Atlassian partner
Atlassian AI, Rovo and Jira-Confluence agents
Ovyka helps organizations turn Atlassian AI and Rovo into useful, governed use cases: augmented search, summaries, Jira-Confluence agents, controlled automation and team assistance.
The goal is not to add another chatbot. It is to make AI work on your real Jira, Confluence and JSM flows, with reliable sources, respected permissions and measurable outcomes.
Atlassian AI business cases
What Rovo should help teams do in practice
A strong AI roadmap starts with work gestures that cost time every week: finding, understanding, prioritizing, documenting, routing and acting. Rovo is most useful when those gestures are connected to your real Jira, Confluence and JSM spaces.
Find: retrieve the right context
Value starts when a project lead, support team or product team can find a decision, procedure or history without searching across several spaces.
Learn: understand before acting
Rovo and Atlassian AI can help summarize a Jira ticket, Confluence page, incident or request thread to accelerate decision-making.
Act: trigger action under control
Agents become useful when they propose or execute an action in a clear scope, with known sources, respected permissions and suitable validation.
Ovyka
What Ovyka delivers in practice
A buyer needs to know what will be produced, not only what the product can do. Ovyka connects AI strategy, Atlassian administration and field delivery.
Use case scoping
Prioritize business pain points, teams, volumes, sources and the criteria that will prove pilot value.
Jira-Confluence-JSM review
Audit projects, spaces, knowledge bases, permissions, workflows, automations, templates and documentation quality.
AI governance and security
Define authorized sources, owners, action limits, human validation, sensitive content and publication rules.
Pilot and scale-up
Configure, test with real teams, measure usefulness, correct data issues and prepare progressive rollout.
Pilot
Pilot Rovo without losing control
The first scope must be useful enough to convince teams, but focused enough to validate permissions, sources, answer quality and human review rules.
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1. Select a verifiable problem
For example: reduce support triage time, prepare sprint summaries or accelerate project onboarding.
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2. Limit the knowledge scope
Confluence sources, Jira projects, JSM queues and connectors are selected explicitly instead of opening the entire knowledge estate.
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3. Define what the agent can do
Suggested reply, comment, summary, ticket, page or recommendation: each action has an expected output and a validation rule.
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4. Measure and correct
Track adoption, quality, exceptions, content cleanup and new candidate scenarios before expanding.
Use case examples
Jira-Confluence scenarios teams can recognize
The best first pilots are not the most spectacular ones. They reduce a visible friction point, improve decision quality and help teams build confidence in AI usage.
Agile, product and delivery
Backlog grooming and sprint planning
- Before
- Incomplete tickets, duplicates, scattered dependencies, poorly linked epics and old decisions that are hard to find.
- With Rovo
- Jira-Confluence context summary, missing-information detection, grouping assistance and suggestions for team validation.
- Value
- Shorter ceremonies, a clearer backlog and fewer back-and-forths before execution.
Support, ITSM and service desk
Request triage and JSM assistance
- Before
- Uneven categorization, manual knowledge-base searches, rewritten replies and escalations that sometimes happen too early.
- With Rovo
- Contextualized agent or chat to qualify the request, find useful articles, summarize previous cases and prepare a controlled reply.
- Value
- More consistent qualification, better knowledge reuse and a smoother support experience.
Knowledge and documentation
Confluence AI and knowledge base
- Before
- Outdated pages, duplicates, incomplete procedures and content that is not structured enough to be found or reused.
- With Rovo
- Support for summarizing, structuring, identifying documentation gaps and turning decisions into usable pages.
- Value
- A clearer, more reusable Confluence base that is more compatible with reliable AI use.
PMO, managers and new joiners
Onboarding and faster project context
- Before
- A new joiner depends on meetings, scattered links and historical tickets to understand who does what and why.
- With Rovo
- Discovery path based on Confluence spaces, Jira tickets, decisions, glossaries, owners and active risks.
- Value
- Faster context acquisition and less dependency on already-solicited experts.
Run, incidents and operations
Incident, post-mortem and follow-up actions
- Before
- Incomplete timelines, scattered causes, forgotten post-incident tasks and documentation that is not updated.
- With Rovo
- Incident summary, decision extraction, PIR preparation, Jira task generation and matching with existing documentation.
- Value
- More actionable retrospectives and better-tracked corrective actions.
Leadership and portfolio
Executive steering and summaries
- Before
- Project information exists, but must be manually consolidated before a committee or decision.
- With Rovo
- Summary of risks, blockers, open decisions, critical requests and indicators from relevant Jira-Confluence sources.
- Value
- Better-prepared decisions, with less time spent reconstructing context.
Atlassian Rovo
Search, chat and agents to turn knowledge into action
Rovo brings together several usage families: search across work knowledge, contextual chat and specialized agents. Atlassian resources notably show examples around ticket organization, release notes, onboarding, support and automations.
Ovyka’s role is to translate these capabilities into use cases adapted to your organization: which spaces, which projects, which permission rules, which validations and which value metrics.
The main issue is not only the AI model: it is the quality of the context, rules and possible actions.
Connected search
Rovo helps teams use work knowledge across Atlassian applications and connected sources, without removing the need for a clean taxonomy.
Contextual chat
Chat becomes more useful when it answers against a precise Jira, Confluence or JSM context: ticket, page, project, incident, procedure or history.
Specialized agents
Agents can support targeted tasks: organize tickets, review a page, prepare a support reply or launch an action under validation.
Jira-Confluence agents
A useful agent is a framed agent
A Rovo agent should be designed as an operational assistant: it has a goal, context, sources, limits and an expected output. Without this framing, AI remains an interesting demo that is hard to industrialize.
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Signal
Jira ticket, JSM request, published page, incident or user question. -
Context
Authorized sources, Confluence spaces, Jira projects, history and business rules. -
Agent
Clear instruction, limited goal, expected output and quality criteria. -
Validation
Human review, permissions, traceability and publication or action rules. -
Action
Comment, summary, ticket, page, notification or usable recommendation.
Business context
Your Atlassian knowledge already exists, but it must become usable
Many companies already have the raw material for good AI use cases in Jira, Confluence and JSM: project decisions, incidents, recurring requests, procedures, tickets, decisions and lessons learned.
The real topic is turning this material into reliable context. Useful AI depends as much on Jira-Confluence practices as on Rovo configuration itself.
Useful data, but scattered
Jira, Confluence, JSM, knowledge bases and project histories already contain a lot of value, but not always in a format AI can use reliably.
Processes are not homogeneous enough
An agent does not sustainably compensate for ambiguous workflows, unused fields, poorly defined statuses or unmaintained documentation.
AI governance must be clarified
The key issue is knowing which sources the agent can use, what it can propose, who validates and how results are measured.
Ovyka support
Move from AI use case to reliable rollout
Ovyka supports French and European organizations with the scoping, configuration, governance and progressive industrialization of Atlassian AI, Rovo and Jira-Confluence agents.
AI business case scoping
Identify business friction points, volumes, teams involved and Rovo use cases that can create visible value quickly.
Jira, Confluence and JSM review
Audit projects, spaces, templates, workflows, fields, automations, knowledge bases and documentation quality.
Permissions and data hygiene
Clarify access, owners, sensitive content, authorized sources and cleanup rules before expanding AI usage.
Atlassian AI/Rovo configuration
Configure available capabilities, scopes, connectors, roles and administration practices.
Jira-Confluence agent design
Describe goals, instructions, sources, limits, human validation, expected outputs and failure scenarios.
Pilot with real teams
Test use cases in a concrete business scope, measure usefulness and correct data or process issues before expansion.
Administrator and user training
Train administrators, pilot teams and contributors on the right habits: sources, limits, validation and continuous improvement.
Continuous improvement
Track adoption, answer quality, exceptions, content to clean and new business cases to industrialize.
Scoping
Key points to frame before the pilot
These points should be clarified before broad rollout because they shape budget, security, adoption and maintainability.
What data can AI use?
Spaces, projects, knowledge bases and connectors must be selected with permissions and sensitive content in mind.
Who validates answers or actions?
The pilot states what the agent can suggest, what remains human and how errors are escalated.
How do we prove value?
Metrics should be simple: time saved, better knowledge reuse, fewer back-and-forths or higher triage quality.
What budget should we plan?
Scoping connects Atlassian licensing, configuration, data cleanup, training and post-pilot support.
Governance
Start with the use case, not the tool
Rovo becomes valuable when Atlassian knowledge is clean, responsibilities are explicit and results are measured on real examples.
- Start from a measurable business case visible to teams.
- Clean spaces, projects, templates and knowledge bases before expanding usage.
- Document owners, permissions, action limits and validation rules.
- Test on real examples, including edge cases and incomplete content.
- Plan training, administrator support and continuous improvement.
Atlassian resources
Atlassian Rovo
Atlassian’s official pages describe Rovo, search, chat and agents. Ovyka helps translate these capabilities into pilots adapted to your context.
Ovyka
Connect AI, Atlassian and business processes
Ovyka AI services
Automation, agents, orchestration and governance for use cases beyond Atlassian.
View AI servicesYour Atlassian partner
Scoping, licensing, Cloud migration, Jira, Confluence, JSM, support and TMA.
View Atlassian expertiseJira Service Management
Portals, service desk, ITSM, knowledge base, SLAs and Jira automations.
View Jira Service ManagementConfluence
Structure the knowledge base and documentation spaces that feed AI usage.
View ConfluenceOvyka references
See the portfolio of customers and organizations supported by Ovyka.
View referencesFAQ
What is Atlassian Rovo?
Rovo is Atlassian’s AI experience that helps teams find, understand and use their work knowledge through search, chat and specialized agents.
What are Rovo agents useful for in Jira and Confluence?
They assist targeted tasks: summarizing context, organizing tickets, reviewing or structuring documentation, preparing support replies or suggesting actions in a defined workflow.
Why work with a partner?
A partner connects business goals, Jira-Confluence configuration, permissions, governance, licensing, pilots and adoption into a coherent path.
Can Ovyka help define the first use cases?
Yes. Ovyka can audit your Jira, Confluence and JSM context, prioritize AI business cases, design a Rovo pilot and support teams through continuous improvement.
Want to identify the right Atlassian AI and Rovo use cases?
Let’s discuss your Jira, Confluence and JSM processes, operational friction points, knowledge quality and the first agents to test with your teams.