Most organisations run on documents: invoices, contracts, forms, reports and records. Most of the information inside them is still extracted by people reading and retyping it. Document processing is one of the clearest wins for applied AI, provided it is built for the documents an organisation actually has, rather than the tidy examples used in demonstrations.
This is the step-by-step approach we follow.
Step 1: Inventory your documents
Start by listing the document types you handle, their volume and their formats: born-digital PDFs, scanned pages, photographs, spreadsheets and emails. The mix determines the approach. A structured PDF can often be read directly; a scanned form needs optical character recognition first; a photograph taken at an angle needs correction before either.
Volume matters too. A document type that arrives a few times a year may not be worth automating. One that arrives thousands of times a month almost certainly is.
Step 2: Define exactly what to extract
Write down the fields you need and what a correct value looks like for each. This is also where you decide how accurate extraction must be, which varies by field. An invoice total must be exact. A document summary may only need to be useful.
Good definitions include examples, especially of difficult cases: dates in unusual formats, amounts split across lines, names that could be a person or a company.
Step 3: Preserve structure on the way in
Documents have structure: sections, tables, headers, footnotes and figures. Flattening everything into plain text loses information that extraction and search depend on. A table row read as a stream of words, for example, no longer shows which value belongs to which column.
Our Enterprise Hierarchical RAG platform ingests documents with their structure intact and includes image analysis, so content that exists only in figures and scans is still found. It runs across more than 160 client deployments.
Step 4: Extract with models built for messy input
Named entity recognition and extraction models must cope with inconsistent formatting, domain-specific terminology and entities that only make sense in context. Models that perform well on clean benchmark text often struggle here.
Our Advanced NER Pipeline combines two approaches: stacked BERT-CRF models that label each word, and reverse question answering that asks the document directly for what is needed. Because they fail in different ways, together they catch cases either would miss.
Step 5: Evaluate against your own documents
Measure accuracy on a sample of your own documents, field by field. Benchmark scores do not tell you how a system will perform on your invoices or contracts. Build a test set that includes the hardest real examples, and use it for every change.
Break results down by field. A healthy looking overall score can hide one critical field that is wrong far too often.
Step 6: Route uncertainty to people
No extraction system is perfect. The practical goal is a system that knows when it is unsure and sends those cases for review, while handling the confident ones automatically.
That design gives you the best of both: most documents flow through without anyone touching them, and difficult ones get human attention. Corrections from review then become training data, so the system improves from exactly the cases it found hardest.
Step 7: Connect the output
Extracted data is only valuable once it reaches the systems that use it. Plan the integration, whether into an ERP, a CRM or a data warehouse, as part of the project rather than an afterthought. Include validation at this boundary too, so malformed data is caught before it reaches a system of record.
Step 8: Monitor in production
Document formats change. A supplier updates its invoice template; a new form version appears. Monitor extraction confidence and review rates over time, because a sudden change in either usually means the documents have changed.
Where the value really comes from
The win is rarely the extraction itself. It is the hours returned to people who were retyping, the errors that no longer happen, and the speed at which information reaches the decisions that depend on it. Measure those, not only extraction accuracy, and you will know whether the project is working.
Common mistakes
- Testing only on clean samples, then discovering the real documents are much messier.
- Aiming for full automation instead of automating the confident cases and reviewing the rest.
- Treating it as finished at launch, when document formats keep changing.