Opportunities › Local AI data analysis with reproducible notebooks › Proposed app
Plainrun
Ask questions of your data and get a notebook you can rerun, with files kept local
- Score
- 38
- confidence 46
- Evidence
- 4
- items on the page
- Rivals
- 2
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2014
- Established market · AI estimate
ScreensConcept
Desktop app: Data analysts work on exploratory analysis for long stretches, edit code cells, compare results, and export notebooks—intensive desk work with many fields and keyboard interaction. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New question
Data stays on device- Fileclaims_2025.csv
About this appAI proposal
Plainrun lets analysts ask questions about a data file in plain language. It writes Python, runs it inside your browser, and records every question, code cell and result as a notebook you can open, edit and rerun.
Your files are read on your device and are not uploaded. The AI model can be one you run yourself, such as a local Ollama server, so sensitive tables stay in your hands. Cloud models are optional and off by default.
It sits between a blank Jupyter notebook and a chat box that hides its code. You get the speed of asking and the record of a notebook: every step visible, changeable and exportable as an .ipynb file.
- Tagline
- Ask in plain words. Keep every step.
- Voice
- PlainCarefulTransparent
- Why this name
- Plain words in, a plain record out: every question becomes code you can run again.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Every answer becomes a notebook cell
Questions, generated code, outputs and charts are saved in order as a notebook, so nothing in the analysis is hidden.
No member yet
Python runs on your device
Code executes in a browser sandbox on your machine. Data files are opened locally and never sent to our servers.
No member yet
Bring your own local model
Connect an Ollama or other local endpoint. Cloud models are an opt-in setting, shown clearly when on.
No member yet
Edit any cell and rerun
Change the generated code by hand, rerun from any cell, and see which later results are now stale.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F5F5BDeep Teal · primary#F6F3ECPaper · surface#1F2A28Ink · text#D9822BAmber · accent#3E6FA8Slate Blue · data
Typography
Aa
Plainrun
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Ask in plain words. Keep every step.
PricingAI proposal
The $199 one-time price and 7-day trial are a public claim about a similar product, not verified. The material gives no other prices; the Team tier is a starting point to test.
Trial
Free
- 7 days, full features
- Local model support
- Export .ipynb
Personal
$199 once
- One analyst, lifetime licence
- Local and optional cloud models
- Unlimited notebooks
- Offline use
Team
$15 a month
- Per seat
- Shared notebook library
- Shared model settings
- Priority support
Would you pay Team · $15 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$190,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Analysts export notebooks and send them to colleagues, reviewers or clients, who open them in Jupyter or in a Plainrun viewer link that shows the steps and the product name.
- Do they become customers?
- Recipients are often analysts who could become buyers, but many only read the result. Test whether viewers of shared notebooks start a trial.
- First customers
- Independent data analysts and small-team data scientists in healthcare, finance or research who handle sensitive files and already use Jupyter.
- Where to reach them
- Hacker News and Show HN: Post a demo notebook made from a public dataset; the material shows a similar post drew 73 points.
- Data science communities: Share example notebooks in analyst forums and subreddits, focused on private, reproducible work.
- Local LLM communities: Offer setup guides for Ollama users who want a data analysis front end.
- First milestone
- Fifty trials started and ten paid licences from analysts in one sensitive-data field.
Why people already pay for this
- VerifiedHacker News post received 73 points and 18 comments
- AI inferenceThe creator built this product because existing solutions fall short: Jupyter is flexible but requires manual work, while AI tools generate code without preserving workflows or reproducibility and run in the cloud.
- Public claimThe product is priced at $199 as a one-time purchase with a 7-day trial available.
- Public claimThe product converts conversational data analysis into reproducible notebooks, preserving the workflow for inspection, modification, and rerunning.
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · In-browser PythonWebAssembly Python runtime with data librariesRuns generated code on the user's device so data files are never uploaded.
- This app · Local model linkConnector to a user-run local LLM endpointLets prompts reach a model on the user's machine or network, not our servers.
RisksAI inference
- TechnicalIn-browser Python has memory limits and fewer packages than desktop Python, so large files or some libraries may fail. Needs testing on typical analyst datasets.
- PlatformBrowsers may block calls from a hosted page to a local model server unless it is configured to allow them. Setup could put off non-technical users.
- AISmall local models write wrong or unsafe code more often than cloud models. Users must check results; weak answers could hurt trust.
- CommercialThe only price in the material is an unverified $199 claim. Analysts may stay with free Jupyter, so willingness to pay must be tested.
- DataNotebooks saved with outputs can still contain sensitive values. The share-without-rows option needs care to avoid leaks.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.