Instead of asking an AI what it thinks, RawKit sends research agents to read what your market already said: Reddit threads, app-store reviews, competitor pricing pages, official statistics. Every quote lands on your board copied from a page you can open, never written by the model.
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 fourth one — a founder who shipped exactly this and says retention killed it. It refutes the idea, so the score falls from solid back to emerging instead of climbing. A scripted demo would have finished on a big green number, which is the exact thing this product exists not to do.
It is not for lack of effort. The work gets spread across a chat window that forgets, a doc with no memory and thirty open tabs — and by Friday you still cannot say why you believe any of it.
Keep a conversation on the board as a card you can point at, link and reopen. You stop scrolling a chat history to find what you already found.
Click the cards that matter and that selection is the context. Precise, visible before you send it, and different for the next request.
Check 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 $0.2.
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 carries the page it was read from.
Open calls you are eligible for in your own country, with their deadlines, straight from the official databases. Public money, named and dated, not a list of programme names.
Confidence is calculated from the evidence links, never claimed in prose. A claim with nothing behind it reads zero and says so, which is the number you actually needed.
One board holds all of it — notes, tables, checklists, files, charts and the agent's output — laid out spatially instead of appended to the bottom of a document.
Seventeen structured card types with named fields. A pain map from March and one from today line up, so a second idea does not start on an empty page.
This is the request that replaces the week described above. Name the idea, ask what people already complain about, and the research agent goes to the communities, review pages and forums where that market talks. Every problem it puts on the board arrives with the quotes it was read from, so you can check the reasoning instead of trusting it.
The cards appear as the run writes them, in the order it writes them. What makes this validation rather than summarising is the last step: a problem with one source behind it says so and stays on the board as the thing to check next — which is the finding you actually needed.
Recorded in the product. The boards, tables and quotes in these clips came out of real runs, and each one is written out in text underneath if you would rather read than watch.
One idea is a guess. Five niche-specific versions of it, each researched on its own, is a comparison. The agent takes the idea already on your board, writes a version of it per audience, and then goes and finds out which of them has demand underneath — so the one you build is the one the evidence picked, not the one you happened to think of first.
Exploring one niche used to cost an evening of searching, which is why most founders explore one. Here a variant that loses costs you minutes, and the reason it lost stays on the board — so the niche you rejected in March is still argued when someone asks you about it in June.
Ask a normal AI to map your competition and a report comes back, with the one thing you needed buried in paragraph four. Ask RawKit and you get a table — one countable answer per row — and a short note beside it saying what the table means for where you can stand.
The table is a real object on the board rather than a picture of one: sort it, edit a cell, add a column and have the agent fill it, or turn the price column into a chart. The note is where a table becomes a decision — which segment is underserved, and what you would have to charge to enter it.
Not a blank document and not a chat log. Findings, the evidence underneath them, the exact context you handed the agent, and a visible hole where the evidence is missing.
The model names a page it has already read. RawKit then copies the words out of that page, with the url and the author. The model never types the quote, so it cannot polish a source into something more convenient.
Click the cards the agent should read. That selection is the context — visible before you send it, and yours to change next time, instead of a window that silently fills with the last two hours.
A conversation worth keeping becomes a card on the board — linkable, movable, readable next to whatever it produced. You never scroll a chat history to find what you already found.
Ask for a checklist and you get a checklist. 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 read twice.
Agents edit the board while you do, adding, updating and connecting cards live. Your undo never touches their work, and theirs never touches yours.
Sixteen concrete steps across five stages. Each one has a single objective, the command that starts it, and a finish condition you can check. Idea to product stops being a blank canvas: you always know the next move and how far along you are.
A gate asks for 3 independent sources — and never blocks you.
Nothing runs on its own. Each stage tells you what to do next and what would finish it. You make the progress; the board keeps the record, so you can reorganise and re-judge results without losing the evidence.
Communities, app-store reviews, marketplaces, LinkedIn and official statistics, searched in your country's language — because an English query for a national source returns listicles instead of the source. Out come a pain map, a competitor table and a persona, each with its evidence attached.
The reality check names the assumptions your idea rests on, then hunts evidence for and against each one — including the founders who tried this and stopped. Next comes a ladder of cheap experiments, each naming where its participants come from, so a hypothesis has a real way to lose.
Positioning against the alternative you actually beat. Pricing anchored to competitors the agent read rather than recalled. Channels ranked by where your 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 images, and publishing to the accounts you connect — so the copy argues the case your research actually supports.
Metrics from published posts come back onto the board, so an experiment's verdict is recorded next to the claim it was testing. Progress becomes a set of settled claims instead of a feeling, including when the verdict is no.
Research tells you what your market already said. A post tells you what they do when your idea is in front of them. RawKit writes the post from the idea on your board, publishes it to accounts you connect, and brings the numbers back to the claim it was testing.
The cheapest experiment there is: say the thing out loud and see who reacts. The agent writes the post from the idea card and the evidence linked to it rather than from a blank prompt, so the copy argues the case your research actually supports — image included.
A post written from a blank prompt sells a product nobody has validated. This one inherits the positioning already on the board, which is the whole difference between running an experiment and doing marketing.
Connect an account once with OAuth and the post goes out from the board. Six platforms are wired, and the scheduler suggests the slot your audience is most likely to see it in — then publishes it at that time on its own, once you have approved the post.
This is the step that closes the loop: the verdict on an experiment is recorded next to the hypothesis it tested, including when the verdict is no. Disconnect a platform at any time and the stored credentials are deleted with it.
Every stage ends in something you can act on and hand to someone else.
Not a list of programme names you then have to research yourself. Open calls, filtered against where your company is registered, how old it is and who is on the team — each with its deadline and the portal it was read from.
matched against EU Portals · grants.gov · CORDIS
The programmes are real; the dates are from an example run, because calls reopen on their own cycles and RawKit reads the current deadline off the portal.
11 of the nineteen are free. Each one is a switch in your settings: a source that is off is never called, and the paid ones show what they cost before they run.
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 paid market study |
|---|---|---|---|---|
| Quotes come from a page it actually read | Yes | No | if you paste them | Yes |
| Confidence is calculated, 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 |
The subscription is not a fee for access. Every dollar of it comes back as usage balance each month, at a better rate than Free — what you buy is the rate, the agents and the connectors.
For finding out whether the idea holds up. Top up from $5 and pay only for what runs.
Start free$9.99 of usage balance included every month. Spend it and you have paid exactly the subscription.
Choose Creative$24.99 of usage balance included every month. Spend it and you have paid exactly the subscription.
Choose ProPrices in USD, before tax; tax is added at checkout. Every account starts on Free. Upgrade, change or cancel from your workspace’s billing settings — a plan that lapses drops to Free and keeps the balance it already has.
| What you get | Free$0 | Creative$9.99/mo | Pro$24.99/mo |
|---|---|---|---|
| Usage and balance | |||
| Price | $0 · pay as you go | $9.99 per month | $24.99 per month |
| Usage balance included | Not included | $9.99 every month | $24.99 every month |
| Usage rate over the provider’s price | +30 % | +20 % | +10 % |
| Top-ups | $5–$500, card saved for optional auto-recharge | same | same |
| Per-run spend cap | $1 | $1 | $1 |
| Agents and models | |||
| Agents | Workspace, Research, Validate (incl. reality check), Strategy, Analysis, Legislative | + Content, Marketing | + Funding, discovery loop |
| Model tiers | fast, primary | fast, primary | fast, primary, premium |
| Concurrent runs | 1 | 3 | 10 |
| Image generation | Not included | Included | Included |
| Video generation | Not included | Not included | Included |
| Research sources | |||
| Community and web | Reddit, Hacker News, GitHub, arXiv, web search, app stores and marketplaces, Etsy, official statistics, channels and communities, Product Hunt | same | same |
| Grant registries | grants.gov, CORDIS | same | same |
| Social listening | Not included | X, Threads, Instagram, LinkedIn posts | same |
| Investors | Not included | Not included | LinkedIn people and companies, startups database, model-native web search |
| Publishing | |||
| Connected social accounts | Not included | Up to 3: X, Instagram, Threads, TikTok, LinkedIn | Unlimited, plus Facebook and YouTube |
| Scheduler and publishing queue | Not included | Included | Included |
| Workspace | |||
| Storage | 1 GB | 10 GB | 100 GB |
| Boards, sharing, multiplayer | Included | Included | Included |
Every plan is metered the same way: each model call, paid search and media generation is priced at the provider’s real price plus your plan’s rate, and drawn from a prepaid balance you can see. Here is what that means for a full research playbook.
Free pays the provider’s price +30 %, Creative +20 %, Pro +10 %. There is no RawKit price list to decode: the rate on every model and source is shown in your settings, and the same number is what the ledger charges.
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, its own switch, and its price. X bills per result returned — $0.005 a post, $0.010 an author — so a 25-post search runs about $0.13 before your plan’s rate, 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. No single run can spend more than $1 on any plan — a run that hits its cap stops and says so rather than quietly truncating the work.
Nothing to install, no API 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.
Whatever came back unevidenced is your 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, checklists 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 calculated from, so the score is never a model's opinion.
Upload a PDF, an image or a spreadsheet and it lives next to the cards 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 your board. If it is not worth building, 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.