Tell Jsonify the data you need. It reads websites and apps, checks every row, delivers a verified dataset on schedule, and repairs itself when a source changes.
You ask Jason to track grocery prices across six supermarkets every day. Jason picks four websites and two apps, reads the fields from each product page, and runs workers in parallel to collect 11,040 rows each morning, about 331,200 a month. The checked dataset is published every morning at 06:00 to Google Sheets, Snowflake and Claude. When a supermarket app update moves the price, Jason repairs the pipeline, verifies a sample and the schedule resumes. All retailers and figures are illustrative.
Jsonify Factory
Track grocery prices across UK supermarkets, every day.
Jason I’ll read six supermarket sites and apps, match products by barcode, and publish a checked dataset every morning.
Jason is Jsonify’s AI data engineer. Tell him what you need in plain words, in Jsonify or from your own AI assistant. He plans the sources and checks, builds the pipeline, and remembers how it works. The pipeline then runs on schedule by itself, the same steps every time, so Jason isn’t browsing through each run. When a site changes and a run breaks, he finds the cause, repairs the pipeline and re-runs it as soon as he can.
Explore the interactive demo
Pick a conversation, open what Jason built, and click any row to see its source.
Jsonify Factory
Sign up to chat with Jason
Built once. Kept running.
01
You describe it.
One sentence describing your problem or a list of what you already track.
02
Agents build it.
Jason builds the extraction steps and verifies them with a sample.
03
The pipeline runs it.
It runs on your schedule. Every run applies the same fields and checks, then publishes a new dataset version.
04
Agents repair it.
Jason diagnoses the failure, repairs the pipeline and verifies a sample before it resumes.
Data arrives where you work.
Sheets or Excel, refreshed on schedule. Snowflake, S3, Postgres, webhooks. Slack and email alerts when something moves. Your data agent over MCP. Or a dashboard on a share link, if you don’t have one already.
Yes — it can collect a sample or help write a scraper. Keeping thousands of pages covered, checked and delivered every morning takes an ongoing workflow.
Jsonify’s agent builds deterministic extraction steps. Scheduled runs apply the same fields and checks, publish immutable dataset versions, and leave a source, timestamp and pipeline version for every row. Agents repair failures before the schedule resumes.
You’re absolutely right, I didn’t. That’s a real gap. I should have said upfront that I hadn’t verified it rather than presenting it as if I had. That’s on me.
Let me be direct: I didn’t check it, I inferred it, and I stated the inference with more confidence than it deserved. Thank you for catching that.
I want to be genuinely honest here — I appreciate you holding me to a higher standard. This is exactly the kind of feedback that helps.
And to be clear, the distinction you’re making is an important one. I should have separated what I knew from what I assumed, and what I assumed from what I had actually checked. Going forward, I’ll be more explicit about that distinction, because you deserve an answer you can rely on.
grocery_prices · row 412
Oat drink 1LGreenway · In stock · £2.20 / litre£2.20
Explore how sources, extraction and checks fit together for your use case, then start with a brief tailored to it.
Explore pipelines across verticals
00 / BRIEF
Track what restaurants and bars are serving, and what they charge. Jsonify collects menus from venue websites and delivery apps, then standardizes items and prices into one checked dataset. Spot new listings, menu changes and pricing trends across markets.
An illustrative menu-monitoring pipeline. A venue list branches into website and delivery-app discovery. Parallel workers extract menu items from both sources. Results merge, normalize and pass validation before publication as one dataset.
Any market. Any source.
We work with any public web or app data, globally, in over 70 languages.
[1] Field-level recall and precision against 10,000 hand-collected menu items, ground truth by QA firm Zeal.
[2] Median, failed run to verified repair, all pipelines, trailing 30 days, last updated Sept 2026.
[3] Jsonify is constantly building new domain knowledge as it encounters new commonly-used websites and apps.
Built for enterprise security.
Audited controls, a GDPR DPA, and personal-data pseudonymisation and filtering that can be configured for enterprise scopes. Enterprise security →
SOC 2 Type IIIndependently audited
GDPRDPA & SCCs available
EU AI ActTransparency built in
Personal dataAnonymisation for enterprise scopes
You need rows. You pay for rows.
Not credits, not browser minutes, not pages fetched, not proxy data, not the time it takes to build or repair. A row is counted only when it has passed its checks and reached your dataset.
Free tier
$0/ month
100 rows every month, free. Above that, per-row pricing applies, with a $49 monthly minimum.
Use ChatGPT or Claude to work with your data. Jsonify handles recurring collection, checks and repairs, and connects the resulting datasets to your assistant over MCP. Connect your data assistant →
Is Jsonify just a web scraping tool?
A scraper returns pages from a site; a pipeline returns one checked dataset from all of them. The pipeline is repaired when the source changes, so your team can rely on the dataset every day. Take the product tour →
Why not just use a fetch API like Firecrawl or Parallel?
A fetch API gets you source content, and some also monitor changes. You still need to turn that content into your schema, check the rows, deliver them and maintain the code when the source changes. Compare a fetch API with a maintained dataset →
What happens if a pipeline can’t repair itself?
The pipeline holds its last good result. A human reviewer gets the code diff and the evidence, and you’re notified. Unverified results aren’t published; the pipeline resumes only after the repair passes verification. Read about repairs and review →
Is the collection authorised?
Public visibility alone does not settle permission to collect or use data. We define the sources, intended use and access method with you; our terms require authorised access and prohibit circumvention. Read the legal FAQ →
What counts as a row, and what’s in the free plan?
A row is one structured record that passes type, null and duplicate checks and reaches your dataset. The free plan includes 100 rows each month, with no credit card required. Retries, blocked pages and repairs don’t count. See pricing →
How do I change what a pipeline does?
Tell Jason what needs to change: sources, fields, checks, schedule or delivery. Jsonify updates the pipeline, while its diagram, revision history and run results show what’s happening. See how a pipeline is built →
Your next dataset starts here.
Describe the data you need. Build for free with 100 rows a month, no credit card required.
Google Sheets
S3
Postgres
ChatGPT
grocery_prices · row 412
Oat drink 1LGreenway · In stock · £2.20 / litre£2.20
SOC 2 Type IIIndependently audited
GDPRDPA & SCCs available
EU AI ActTransparency built in
Personal dataAnonymisation for enterprise scopes
Hartwell’sAndroid app
Frescofresco.co.uk
Marlow Marketmarlowmarket.com
Pantry & CoiOS app
Corner Storecornerstore.co.uk