Labelbox VS Datanem
Compare Labelbox VS Datanem and see what are their differences
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A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.
Your documents already contain a database. Datanem gets it out.
Any document with a repeating shape, whether that's dispatch notes, invoices, purchase orders, or lab results, contains tabular data that someone is still retyping by hand. Datanem reads your documents and turns them into structured tables, one row per document, with the columns you would have chosen yourself.
How it works:
Upload: Drop in up to 250 files at once (10MB each). Supports PDF, Word, plain text, or even a photo of a printed page, we can scan anything. They don't have to be digital originals.
Agree the columns: Datanem reads a sample, proposes a table, and waits for your approval. Rename a column, drop one, or add one it missed. Save the design and the next batch reuses it. Or skip the review and let it decide.
Export: Get one row per document, downloadable as Excel, CSV, or ready-to-use CREATE TABLE and INSERT statements for SQLite, Postgres, or DuckDB. Anything the model was unsure of is flagged for you to check.
What makes Datanem different:
No fixed templates! There is no rigid template to bend your paperwork into. Datanem reads what you actually have and proposes a table to match, then keeps using it so every batch lands in the same shape.
Transparency about what it leaves out! Once a table is agreed, anything outside it would normally be dropped silently; a failure you'd never find out about. Datanem lists the values it saw but couldn't file, says how many documents carried each one, and offers to add the column.
Built for GDPR from day one! Uploads are held in an EU-only bucket and never leave it. Uploads and extracted data are deleted after 30 days on the free plan. Your documents and the data drawn from them are never used to train models.
Common use cases:
Dispatch notes, delivery notes, invoices, purchase orders, remittance advice, packing lists, bills of lading, inspection reports, lab results, application forms, and timesheets.
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Datanem's answer:
Operations teams manually entering data from invoices, delivery notes, and purchase orders
Finance and accounting professionals processing remittance advice and payment records
Logistics and supply chain teams handling packing lists and dispatch notes
Quality control and lab staff working with inspection reports and test results
Anyone who receives batches of structured documents and needs them in a database or spreadsheet, fast.
Datanem's answer:
No fixed templates. Datanem adapts to your actual documents. Flags uncertain values so you check a few cells, not all. GDPR-compliant with EU storage and auto-deletion. No credit card, no demo call to start.
Datanem's answer:
Datanem is a UK-based startup founded by Alexander Green. The founding insight is simple but sharp: every organisation sits on folders full of documents with repeating structures: invoices, dispatch notes, purchase orders, lab results; someone, somewhere, is still retyping that data by hand.
The product was built around a core observation about how document processing actually fails in the real world. Most tools assume your documents fit a template. But in practice, paperwork varies. Suppliers format invoices differently, forms change over time, and scans come out crooked. Datanem's approach flips this: instead of forcing your documents into a rigid template, it reads what you actually have and proposes a table to match.
The other insight? Silent failure is the enemy. Traditional extraction tools drop anything that doesn't fit the template and never tell you. Datanem was built to surface those omissions explicitly, listing values it saw but couldn't file, showing how many documents carried each one, and offering to add the column. This transparency, letting you check a handful of flagged cells instead of all of them, is a deliberate design choice born from watching operations teams waste hours verifying data they assumed was complete.
From day one, the product was also built with GDPR in mind: EU-only storage, automatic 30-day deletion, and a strict policy of never using customer documents or extracted data for model training
Service goes down often. Very slow team. Slow support.
Based on our record, Labelbox seems to be more popular. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Cursor's security agents primarily operate in the first dimension, catching vulnerabilities in code. That's valuable and necessary work. But as you'll see in the walkthrough below, the other two dimensions matter just as much, especially at enterprise scale. And the organizations getting the best results, like Labelbox, which cleared a multi-year vulnerability backlog by running Cursor and Snyk together, are the... - Source: dev.to / 6 months ago
Use tools like Weights & Biases, Labelbox, or Maxim’s data engine to version your datasets, track changes, and continuously add new edge cases and user feedback. - Source: dev.to / about 1 year ago
Labelbox | Remote | Frontend / WebGL, Backend, Engineering Managers | https://labelbox.com Labelbox is building the training data platform to power breakthroughs in machine learning. We provide an end to end solutions for the full AI lifecycle from creating catalogs of unstructured data all the way to building the tools for humans to label the data to teach machines. Why choose us? - Source: Hacker News / almost 4 years ago
Hey, I have currently developed a U-Net model for segmentation and I am trying to use the model assisted labeling feature on LabelBox to annotate some masks, so I can save time on relabeling. I am just wondering if anyone is familiar with this feature or can give me a step by step guideline on how to go about doing this. I went through the examples on their GitHub but I’m honestly still very confused. Any help... Source: about 4 years ago
By now, I hope you see where I'm going with this. What is MDR doing? They're creating the labelled data used to train severance chips. They get a raw download of human brains in encoded format, and go about manually labelling the different pieces based on their most basic elements. Then, based on this manually labelled data, an algorithm can be trained to create a severance chip. MDR is basically Labelbox for... Source: over 4 years ago
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