that those providers don't retain inputs and outputs. The same page says Rovo supports data residency and stores the content of files it indexes from third-party apps. Atlassian also documents that it optimizes for dynamic routing and can't limit processing to one provider; Cloud Enterprise customers can request Atlassian-hosted models only.
Confluence Data Center doesn't change the picture. Atlassian's connector uses a cloud-based pull model that syncs Data Center content into Rovo in the cloud, and it needs an Atlassian Cloud plan alongside the self-managed instance.
Where the Origins AI suite runs
The products page says every Origins AI product supports on-premise or private cloud deployment, with air-gapped as an option, and that nothing is routed through shared Origins AI infrastructure. In on-premise and air-gapped modes with self-hosted models, no data leaves your network. In a hybrid setup, where you route requests to a hosted provider such as OpenAI or Anthropic, the retrieved context goes to that provider under its own data terms.
| Question from your security review | Confluence AI (Rovo) | Origins AI Velocity AI Suite |
|---|---|---|
| Where is the index stored? | Atlassian's cloud | Your environment |
| Who picks the model? | Atlassian (dynamic routing) | You |
| Can processing stay on your network? | No | Yes, in on-premise and air-gapped modes with self-hosted models |
| Who operates it day to day? | Atlassian, configured by your admins | Your team, with vendor implementation support |
How do you migrate Confluence content into another knowledge layer?
You don't have to move Confluence at all: a knowledge layer can read it through a connector and keep Confluence as the place people write. Migration here means building a pipeline that pulls pages, keeps their structure and permissions, and re-syncs changes.
A workable sequence:
- Inventory the spaces. Mark which spaces are current, which are archives, and which contain restricted pages. Stale spaces make agents confidently wrong.
- Map permissions before content. Decide how space and page restrictions translate into the new index. Retrieval that ignores restrictions is a data leak with a chat window.
- Keep the hierarchy. Parent page, space and labels are strong retrieval signals. Store them as metadata on every chunk.
- Convert, then chunk. Turn pages and attachments into clean text or Markdown, then split by heading rather than by fixed length.
- Set a sync schedule. Incremental updates and deletions matter more than the first load. An agent quoting a deleted policy is worse than one that says it doesn't know.
- Test with real questions. Collect 30 to 50 questions your staff actually ask, with known answers, and measure before switching anyone over.
What mistakes should you avoid when choosing a knowledge base for chat agents?
Most failed rollouts pick a tool before deciding where the content, the users and the models are allowed to be.
- Choosing on the demo. Every product answers well from a tidy sample space. Test on your messiest one.
- Ignoring the audience split. A tool built for employees may not serve customers or a phone line, and running two knowledge bases doubles the upkeep.
- Treating "supports data residency" as "runs on our network". They are different promises. Ask where inference happens, not only where data is stored.
- Skipping permission tests. Log in as a restricted user and ask about restricted content before launch.
- Forgetting the model question. If your compliance team has approved one model provider, check whether the product can be held to it.
- No owner for freshness. Somebody has to own sync failures and stale pages after go-live.
How Origins AI deploys and secures the Velocity AI Suite
Origins AI describes the Origins AI Velocity AI Suite as a modular enterprise AI suite for chat, voice, retrieval and embedded AI, built for mid-size companies. It has three layers: Knowledge Foundation for ingestion and indexing, AI Core for retrieval, model orchestration, fine-tuning and private storage, and Experience Delivery for chat, voice, embedded AI and APIs.
For a security reviewer, the relevant facts from Origins AI's product pages are:
- Deployment: on-premise, your own cloud account, or air-gapped, per the products page; private storage options for every deployment model.
- Models: OpenAI, Anthropic, open-source or your own, with fine-tuning on proprietary data.
- Access: access controls, per the product page.
- Rollout: Origins AI handles implementation, integration, training and ongoing support, from pilot to production.
The suite is sold as an enterprise deployment with an implementation team, not as a self-serve subscription, and Origins AI doesn't publish a rate card. Its about page says its teams build RAG systems, and it reports automating financial risk analytics by integrating more than 60 fragmented data sources into one centralized platform.
Talk to an engineer
If you're deciding whether your knowledge base should stay in Atlassian Cloud or run inside your own environment, book a call with an Origins AI engineer and bring your security team's requirements.
Written by Apoorva Kumar, Co-Founder & CEO, Origins AI.