Datanem
Upload dispatch notes, invoices, delivery notes or forms. Datanem works out the columns, extracts every row, and gives you Excel, CSV or SQL.
A startup from the United Kingdom that is founded by Alexander Green.
Upload dispatch notes, invoices, delivery notes or forms. Datanem works out the columns, extracts every row, and gives you Excel, CSV or SQL.
A startup from the United Kingdom that is founded by Alexander Green.
This page is designed to help you find out whether Datanem is good and if it is the right choice for you.
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.
Listed in
Multi-format document support
Upload PDFs, Word documents, plain text files, or photos of printed pages. Scans are processed with OCR so they don't need to be digital originals.
Batch Processing
Process up to 250 files at once (10MB each) in a single upload, with saved column designs reused across batches.
Smart column detection
Reads a sample of your documents and automatically proposes a table structure, which you can review, rename, or customise before export.
Confidence flagging
Any value the AI is uncertain about is flagged in the table, so you check a handful of flagged cells instead of verifying every single entry.
Transparent omission reporting
Lists values it saw but couldn't file, shows how many documents carried each one, and offers to add the column. No silent failures.
Multiple export formats
Download your structured data as Excel, CSV, or ready-to-use SQL CREATE TABLE and INSERT statements for SQLite, Postgres, or DuckDB.
GDPR-compliant by design
EU-only storage, automatic 30-day deletion of uploads and extracted data, and a strict policy of never using your documents for model training.
No friction to start
No credit card required, no demo call needed. Just upload and go.
Saved column templates
Once you approve a table design, it's saved and automatically applied to future batches, so every batch lands in the same shape.
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.
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 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
We have collected here some useful links to help you find out if Datanem is good.
Check the traffic stats of Datanem on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Datanem on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Datanem's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Datanem on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
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