Draaftly for enterprise strategy teams
Board papers that hold up under questioning
Strategy and insights teams are asked for a view faster than evidence can be gathered. Draaftly keeps the research, the numbers and the paper in one place, so the recommendation arrives with its working attached.
Why internal analysis gets picked apart
Internal work faces the toughest reviewers in the building, and usually the least time.
Numbers live in five versions of one spreadsheet
The deck, the business case and the model disagree, and the meeting turns into reconciliation.
Research is bought twice
A report commissioned by one business unit is re-commissioned by another because nobody could find the first one.
Assumptions are invisible
The case rests on a growth rate somebody chose months ago, and nobody can say who or why.
The answer is needed before the next committee
Deadlines are set by the governance calendar, not by how long good analysis takes.
Which parts of Draaftly help
Modules share the same evidence, assumptions and house style, so work done in one carries into the next.
- Draaftly ResearchComing soonMarket sizing, competitor tracking and customer research with every figure carrying its source and date.Explore Research
- Draaftly StrategyComing soonBusiness cases, market entry plans and board material built from one set of numbers.Explore Strategy
- Draaftly FeasibilityEarly accessCapital project appraisal with NPV, IRR, payback, DSCR and sensitivities calculated in code.Explore Feasibility
- Draaftly ProposalEarly accessStructured internal and vendor-facing documents that answer each requirement explicitly.Explore Proposal
A market size you can defend in the room
A fictional market, estimated top-down and bottom-up. The two estimates are shown side by side with the gap explained, rather than one tidy number nobody can source.
Every sample on this site uses fictional projects, markets and figures.
Top-down
- Households
- 2.40m
- Census 2025, table 2
- × Buy plant-based milk
- 31%
- Consumer panel, Q2 2026
- × Annual spend
- $96
- Retail price survey
$71.4m
Bottom-up
- Stores stocking the category
- 1,140
- Retail audit, 2026
- × Monthly sales per store
- $4,850
- 18 store interviews
- × 12 months
$66.3m
Market size: $66–71m a year · estimates differ by 7%
What changes for the team
- One assumptions register per project, with an owner and a status against each input.
- Market and competitor evidence that stays available to the next team that needs it.
- Board papers, decks and models that share one calculation engine, so they cannot contradict each other.
- A clear Draft, Review, Approved, Published trail for anything that goes to a committee.
- Scrutiny questions answered in the meeting, from the source behind the slide.
Or have our team do the work
Need a study done rather than a tool to do it with? Our team delivers fixed-fee research and feasibility work.
Questions people ask
How does this fit with the market research we already buy?
Subscribed and commissioned reports can be uploaded as evidence and cited alongside public data, so bought research and your own analysis sit in one place.
Can different business units keep their work separate?
Yes. Teams and roles control who can see each project, while a shared evidence library can be opened up where you want reuse.
Who signs off the analysis?
Your team does. Each project moves through Draft, Review, Approved and Published, and nothing is exported as final until a named person approves it.
Is our internal data used to train AI models?
No. Draaftly never uses your data to train AI models. Our AI providers, Anthropic and Google Cloud, do not use customer data from their commercial services to train their models by default. We explain this in full on our Trust page.
Which AI models does Draaftly use?
Draaftly uses Claude models from Anthropic, accessed through Google Cloud. Calculations run in our own calculation engine, not in the AI model.
Bring your next business case into Draaftly
We will set up your house style, your assumptions template and a first project with your own data.