RawKit is an evidence-first canvas for research and idea validation. Run the founder playbook end to end — market analysis, competitor analysis, go-to-market strategy and funding search — with every finding traceable to the source it came from.
The percentage is not scripted: this page imports claimConfidence — the canvas’s own scoring — and runs it on the quotes as they land. Watch the last one. It is a founder who already built this and says retention killed it, and the score goes down instead of up.
Validating an idea rarely fails because the founder was lazy. It fails because the work is spread across tools that forget, and nothing can be traced back to where it came from.
Keep a conversation on the board as an object you can point at, link and reference later. You stop scrolling a chat history to find what you already found.
Select the objects that matter and that selection is the context. Precise, visible, and yours to change — not a window that quietly fills with the last two hours.
Check the demand first. A research pass costs cents of model time, and the run shows the figure per model and per source — the example receipt further down came to $0.02.
One table, at least ten rivals, filled row by row with what they charge, who they sell to and where they are weak — every row carrying the page it was read from.
Open calls you are eligible for in your own country, with deadlines, from the official databases. Public money, named and dated, not a list of programme names.
Confidence is computed from the evidence links, not asserted. A claim with nothing behind it reads zero and says so, which is the number you actually needed.
One canvas holds all of it — notes, tables, todo lists, files, charts and the agent's output — arranged spatially instead of appended to the bottom of a document.
Seventeen structured artifact kinds with named fields. A pain map from March and one from today line up, so a second idea does not start from an empty page.
With normal AI each request lands a full blown report, making it impossible to focus on the one thing you need to know. A table is a single, countable answer, and it is easy to read.
| Competitor | Price | Segment | Weak spot | Fits our segment? |
|---|---|---|---|---|
| Ledgerly | €49/mo | SMB | — | — |
| Balanced | €180/mo | Mid-market | — | — |
The two violet columns are AI columns: you write the question once and it answers it per row, cell by cell, with the same states the board shows — pending, thinking, answered. Names here are invented, because assigning a real company a weakness on a marketing page is an assessment we have not made.
Not a blank document and not a chat log. Insights, the evidence behind them, the context you chose to give the agent, and a visible gap where the evidence is missing.
The model names a source it has already read and the runtime lifts the words out of it, together with the url and the author. It never writes the quote, so it cannot paraphrase a source into something more convenient.
Click the objects the agent should read. That selection is the context — visible, exact, and yours to change between requests instead of a window that fills up with whatever happened last.
A conversation worth keeping becomes an object on the board — linkable, movable, and readable next to the artifact it produced. You never scroll a chat history to find what you already found.
Ask for a checklist and you get a todo list. Ask for a comparison and you get a table you can sort, edit and turn into a chart. Structure instead of paragraphs you have to re-read.
Agents work on the board while you do, as a peer rather than a chat window — they add, edit and connect objects live, and your undo never touches their edits.
Sixteen concrete steps across five stages, each with one objective, one command that starts it, and a finish condition you can check. Idea to product stops being a blank canvas and becomes a route with your position marked on it — you always know the next move and how far along you are.
A gate asks for 3 independent sources — and never blocks you.
Nothing here runs on its own. Each stage provides guidance with clear steps. You make the progress and the board keeps the record — so you can reorganise and re-evaluate results without losing the evidence.
Communities, app-store reviews, marketplaces, LinkedIn and official statistics — in your country's language, because an English query for a national source finds listicles and not the source. Out come a pain map, a competitor table and a persona, each with its evidence attached.
The reality check names the assumptions the idea rests on and looks for evidence for AND against each one — including the people who tried this and stopped. Then a ladder of cheap experiments, each naming where its participants come from, so a hypothesis has a way to lose.
Positioning against the alternative you actually beat, pricing anchored to competitors you read rather than recalled, a channel plan ranked by where the segment measurably gathers, and the public funding calls you are eligible to apply to with their deadlines.
Brand and campaign material that inherits the positioning already on the board, posts with generated media, and publishing to the accounts you connect — so the copy argues the case the research supports.
Published-post metrics come back onto the board, so an experiment's verdict is recorded next to the claim it was testing, and progress is a set of resolved claims rather than a feeling — including when the verdict is no.
Every stage ends in key findings, not a wall of prose you have to summarise yourself.
Nineteen of them, 11 free. Each one is a switch in your settings — a source that is off is never called, and the paid ones say what they cost before they are.
Each of these works for something. None of them leaves you able to point at why you believe a thing.
| Feature | RawKit | AI chat | A doc or spreadsheet | A research study |
|---|---|---|---|---|
| Quotes come from a source it actually read | Yes | No | if you paste them | Yes |
| A claim's confidence is computed, not asserted | Yes | No | No | in the write-up |
| Says plainly when something is unevidenced | Yes | No | if you notice | Yes |
| Searches your country in its own language | Yes | sometimes | No | Yes |
| The output is comparable across projects | Yes | No | if you keep a template | No |
| You see the cost before it is spent | Yes | No | Yes | No |
| Result arrives in minutes, not weeks | Yes | Yes | Yes | No |
| You can edit it afterwards | Yes | the chat, not the output | Yes | No |
RawKit is in alpha and has no published plans yet — we currently use pay as you go model with free credits for early signups. The following is a breakdown of what it costs to run a research playbook with RawKit.
Reddit, Hacker News, GitHub, arXiv, Eurostat, OpenAlex, the EU Funding Portal, grants.gov, CORDIS, Y Combinator's directory and Etsy cost nothing to read. A research run that stays on those spends model tokens and nothing else.
Every paid source carries a paid badge and its own switch, and the expensive ones say why. X bills per resource returned — $0.005 a post, $0.010 an author — so a 25-post search runs about $0.13, more than ten times any other source here.
Tokens and dollars, broken down per model and per data source, on the run itself and in the month-so-far view. A run that hits its budget stops and says so rather than quietly truncating the work. You also choose the model per task, so depth and price are your call.
No install, no key to obtain, no data to prepare.
One card: the idea, who it is for, the market you mean. Email and a verification link is the whole signup — no card, no sales call.
Pick a depth and watch it work on the board. It reads the sources you enabled, in your country's language, and shows the tokens and dollars as it goes.
What came back unevidenced is the list of what to check next. That list is the point — a research tool that only returns good news has told you nothing.
Sticky notes, text, four shapes, frames, tables, todo lists and graphs — with rich text, snapping and tidy-up. Every one of them works with no agent in the room.
Draw a connector from a quote to a claim and label it. Those labels are what the confidence number is computed from, so the score is never a model's opinion.
Upload a PDF, an image or a spreadsheet and it lives next to the artifacts it belongs to. Deleting the project deletes the files with it.
Any table on the board can be turned into a graph in place — useful the moment a competitor table has a price column.
The playbook shows which gates are unmet and what would clear them. It suggests; it never locks a stage or refuses an action.
Pick the model for each job — OpenAI, Gemini, DeepSeek and MiniMax are wired, with a separate pick for image generation.
One request mines what people already said about your idea and puts the quotes on a board. If the idea is not worth it, you will know this afternoon — for cents instead of another weekend.
Each category reads the sources that answer for it — app stores for an app, marketplaces for a product, communities for SaaS.