CV to Excel, CSV or JSON

Turn a stack of CVs into one comparable table – candidate, contact details, qualifications, employment history and skills, in the order you want to read them.

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Try it on your own CV

One document, free, right now – no account and no card. You get the fields, the checks and the export, exactly as they come out of the pipeline below.

No document to hand? See it run on a sample – this does not use your free document.

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How it works

The whole pipeline, in the order it runs. The document type decides which fields come out and which checks run; the path is the same either way.

The pipeline: read it, extract it, check it, review it, export it – with a failed check routed to a person for review. 1 Read it 2 Extract it 3 Check it 4 Review it 5 Export it anything unresolved goes to a person, not a guess
1

Read it

The CV is OCR’d and its structure located – header, experience, education, skills – rather than scraped as loose text.

2

Extract it

Each role becomes its own record with employer, title and dates, so a career reads as a sequence instead of a paragraph.

3

Check it

Dates are normalised, overlaps and gaps surfaced, and total experience derived from the history itself.

4

Review it

Anything read with low confidence is surfaced beside the original page.

5

Export it

One row per candidate for screening, or JSON into an applicant-tracking system.

What it extracts, and what it checks

Extraction is the easy half. The checks are what turn a converted document into one you can post without reading it twice.

Extracted

  • Candidate name and contact details
  • Current and previous employers, with dates
  • Job titles and responsibilities
  • Qualifications and institutions
  • Skills, languages and certifications
  • Total years of experience

Checked

  • Employment dates are checked for overlaps and for gaps worth asking about.
  • Dates are normalised to one format, so a CV that mixes them still sorts.
  • Years of experience are derived from the history rather than taken from a claim on the cover page.
  • A field the extractor could not read is flagged rather than filled with a guess.
  • Every extracted field keeps a pointer to the page it came from.

What goes in, and what comes out

The document on one side, the fields pulled off it on the other – each one checked before it is handed over.

A CV on the left and the fields extracted from it on the right, each one ticked as checked. CV (PDF) Structured, and checked read Candidate name and contact details Current and previous employers, with dates Job titles and responsibilities Qualifications and institutions Skills, languages and certifications

What a flagged document looks like

The original page on one side, the fields read off it on the other. Correct a reading and carry on – the point is that nobody re-types a document to fix one number.

An Automize approval: the supplier invoice PDF beside the fields extracted from it, with approve and reject controls.
A supplier invoice on the same review screen – the document beside what was read off it, to approve, correct, or send back.

And then it does not have to stop there

A spreadsheet is the end of the job for most converters. Here it is a step: the same structured output can go straight into a process that posts it to your ledger, matches it against a purchase order, files the original, and asks a person only about the exceptions.

See what a Digital Worker does with it

Related tools

Invoice extractor

Drop in a supplier invoice and get the header and every line item back as structured data – with the VAT and the totals checked against each other.

Statement extractor

Drop in a PDF or a photograph of a bank statement and get every transaction back as a spreadsheet – dates, descriptions, debits, credits and the running balance.

Questions

Mostly. Two-column and heavily designed CVs are read by understanding the layout rather than reading top to bottom, which is what stops a sidebar of skills being spliced into the employment history. A CV built entirely as an image with decorative fonts is the case most likely to need review.
Yes. The extraction is schema-driven, so you can ask for the fields your screening actually uses – notice period, current salary, drivers licence, professional registration – rather than a fixed set.
No, and deliberately not. It extracts what the CV says into a comparable shape. Deciding who to interview stays with a person – an automated decision made about someone on the basis of their personal information is exactly the thing POPIA section 71 restricts.
They live in your Automize company under your own retention settings, like any other document. Candidate CVs are personal information, so the retention period you set for them is a decision worth making deliberately rather than leaving at the default.

Try it on your own document

Create a free Automize company and upload a document. Extraction, the checks and the exports are all in the platform – and when the same document arrives every month, the process that reads it can post it too.

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