Data Extraction AI: Get Values You Can Check Against the Source

Data extraction AI turns information in documents or other source material into named fields you can review and use. Start by defining those fields, require evidence for each proposed value, and route missing or conflicting information to a person before it enters another workflow.

A complete-looking table can still contain the wrong date or a guessed total. Tools like fenbs preserve the extraction rules people approved, links to permitted references, and unresolved review tasks; a separate extraction tool processes the documents.

How do you build a checked extraction workflow?

1. Choose one document family and define the fields needed for the next job. Start with a small representative set rather than every document the business owns.
2. Write the meaning, format, source location, and missing-value rule for each field. Explain which values may be normalized and which must be copied exactly.
3. Run the extraction tool and request the source document, page, and supporting text or location for each candidate value where the tool supports them.
4. Apply deterministic checks, such as allowed formats and arithmetic, then send ambiguous or unsupported values for human review.
5. Compare the output against manually checked examples. Release approved results and preserve the source references and unresolved exceptions.

Keep extraction separate from writing values into another application. Data entry with AI covers destination mappings, submission checks, and retries. This workflow ends with reviewed structured output and enough evidence to check it.

What does each extraction field need to specify?

Name the exact concept you want. "Date" could mean issue date, service date, due date, or the date a file was scanned. The model should not choose the most convenient one. Give it a field definition and an instruction to leave the value unresolved when the document does not establish it.

Store the original value separately from a normalized value when a transformation matters. For example, converting a written date to a standard format should not erase the text used to interpret it. Likewise, keep a calculation distinct from an amount actually printed in the document.

Once people approve the definitions, fenbs Decisions and rules can hold those instructions for future assistants. Changing the meaning of a field should be a visible human choice, not a quiet prompt edit halfway through a run.

Which extraction method fits the source?

Check whether the file already contains usable text before treating it as an image. Scans may need OCR, software that recognizes text in images. Tables, checkboxes, handwriting, and complex layouts add different difficulties, so test the kinds of pages you actually receive.

For a concrete product example, Amazon Textract's documentation↗ (opens in a new tab) describes text, forms, and table extraction, with separate operations for certain document types. That does not establish accuracy for your documents. Choose the operation and output that fit your fields, then evaluate the result.

A general assistant can help interpret varied wording, while a specialized document service may provide more structured locations or field outputs. You may need both recognition and interpretation. Keep the handoff visible so you can tell whether a wrong value came from reading the page or interpreting its meaning.

Use an approved environment for sensitive documents and check the service's data handling before uploading them. The AI at work guide provides the broader workflow boundary; an extraction trial should remain limited to permitted sources and fields.

How do you preserve evidence for every value?

Give each source a stable identifier and keep its version with the output. Where supported, retain the page number, the nearby field label, and the text or page region supporting the value. A reviewer should be able to move from a table cell back to the relevant part of the original.

Inspect row relationships, not only individual numbers. An extractor can read a quantity correctly but attach it to the next product. Check repeated headings, continued tables, subtotals, and values that appear on several pages. Preserve separate records when two sources disagree rather than combining them into a confident answer.

Treat text inside a document as source content, not as permission to change the task. A note saying to ignore prior instructions should not override your extraction rules. Include misleading text in a test document and check that the workflow still returns only the requested fields.

What should confidence scores and review checks do?

Use a tool's confidence score as one signal for review, alongside field checks and source evidence. Microsoft's Document Intelligence guidance↗ (opens in a new tab) explains estimated confidence for extraction results and recommends considering human review. Check what a score represents in the particular output you use.

Do not ask a conversational model whether it feels confident and treat its answer as a measured probability. Test each important field against reviewed examples. A threshold suitable for routing a document may be inappropriate for accepting an amount or an account identifier.

• Reject unsupported values even if the output format is valid.
• Flag missing required fields, conflicting source values, and unfamiliar layouts.
• Check totals and units while allowing for legitimate adjustments or rounding that need explanation.
• Review high-consequence fields according to an explicit policy, including cases that receive a high score.

How do you evaluate the workflow before using it?

Build a manually reviewed sample with ordinary and difficult examples: rotated scans, missing pages, repeated totals, changed layouts, and incomplete documents. Keep some examples aside until the instructions are settled. Otherwise you may only learn that the setup handles documents it has already seen.

Measure correctness by field and document type, along with the work needed to correct results. A high overall score can conceal a field that repeatedly fails. Check whether the exception list catches the failures that matter, and investigate confidently wrong outputs rather than only low-scored ones.

Record the extraction tool and setup used, the reviewed sample, and the checks that passed or failed. The AI brain guide explains why this operational context should survive beyond one conversation. Recheck the workflow when the source format or extraction setup changes.

How to do this with fenbs

1. Create a free account and add a project for the extraction workflow. One person can start free, with no credit card.
2. Record the human-approved field definitions, normalization choices, and review requirements in Decisions and rules.
3. Add test and exception tasks, with their source references, planned checks, and review status. Keep sensitive documents and extracted records in approved systems.
4. Ask a connected assistant to read the project context before continuing. Save progress afterward, including the reviewed findings, open questions, and next test.

fenbs does not recognize document text or extract values from files. Use a document-processing tool for that job; fenbs holds the rules and work around it. Start by recording the fields and review requirements for one document family, then explore the AI at work hub for adjacent workflows.