Service
Document and data automation
Automations for the dull but critical work: extraction, classification, validation, renaming, reconciliation, summarising, review routes and deliverables in CSV, Excel or databases.
Expected outcomes
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.
Related experience
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.
Related work
Anonymised examples
Survey pipeline and qualitative analysis
Transformation of survey exports into reviewable analytical outputs with Python, pandas, Excel and human judgement.
Document automation with Python and human review
Workflows to extract text, classify documents and generate structured indexes from administrative PDFs.
Go deeper
Related articles
Organisational memory: keeping knowledge in the company when the AI tool changes
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.
Knowledge and context for AIFrom PDF to reviewed data: fingerprint, evidence and review queue
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.
Document automationFrom cost savings to irrelevance: the danger of automating without redesigning your processes
Automating inefficient tasks moves the disorder onto a faster platform. Real value comes from redesigning the process architecture.
Technology adoption and processesDoes this fit your situation?
Briefly describe the operational, data or AI problem. The first reply can help to guide the type of diagnostic.