Eliminate Manual Data Entry With AI That Reads Your Documents for You
Quick answer: AI data extraction automation reads documents such as PDFs, forms, emails, scans and spreadsheets, identifies the fields you need, checks them against your rules and sends the results to your systems. Low-confidence items go to a person for quick review.
DEFINITION
What Is AI Data Extraction Automation?
AI data extraction, sometimes called intelligent document processing, uses AI to turn unstructured documents into structured data. It combines optical character recognition, which reads text from images, with models that understand layout and meaning, so they know that a number is a total, a date is a due date, or a block of text is an address.
Unlike older template-based tools, it doesn't need a fixed layout for every document. It finds the fields you care about across many formats and quality levels, scores how confident it is, and works alongside people who review only what needs checking.
THE PROBLEM
The Problem
Most businesses still depend on someone reading a document and typing what it says into another system. Application forms, purchase orders, contracts, delivery notes, claims, ID documents and supplier spreadsheets all arrive in different layouts, and each one needs the same few fields copied across.
Manual entry is slow, repetitive and error-prone. A single mistyped number can cause a wrong shipment, a payment error or a compliance issue that takes far longer to fix than it took to make. As volume grows, teams fall behind, backlogs build and skilled people spend their days on work that adds little value.
Older automation tools only help when every document looks the same. The moment a supplier changes its template or a customer sends a phone photo instead of a PDF, rule-based extraction breaks and the work lands back on your team.
SIGNS YOU NEED IT
Signs You Need This
Staff spend hours each day copying data from documents into systems
Backlogs grow during busy periods and delay customers or payments
Errors in entered data cause rework, disputes or compliance risk
Your documents come in many layouts, formats and qualities
Template-based tools break whenever a document changes
You can't scale operations without hiring more data entry staff
THE SOLUTION
How Codersarts AI Solves It
Codersarts AI builds extraction pipelines that read documents the way a careful person would. AI models combine text recognition with an understanding of layout and context, so they find the right fields even when every document looks different, including scans, photos and handwriting where legible.
Every extracted value is checked against your rules: formats, required fields, totals that must add up and values that must match records in your systems. Confident results flow straight through. Uncertain ones go to a simple review screen where a person confirms or corrects them in seconds.
The pipeline sends clean, structured data to the systems you already use, such as your ERP, CRM, database or spreadsheets. Your team moves from typing to reviewing exceptions, and the process improves as corrections are fed back.
HOW IT WORKS
How It Works
Document review — we study your document types, fields, volumes and target systems, and agree accuracy goals
Extraction design — we define the fields, validation rules and confidence thresholds for each document type
Build the pipeline — intake, classification, extraction, validation and export are built and connected
Human review screen — a simple interface for checking low-confidence fields and exceptions
Measure and improve — accuracy is tracked per field, and corrections improve results over time
KEY FEATURES
Key Features
Any layout — works across changing templates, multi-page documents and mixed formats
Scans and photos — reads scanned documents and phone photos, not only digital PDFs
Document classification — sorts incoming files by type before extraction
Validation rules — checks formats, totals, required fields and matches with your records
Confidence scoring — routes uncertain values to a person instead of guessing
Human-in-the-loop review — fast review and correction screen for exceptions
System integration — sends data to your ERP, CRM, database, spreadsheets or workflows
Accuracy reporting — field-level accuracy and throughput tracked over time
EXAMPLE
Example Workflow
A logistics company receives 400 delivery notes a day by email, as PDFs and phone photos
The pipeline collects each attachment and classifies it as a delivery note
It extracts the order number, date, items, quantities and signature status
Values are checked against open orders in the company's system
Most notes post automatically, while a few with a smudged quantity go to review
A team member confirms the flagged values in seconds and the order is updated
WHERE IT FITS
Where It Works Best
Logistics — delivery notes, bills of lading, customs documents and proof of delivery
Insurance — claims forms, supporting documents and policy schedules
Financial services — applications, bank statements and know-your-customer documents
Healthcare administration — referral letters, intake forms and insurance details
Real estate and legal — contracts, leases and key clause extraction
COMPARE OPTIONS
How It Compares
AI data extraction | Template-based OCR | Manual data entry | |
Changing layouts | Handles them | Breaks on change | Handles them |
Speed at volume | Seconds per document | Fast, when templates match | Minutes per document |
Accuracy checks | Built-in validation and confidence | Limited | Depends on the person |
Scans and photos | Yes | Often poor | Yes |
Human review | Only for exceptions | Frequent manual fixes | Every document |
Scales with volume | Yes | Partly | No, needs more staff |
COMMON CHALLENGES
Common Challenges and How We Handle Them
Poor scan quality — images are cleaned and enhanced before reading, and unreadable fields are flagged for review
Many document layouts — models understand context, so new layouts don't need new templates
Errors reaching your systems — validation rules and confidence thresholds stop doubtful data from posting automatically
Sensitive personal data — private hosting, encryption, access control and retention rules are built in
INTEGRATIONS
Integrations and Platforms
Documents can arrive by email, upload portal, shared folder, scanner or API. Extracted data can be sent to your ERP, CRM, accounting or operations system, a database, spreadsheets or workflow tools such as n8n, through APIs or file exports.
We built and run our own document AI product, DocProcessing360, and bring that experience to every pipeline. We recommend models and hosting based on accuracy, cost per document and data residency, including private deployment where required.
TIMELINE
Implementation Timeline
Phase | What happens |
Weeks 1–2 | document review, field definitions and sample testing |
Weeks 3–4 | pipeline build, validation rules and integrations |
Weeks 5–6 | review screen, accuracy testing on real documents and launch |
After launch | new document types and monthly accuracy reviews |
SECURITY
Security and Privacy
Documents and data stay in accounts you own
Your data is not used to train AI models
Encryption in transit and at rest
Role-based access to the review screen and results
Automatic retention and deletion rules for processed documents
PRICING
Cost and Pricing
We quote a fixed price after reviewing sample documents, based on the number of document types, fields, volume and systems to connect. There's no hourly billing.
Running costs are AI processing per document and hosting, paid directly to your providers. We estimate cost per document during scoping, so you can compare it with the cost of manual entry.
BUILD OR BUY
Custom Build or Off-the-Shelf Tool?
Off-the-shelf document capture tools work well for common document types such as standard invoices or receipts. They become limiting when your documents are specialised, your validation rules are specific, or the data needs to flow into custom systems and workflows.
A custom pipeline is built around your exact document types, fields, rules and systems. You own it, can run it in your own cloud, and can add new document types without buying new modules.
MEASURING SUCCESS
How We Measure Success
Straight-through rate — the share of documents processed without any human touch
Field accuracy — accuracy measured per field on your real documents
Processing time — the time from document arrival to data in your system
Cost per document — AI processing and review time compared with manual entry
Backlog — the number of documents waiting to be processed
GETTING STARTED
What You'll Need to Get Started
20 to 50 sample documents for each document type, including difficult ones
The list of fields you need from each document
Your validation rules, such as required fields or matching records
Access to the target system, or its API documentation
An owner to review accuracy results during testing
BEST PRACTICES
Best Practices for Success
Start with your highest-volume document — the biggest manual workload gives the fastest, clearest payback
Collect difficult samples — include poor scans, unusual layouts and edge cases in testing, not just clean examples
Set confidence thresholds per field — critical fields like amounts deserve stricter review rules than optional ones
Keep a human in the loop — review exceptions rather than removing people from the process entirely
Track accuracy over time — measure field accuracy monthly and feed corrections back into the pipeline
KEY TERMS
Key Terms Explained
Intelligent document processing (IDP) — using AI to classify, read and extract data from documents
OCR — optical character recognition, which turns text in images into machine-readable text
Straight-through processing — documents that go from arrival to your system with no manual step
Confidence score — the AI's measure of how sure it is about each extracted value
Human-in-the-loop — a person reviews items the AI isn't confident about
OUTCOMES
What You Can Expect
Documents processed in seconds instead of minutes
Far fewer data entry errors reaching your systems
Your team reviews exceptions instead of typing everything
Volume can grow without adding data entry staff
Clear, measurable accuracy for every field
WHY CODERSARTS AI
Why Teams Choose Codersarts AI
Fixed price — a clear scope, timeline and price agreed before work starts
Built around your process — designed for your documents, rules and systems, not a generic template
Works with your team — we collaborate with your developers, operations or IT team from day one
Start focused, then grow — launch a tight first version, then expand as results come in
You own it — full source code, documentation and IP transferred on delivery
FIRST 90 DAYS
What the First 90 Days Look Like
First 30 days — the pipeline runs alongside your current process so results can be compared with manual entry
Days 31–60 — confident documents flow straight through, and reviewers handle only flagged items
Days 61–90 — new document types are added and accuracy is reviewed with your team every month
WHO'S INVOLVED
Who's Involved
Operations owner — defines fields, rules and accuracy targets
Reviewers — check flagged items in the review screen
IT or systems contact — connects target systems
Codersarts AI team — builds, tests and hands over
BUILD IT WITH US
Related Services
RELATED USE CASES
Related Use Cases
FAQ
Frequently Asked Questions
What types of documents can it handle?
Forms, PDFs, emails, scans, phone photos, spreadsheets and multi-page documents, including ones with changing layouts.
How accurate is AI data extraction?
Accuracy depends on document quality and complexity. We measure it per field on your real documents and set thresholds so uncertain values are reviewed by a person.
Can it read handwriting?
It can read clear handwriting in many cases. Unclear handwriting is flagged for human review.
Does it work with our systems?
If your system has an API, accepts file imports or connects to workflow tools, we can send extracted data to it.
What happens when the AI isn't sure?
Low-confidence values go to a review screen where a person confirms or corrects them, and those corrections improve future results.
Can it handle sensitive documents?
Yes. We use private hosting, encryption, access controls and retention rules, and your data is never used to train models.
Do you have your own document AI product?
Yes. We built DocProcessing360, our own document processing platform, and use that experience in custom pipelines.
How long does it take?
A first version for one or two document types typically takes about 6 weeks.
Can it process documents in other languages?
Yes. Many languages are supported, and we test accuracy on your real documents in each language.
How many documents can it handle?
Pipelines scale from dozens to many thousands of documents a day, depending on hosting and volume.
Can we add new document types later?
Yes. New types can be added by defining their fields and rules, without rebuilding the pipeline.
Can it run in our own cloud?
Yes. We can deploy the pipeline in your cloud account, with private model options for sensitive data.
Can it extract tables from documents?
Yes. Tables such as line items, schedules and statements can be extracted row by row into structured data.
What happens to documents after processing?
They're stored or deleted according to retention rules you set, and only the extracted data is kept where required.
Can it extract data from emails as well as attachments?
Yes. Email bodies, subjects and attachments can all be processed as part of the same pipeline.
Can it detect missing documents or pages?
Yes. Rules can flag missing pages, missing required fields or incomplete document sets for review.
Ready to Build Your AI Data Extraction Automation?
Tell us about your process and goals. We'll reply with a clear scope, timeline and fixed price.