# Document and data automation

> Processes PDFs, spreadsheets, surveys and repetitive exports with scripts, OCR, rules and human review.

- URL: https://www.enter20.com/en/services/document-data-automation/
- Español: https://www.enter20.com/servicios/automatizacion-documentos-datos/
- enter 2.0 — Practical AI, governed data, dashboards, automation and information systems for organisations that need to make better decisions.

Automations for the dull but critical work: extraction, classification, validation, renaming, reconciliation, summarising, review routes and deliverables in CSV, Excel or databases.

## What can remain after the work

- Python pipelines for PDFs, OCR, CSV, Excel and administrative documents.
- Rule-based classification, with AI support where privacy and risk allow.
- Traceability of exceptions, imputations and human decisions.
- Outputs ready for reporting, archiving or loading into systems.

## Technical and operational proof

- PDF/OCR extraction tools, invoices, receipts, payslips and bank documents.
- Survey analysis with pandas, parquet, Excel, codebooks and review queues.
- Use of YAML rules, post-processors and AI models as controlled support.

## Anonymised examples

- [Survey pipeline and qualitative analysis](https://www.enter20.com/en/work/survey-qualitative-analysis/): Transformation of survey exports into reviewable analytical outputs with Python, pandas, Excel and human judgement.
- [Document automation with Python and human review](https://www.enter20.com/en/work/python-document-automation/): Workflows to extract text, classify documents and generate structured indexes from administrative PDFs.

## Related articles

- [Organisational memory: keeping knowledge in the company when the AI tool changes](https://www.enter20.com/en/blog/organisational-memory-keep-knowledge-when-changing-ai-tools/) (2026-11-09): AI assistants need context, and that context is usually built inside a vendor's tool. Curating it as the organisation's own knowledge cards, with sources, an owner and a review date, makes it reviewable and portable.
- [From PDF to reviewed data: fingerprint, evidence and review queue](https://www.enter20.com/en/blog/from-pdf-to-reviewed-data-fingerprint-evidence-review-queue/) (2026-09-18): Extracting data from invoices, payslips or calls for proposals is easy to promise and hard to make reliable. Reliability comes from the document's fingerprint, the evidence kept alongside each data point and a review process that does not get in the way.
- [From cost savings to irrelevance: the danger of automating without redesigning your processes](https://www.enter20.com/en/blog/danger-of-automating-without-redesigning-processes/) (2026-07-08): Automating inefficient tasks moves the disorder onto a faster platform. Real value comes from redesigning the process architecture.
- [How to use AI safely in organisations that handle sensitive documents](https://www.enter20.com/en/blog/using-ai-safely-with-sensitive-documents/) (2025-09-19): Using AI safely with documents depends on classification, data minimisation, human review, contracts, records and local alternatives when the risk demands them.

## Does this fit your situation?

Briefly describe the operational, data or AI problem. The first reply can help to guide the type of diagnostic.

Contact: [contacta@enter20.com](https://www.enter20.commailto:contacta@enter20.com) · [Contact form](https://www.enter20.com/en/contact/) · [Diagnostic questionnaire](https://evaluo.enter20.com)