Opportunities › Web content extraction and cleaning at scale › Proposed app
Bareleaf
Low-cost web page cleaning that strips boilerplate before your LLM sees it
- Score
- 54
- confidence 56
- Evidence
- 10
- items on the page
- Rivals
- 1
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2015
- Established market · AI estimate
ScreensConcept
Desktop app: Teams configure bulk extraction jobs, review quality checks side-by-side, inspect removed content, and manage spend—dense configuration and analytical work at a computer. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New job
Estimate only$790
About this appAI proposal
Bareleaf turns raw HTML into clean main content. It removes ads, navigation, footers and sidebars, and returns text or Markdown ready for training sets, search indexes and LLM context windows.
It is built for AI teams and data infrastructure groups that process pages in the millions or billions, where the per-page cost of extraction limits how much they can clean. The proposal is a compact encoder-based model that runs on cheaper GPUs.
Every job shows its cost per thousand pages up front and a sample of what was removed, so you can check quality before you commit a whole crawl.
- Tagline
- Clean pages, at a price you can scale.
- Voice
- plainprecisefrugal
- Why this name
- A bare leaf is the part left once the husk is gone: the main content without the clutter. Two plain words, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Bulk extraction jobs
Point Bareleaf at a bucket of HTML or WARC files and get clean output back in the same layout, with the cost shown before you start.
No member yet
Single-page API
Send HTML to one endpoint and get main content as text or Markdown, for live search and retrieval pipelines.
No member yet
Removed-content review
See what was cut from a random sample of pages, side by side, so boilerplate leaks and over-trimming are easy to spot.
No member yet
Cost estimator and spend cap
Enter a page count, see the price, and set a hard spend cap per job so a large crawl never surprises you.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#F6F3EAPaper · surface#1F2A24Ink · text#1F5C4ADeep Fern · primary#E08A2EAmber · accent#3C7A9ESlate Blue · data
Typography
Aa
Bareleaf
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Clean pages, at a price you can scale.
PricingAI proposal
The material gives only a public claim: Dripper costs $159,000 per 1B pages versus $7,900 for another tool. It is not from a pricing page. Scale is set near the claimed lower rate as a starting point to test.
Trial
Free
- 10,000 pages free
- Single-page API
- Removed-content review
Builder
$29 a month
- 2M pages included
- Bulk jobs from S3 or uploads
- Quality check set
- Email support
Scale
$790 a month
- 100M pages included
- Spend caps and job alerts
- Leak detector
- Priority support
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
$194,000
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
- No loop in the product. Bareleaf runs behind the scenes in data pipelines. Its output is cleaned text that goes into models and indexes, so end users do not see its name. Growth comes from developer content, benchmarks and direct sales.
- Do they become customers?
- Pipeline engineers who try the API are the buyers, so reach and customers overlap. Test whether a free 10,000-page trial and a public quality benchmark convert to paid bulk jobs.
- First customers
- Small AI teams and search start-ups with 3 to 30 engineers that crawl 10M to 1B pages and feel extraction cost as a budget line.
- Where to reach them
- Hacker News and dev forums: Post a public benchmark and cost comparison on an open set of pages, with the method and removed-content samples shown.
- Open-source data communities: Offer free credits to teams building open web datasets in return for published quality results.
- Direct outreach: Email data leads at AI and search companies with a free cost estimate based on their own crawl size.
- First milestone
- Three teams run a paid 10M-page job after a side-by-side quality check against their current extractor.
Why people already pay for this
- VerifiedHacker News post received 106 points and 26 comments
- Public claimCurrent extraction tools are too expensive to scale: cleaning 1 billion pages costs $159,000 with Dripper, making large-scale data cleaning a material cost constraint.
- Public claimBoilerplate in web content contaminates AI model outputs: ads and navigation elements can leak into LLM context windows and final answers to users.
- Public claimTeams building search and LLM systems depend on web content extraction as a core workflow, making it a solved problem that exists as a service.
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 · GPU inferenceRented low-cost GPU capacity with batch queueThe cost advantage depends on running a compact encoder model on cheaper GPUs in large batches.
- This app · Model trainingSmall encoder model fine-tuned on labelled HTMLExtraction is framed as labelling page blocks as content or boilerplate, which needs a trained model.
RisksAI inference
- AIThe extraction quality claim is unproven for this product. A smaller model may miss content on unusual layouts, and buyers will switch only if quality matches their current tool.
- CommercialBuyers at billion-page scale are few, and may build extraction in-house or negotiate volume discounts with incumbents, leaving little room for a new vendor.
- TechnicalThe cost advantage rests on an AI inference of the hardware difference between encoder and decoder models. Real margins at low prices are unverified and need to be measured.
- DataTraining data for labelled HTML must be gathered or licensed, and customers may be reluctant to send crawled pages they do not have rights to share.
- CommercialLow per-page prices mean revenue depends on volume. A small number of customers could account for most of the income.
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.