Decide what to build nextwith receipts, not vibes
Prioritization meetings are decided by whoever argues loudest, because assembling the evidence is expensive: the candidates live in Jira, the arguments in meeting notes, the trade-offs in someone's head. Khint pulls the tickets into one work session, structures value against effort with AI, and turns the outcome into the 2×2 slide and the decision record, so the meeting argues about the call, not the paperwork.
The loudest voice wins
when the evidence is scattered
Every team means to prioritize on value and effort. In practice, the candidate tickets are in Jira, last month's arguments are in three different meeting notes, and the effort estimate is a shrug from engineering. Rebuilding that picture per candidate takes so long that most rounds skip it and run on recency and volume instead. Khint attacks the assembly cost: Memory gives the round one accumulating session, the palette pulls the Jira candidates into it, and reusable Actions turn the raw debate into a comparable summary per feature. The decision is still yours. It is just finally looking at the same page.
A prioritization round, end to end
Pull the candidates, structure the trade-offs, land the slide, write the decision back.
Open one session for the whole round
Start a Memory sessionnamed for the round: “Q3 prioritization”. From here on, every ticket you pull and every summary you draft lands in that session, and every AI action you run can see its compacted context. The debate stops living in six tabs and starts accumulating in one place.
Pull the candidates out of Jira
With Atlassian connected on the MCP page, the palette pulls tickets into the session: Pull issue for the ones stakeholders keep naming, Search to sweep for the rest (describe the query in plain words and Khint drafts the JQL, previewing it before it runs), Active sprint for what is already in flight. Each pull is a session entry the next steps can build on.
Structure the debate, candidate by candidate
Select the raw workshop notes wherever they live, hit Cmd+Shift+K, and run a saved Actionlike “Summarize value, effort, and risk per candidate”. You write that action once (type a description and press Generate for a first draft), and because the session context rides along, the summary reflects the tickets you pulled, not just the paragraph you selected. The structured output pastes back in place.
Land the 2×2 slide stakeholders expect
On the Mac, paste the summary onto a PowerPoint slide, select it, and run Transform to diagram from the palette. Khint renders a preview and you can force the 2×2 matrix framework (value on one axis, effort on the other), then save a
.pptxmade of real editable shapes. The slide that usually eats an evening of box-dragging takes a keystroke.Write the decision back, not just down
From the same palette: Change status on the winners, Add comment with the rationale on the tickets you deferred, and Create pageto publish the decision record to Confluence. Six months later, “why didn't we build this?” has an answer sitting on the ticket itself.
Why not a prioritization spreadsheet?
The scoring spreadsheet fails the same way every quarter: it is a copy. Someone re-types ticket titles into rows, the scores go stale the week after the meeting, and the reasoning behind a 3 versus a 4 evaporates. Khint keeps the round attached to the source instead: candidates are pulled from Jira into the session with their real content, the value/effort summaries are drafted from what was actually said, and the outcome goes back onto the tickets and into Confluence rather than into a file nobody reopens. If your candidates arrive as raw customer input rather than tickets, start with customer feedback to backlog first; once the winners are chosen, AI backlog refinement gets them sprint-ready, and the PowerPoint diagram generator covers every other slide the readout needs.
Common questions
Does Khint score my features for me?
No, and on purpose. Khint does not output a RICE number or rank your roadmap by magic. What it does is make the inputs to the decision cheap: it pulls the candidate tickets into one work session, turns raw discussion notes into a structured value / effort / risk summary per candidate, and drafts the slide and the decision record. The trade-off call stays with you and your stakeholders, where it belongs.
Where do the candidate features come from?
Mostly from Jira. With Atlassian connected on the MCP page, the palette gets Jira rows: Pull issue brings a specific ticket into your active Memory session, Search finds candidates (describe what you want in plain words and Khint drafts the JQL, showing it for confirmation before running), and Active sprint pulls the current sprint. Each pull lands as an entry in the session, so the AI actions you run afterwards can see what was pulled. Pull rows are opt-in: switch them on from the Atlassian card on the MCP page.
Can Khint make the value-vs-effort matrix slide?
Yes, on the Mac. Enable the PowerPoint companion on the MCP page, paste your prioritization summary onto a slide, select the text, and run Transform to diagram from the palette. Khint picks a fitting framework and renders a preview; if it chose something else, override it to the 2×2 matrix from the framework selector. Save the result as a .pptx with real editable shapes (not a screenshot), or as a transparent PNG.
What happens after the decision is made?
The palette writes it back where the team will see it. Change status moves the winners forward in Jira, Add comment leaves the rationale on the tickets you deferred, and Create page publishes the decision record to Confluence. Every write is also logged into the active Memory session, so the whole round (pulls, summaries, decisions) stays in one place you can revisit.
Bring the evidence to the meeting
Free with 10 AI actions and 5 captures per day. No credit card. Pull the candidates, structure the trade-offs, and let the decision write itself back.