How to Automate Data Extraction With AI

How to Automate Data Extraction With AI

AI data extraction turns information from documents, messages, notes, or other unstructured sources into defined fields that a workflow can use.

A simple extraction workflow may look like:

Source Text
→ AI Extraction
→ Structured Output
→ Human Review

A more complete process may also classify the document, prepare the source, validate field types, check totals, route uncertain cases, and save an approved result.

AI is useful because the same information may appear in different places or use different wording.

For example, an invoice total may appear as:

• Total due;
• Amount payable;
• Balance;
• Grand total; or
• Total including tax.

A model can interpret these variations and return one consistent field.

The extracted result should not become trusted business data automatically.

Important fields still need validation, source evidence, and human review when an error could affect money, customers, access, legal rights, safety, or another consequential process.

What is AI data extraction?

AI data extraction is the process of identifying selected information in an unstructured or semi-structured source and returning it in a predictable format.

The source may be:

• an email;
• a PDF;
• a report;
• a form;
• an invoice;
• meeting notes;
• a contract;
• a customer message;
• a research paper;
• an image containing text; or
• a group of related documents.

The output may be:

• a table;
• named fields;
• a JSON-like structure;
• a list of records;
• a classification;
• a checklist; or
• another format used by a later workflow step.

AI extraction is different from summarisation.

A summary condenses the meaning of a source.

Extraction returns specific information defined in advance.

Choose a narrow extraction task

Start with one document type or source pattern.

Instead of:

Extract everything important from this document.

choose:

Extract the supplier name, invoice number, invoice date, due date,
currency, subtotal, tax, total, and purchase-order number.

A narrow task is easier to test.

It also reduces the chance that the model returns fields that the process does not need.

Good first extraction tasks are:

• repeated often;
• based on sources you understand;
• expected to return a fixed set of fields;
• easy to compare with the original;
• low risk while the workflow is being tested; and
• useful to another review or workflow step.

Avoid beginning with highly variable document packets containing many unrelated document types.

Define the extraction schema

A schema describes the fields the workflow should return.

For every field, define:

• field name;
• description;
• expected type;
• allowed format;
• whether it is required;
• what counts as missing;
• whether several values are allowed; and
• which source text should support it.

For example:

Supplier name:
Invoice number:
Invoice date:
Due date:
Currency:
Subtotal:
Tax:
Total:
Purchase-order number:
Missing information:

Clear field names improve consistency.

Avoid vague names such as Value, Information, or Other details.

A model cannot reliably follow a schema that the workflow designer has not defined clearly.

Describe each field precisely

Similar fields can be confused.

For example:

• invoice date is not the due date;
• subtotal is not the final total;
• customer is not the supplier;
• contract start date is not the signature date;
• requested action is not the completed action; and
• proposed deadline is not the confirmed deadline.

Add a short description when the distinction matters.

For example:

Due date:
The date by which payment is requested.
Do not return the invoice date or delivery date.

Include examples for difficult fields.

Keep definitions stable across test runs so results can be compared fairly.

Define missing-value behaviour

Models often try to complete a structure even when the source is incomplete.

Tell the model how to represent missing information.

Use a value such as:

Not provided

Other options may include an empty field, null, or a specific status used by the later workflow.

Do not use a plausible estimate.

For example, if an invoice has no purchase-order number, the workflow should not create one from another reference.

Preserve missing values through later steps.

A second AI step should not fill them merely to make the final record look complete.

Preserve source evidence

Important fields should remain connected to the source.

A useful extraction result may include:

Field:
Extracted value:
Source text:
Page or section:
Review status:

Source evidence helps a reviewer confirm that the value was extracted correctly.

It is especially useful for:

• amounts;
• dates;
• names;
• identifiers;
• obligations;
• deadlines;
• quotations; and
• conditions.

A model may invent a page number or source reference.

Confirm that the reference exists and contains the stated value.

Prepare the source before extraction

The quality of the source affects the result.

Preparation may include:

• removing repeated headers and footers;
• preserving headings;
• separating document types;
• converting tables into readable text;
• removing duplicate pages;
• selecting relevant sections;
• correcting page order;
• masking unnecessary personal information; or
• rejecting unreadable content.

Do not remove labels that explain what a value means.

A number without the surrounding heading may be impossible to interpret correctly.

For scanned documents or images, confirm that text recognition preserved names, decimals, dates, and special characters.

Extraction cannot be more accurate than the readable source it receives.

Classify documents before extracting fields

Different document types may require different schemas.

A mixed workflow may first classify the source as:

• Invoice;
• Purchase order;
• Contract;
• Delivery note;
• Customer request;
• Research paper; or
• Other.

The workflow can then route the source to the appropriate extraction step.

Do not use one large schema for every document type.

Irrelevant fields can encourage guessing and make validation harder.

Include an Other or Unclear route for documents that do not match the expected types.

Review classification separately from extraction.

A perfect extraction schema will still fail when the document enters the wrong route.

Use structured output

The output should be predictable enough for another workflow step to read.

Common formats include:

• labelled fields;
• tables;
• lists of records;
• key-value structures; and
• JSON-like objects.

Define:

• exact field names;
• date format;
• decimal format;
• currency format;
• whether arrays are allowed;
• whether repeated items become rows; and
• how missing values appear.

Avoid asking for a narrative explanation when the next step needs structured fields.

Keep any commentary or uncertainty in separate fields.

A valid format does not prove that the extracted content is correct.

Separate extraction from business logic

The AI step should extract information.

Fixed workflow steps should handle exact decisions.

For example:

Invoice
→ Extract Supplier, Dates, and Amounts
→ Validate Required Fields
→ Check Total
→ Apply Approval Threshold
→ Human Review

Do not ask the model to decide whether an invoice should be approved when the approval rule is a known threshold.

Separation makes the workflow easier to:

• test;
• maintain;
• audit;
• improve;
• reuse; and
• troubleshoot.

You can improve extraction accuracy without changing the approval logic.

You can update the business rule without rewriting the extraction prompt.

Validate field types

Use deterministic checks after extraction.

Validate:

• required fields;
• allowed categories;
• date formats;
• number formats;
• currency codes;
• email addresses;
• identifiers;
• duplicate values; and
• expected list lengths.

For example:

If Invoice number is Not provided → Review
If Total is not numeric → Review
If Currency is not approved → Review
If Due date is earlier than Invoice date → Review

Validation should stop or route invalid output.

It should not silently coerce an uncertain value into a normal-looking record.

Validate totals and relationships

Some fields can be checked against each other.

Examples include:

• subtotal plus tax equals total;
• line-item totals match the stated total;
• start date is before end date;
• quantity multiplied by unit price matches line total;
• stated percentage matches the related amount; and
• referenced identifier appears in the approved source system.

Use normal calculations for these checks.

Do not ask another AI model to perform exact arithmetic when a deterministic expression can do it reliably.

A failed relationship check should trigger review.

It may indicate extraction error, source inconsistency, or a document that follows a different rule.

Extract repeated items carefully

Some documents contain lists or tables.

Examples include:

• invoice line items;
• participants;
• products;
• research outcomes;
• contract obligations;
• action items; and
• transaction records.

Define the row schema.

For invoice lines, it may include:

Description:
Quantity:
Unit price:
Tax rate:
Line total:
Source row:

Test documents with:

• one item;
• many items;
• wrapped descriptions;
• missing quantities;
• discounts;
• several tax rates;
• subtotal rows; and
• notes inside the table.

The workflow should not treat totals or headings as line items.

Handle long and multi-document inputs

A long document may need staged extraction.

The workflow can:

1. divide the source by section or document;
2. extract fields from each part;
3. preserve source references;
4. combine matching records;
5. remove duplicates;
6. identify conflicts; and
7. return one final structure.

Multi-document packets should keep document identities visible.

Do not merge two different invoice numbers or people into one record.

When sources conflict, return both values and mark the conflict for review.

Do not let the combining step choose one value without an approved rule.

Add human review

Human review is appropriate when:

• a required field is missing;
• sources conflict;
• the document type is unclear;
• a total fails validation;
• the source is unreadable;
• an identifier cannot be verified;
• the value affects money or access;
• the output will update a business system; or
• the task has legal, medical, financial, employment, safety, or security consequences.

Give the reviewer:

• the original source;
• extracted fields;
• supporting source text;
• validation results;
• tool activity;
• uncertainty; and
• the proposed destination.

Record corrections so repeated errors can be used to improve the workflow.

Protect sensitive data

Extraction workflows often process confidential documents.

Before use, identify:

• which model receives the source;
• whether it is local or cloud-based;
• which tools receive fields;
• where source and output files are stored;
• what appears in logs;
• who can access the result;
• which credentials are used; and
• how long data is retained.

Send only the information required for extraction.

Remove unrelated personal details where possible.

A local model can keep model processing on the computer, but the complete workflow is only local when its tools, source files, storage, and destinations also remain local.

Build a data-extraction workflow in Feluda

Feluda is a desktop application for building and running visual AI workflows.

Begin in Workbench.

Test one source type with representative, non-sensitive examples.

Use a precise instruction such as:

Extract the following fields from the invoice:

Supplier name
Invoice number
Invoice date
Due date
Currency
Subtotal
Tax
Total
Purchase-order number

Write "Not provided" for missing fields.
Use only the invoice.
Do not calculate or guess a value.
Include the source text for each amount and date.

Compare the result with the source.

Once the schema is reliable, build the repeatable process in Studio.

Use focused Feluda blocks

A practical workflow may use:

Document Input
→ LLM Label Document Type
→ LLM Extract Fields
→ Expression Validate Fields
→ Output for Review

Use:

• LLM Label for document classification;
• LLM Extract for named fields and repeated records;
• LLM for source-based explanations or summaries;
• Expression for type checks, calculations, thresholds, and routing;
• Emit for useful intermediate output; and
• Output for approved, review, missing-information, or error results.

Keep business decisions outside the extraction block.

Feluda can connect to supported cloud providers and compatible local models.

Choose the model according to extraction accuracy, source length, supported file types, privacy, speed, cost, and available hardware.

Use tools and Genes carefully

Genes can add tools, prompts, flows, and resources.

A data-extraction tool may read a file, retrieve a record, save structured data, or update another system.

Before enabling it, check:

• what it can read;
• what it can create or change;
• which fields it receives;
• whether it connects externally;
• which account it uses;
• whether the action can be reversed; and
• how completion is confirmed.

Separate extraction from writing to the destination.

Review important fields before they update a financial, customer, operational, legal, or access-related system.

Confirm tool activity and inspect the final record.

Test the extraction workflow

Use RunFlows with:

• a normal source;
• missing fields;
• unusual field labels;
• several dates;
• several currencies;
• conflicting values;
• a long document;
• repeated items;
• an unreadable scan;
• an unrelated document;
• every classification route;
• an unavailable model; and
• a tool failure.

Confirm that the workflow:

• returns the correct fields;
• preserves source meaning;
• uses Not provided instead of guessing;
• validates types and relationships;
• routes conflicts correctly;
• keeps source evidence;
• displays errors visibly;
• avoids duplicate writes; and
• produces a useful reviewable result.

Re-test after changing the schema, model, instruction, source format, tool, or workflow logic.

Measure extraction quality

Do not measure only whether the document completed.

Track field-level results such as:

• correct values;
• missing values;
• invented values;
• wrong field assignments;
• invalid formats;
• source-reference accuracy;
• repeated-item accuracy;
• validation failures;
• reviewer correction rate;
• processing time;
• cost per approved record; and
• tool failure rate.

Some fields matter more than others.

A wrong invoice total may be more serious than a missing optional note.

Weight review and automation decisions according to field impact.

Common data-extraction mistakes

Avoid:

• asking the model to extract everything;
• using vague field names;
• failing to define missing values;
• merging extraction and business decisions;
• treating valid structure as proof of accuracy;
• removing source context;
• skipping type and relationship checks;
• processing mixed document types with one schema;
• allowing uncertain values to update systems automatically;
• ignoring scan and table quality;
• measuring document completion instead of field accuracy; and
• deploying without a review and correction process.

Data extraction should create structured information without hiding where it came from or how certain it is.

Start with one source and one schema

Choose one repeated document or message type.

Define the fields precisely.

Test representative examples in Workbench.

Build the smallest extraction and validation workflow in Studio.

Run difficult and failing cases through RunFlows.

Keep source evidence and review important fields before they trigger another action.

AI data extraction is most useful when it converts varied information into a consistent structure while preserving accuracy, provenance, privacy, and human control.