From vague demand to a
clear delivery decision.
Turn raw work requests into structured, decision-ready demands before you commit people, budget, or a date.
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What's the request?
Title
Mobile Checkout Performance & Conversion
Problem
Checkout drop-off rate is 34% on mobile, above benchmark. Likely a mix of load time and UX friction.
Outcome
Reduce mobile checkout drop-off from 34% to under 25% by end of Q3.
Likely owner
TBD — suspected Mobile Team
Vague asks don't become clear by themselves.
Most demands start as a Slack message or a meeting ask. What happens between that moment and committing to delivery determines whether you succeed or scramble.
- ✕A Slack message lands: "can we improve mobile checkout before Q3?" — vague, no owner, no success metric
- ✕You spend an hour extracting a real ask from three threads and a meeting note
- ✕You commit before the scope, owner, and risks are clear
- ✕Two months later: delivery surprises that could have been caught at intake
- ✕You prep a leadership update from scratch, again, every week
- ✓The hour of digging through threads is gone — the title, problem, outcome and scope are already drafted for review
- ✓Review and edit structured fields, with AI available inline for any weak field
- ✓Spot the gaps before committing: missing owner, unclear outcome, no out-of-scope
- ✓Mark it shaped and export a one-pager ready for the VP review
- ✓Every demand captured the same way — searchable and auditable
From a vague request to a clear decision — in three steps.
Not a capture tool. AI surfaces what's missing so you decide with eyes open — before you commit people, budget, or a date.
Capture the request
Paste any raw ask — a Slack message, an email, a leadership note. AI drafts it into a structured demand in seconds. If it's too thin to act on, you get two questions instead of a silent bad ticket — that's the point.
Clarify what's missing
The clarification brief separates what you know from what AI assumed — and the high-impact questions to answer before committing. Add context, ask the requester, or run a discovery.
Decide what happens next
With the delivery risk in front of you, decide: plan, commit, pause, or decline. Share a leadership-ready one-pager when you do.
Where this sits next to Jira or Linear
DeliverySheet runs the part before the tracker — the raw ask, what's still missing, and the decision to commit. Today it doesn't replace Jira or Linear: the ticket is still what you write once that decision is made. What it adds is the record of why you committed, which no tracker keeps. The full comparison →
What it actually looks like
Built for engineering and product leaders.
Engineering Managers, Directors and VPs of Engineering, and Product Managers. If you receive demands, shape work, and report upward — this is for you.
Built by one person who spent 18 years on the other side of this problem.
I'm Tan Gravam. Eighteen years in SAP, treasury and enterprise systems — now turning recurring problems into focused software, from Amsterdam. DeliverySheet exists because I kept watching teams commit to work nobody had made clear yet, and no tool would tell them what was still missing before the commitment was made.
It's a small product with a narrow promise, and I answer the support email myself — [email protected].
Why the product is built this way
Every constraint in DeliverySheet came from an argument I'd rather have in the open. These are the arguments.
- Turning a request into something you can decide on
Requests arrive as topics, solutions and half-sentences. What has to be added before one can be shaped, decided on, or refused honestly.
- How to decide what to commit to
What has to be true before a request becomes a promise, and what to do with the requests that never should become one.
- Capacity and what to measure
Sizing work in a unit that survives a budget meeting, and measuring what people are committed to instead of what they spend.
- After the commitment: tracking, closing and learning
What happens once the promise is made — status that says something, an outcome that is not just done, and a retrospective that is not theatre.
- Planning that stays honest
Separating fact from assumption, modelling cross-cutting work as what it is, and knowing exactly where AI belongs.
Shape your first demand today.
Paste a real ask from your backlog. You get a structured demand back — or the two questions that turn it into one. Takes two minutes.
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