Documents and reporting · International teams
AI document data extraction
Turn selected document fields into structured records that staff can check against the original.
Understand the service
What this means in practice
Document extraction converts text, tables or form fields into a defined data structure. OCR may first recognise text in a scan; an extraction model then identifies the fields your process needs. These are different steps, and a readable document can still produce an incorrect field assignment.
Temrik can assess one document family and design a checked extraction workflow. The first decision is the output schema: which fields matter, which formats are valid and what should happen when information is missing. Keep the original page accessible so reviewers can resolve uncertainty without guessing.
- Microsoft: Document Intelligence overview Document models extract information from supported inputs.
- NIST: AI measurement and evaluation Measurement should reflect the technology and its use.
A practical workflow example
A team receives delivery dockets in several formats. A proposed workflow extracts the supplier, reference, delivery date and item quantities, then checks the result against the expected order. A blurred quantity or unexpected item remains an exception instead of becoming an apparently clean record.
Proposed engagement
How we would approach the work
Sample the variation
Gather authorised examples covering scan quality, layouts, languages and handwritten additions. Keep a separate test set that is not used to tune the extraction.
Define field checks
Specify required fields, accepted formats and cross-checks such as totals or order references. Store source locations with extracted values where supported.
Measure usable output
Evaluate each important field and the time needed for correction. Test missing pages and duplicate documents before connecting the output to another system.
Deliverables to agree in the scope
- A document inventory and extraction schema.
- A representative evaluation set with field-level results.
- A review queue design and proposed downstream mapping.
Access, sample information and reviewer availability affect the plan. Any implementation, provider costs, support arrangements and acceptance criteria are agreed before work begins.
Limits worth understanding
- Poor scans, unusual layouts and ambiguous tables can require manual handling.
- A provider confidence value is not a guarantee that an extracted field is correct.
Questions to bring to the first conversation
- Which fields create the most retyping today?
- What error rate can the receiving process tolerate?
- Can reviewers see the original document beside the result?
International teams
Scope the work for your operating context.
For documents from multiple countries, include different date formats, decimal separators, currencies and languages in the test set. Do not merge fields with similar labels until the document owner confirms they mean the same thing.
A starting reference for your review: NIST: AI Risk Management Framework. Local obligations and deployment settings need to be assessed for the actual use case.
A focused next step
Work with Temrik.
Tell us about the workflow you want to improve and the outcome you need. We can review the context and discuss a focused assessment. Scope and price are agreed before paid work begins.