Workflow

The data is in the PDF. The work is getting it out

Roughly 80% of enterprise data is unstructured, and a great deal of it arrives as a PDF somebody has to retype. Attach the file, tell SheetAI which columns you want, and it writes the rows into your sheet, with the page each figure came from, so a reviewer can check it against the source.

Try it on your sheet See real workflows

Audit-ready trailNo data trainingReversible per action

80-90%

Of company data is unstructured

MIT Sloan reports that multiple analyst estimates put 80% to 90% of data in unstructured forms such as text, documents and media rather than tables.

Source: MIT Sloan: Tapping the power of unstructured data (2021)

6.57%

Error rate when abstracting records by hand

A systematic review and meta-analysis by Garza and colleagues found a pooled error rate of 6.57% when people read source records and transcribe fields by hand.

Source: Garza et al.: Error Rates of Data Processing Methods in Clinical Research (2023)

8.2 days

Average time to process an invoice

Ardent Partners' 2025 accounts payable research puts the industry average at 8.2 days per invoice, against 2.9 days for teams using advanced automation.

Source: Ardent Partners: State of ePayables 2025 (via Medius)

PDF Data Extraction: illustration for SheetAI's pdf data extraction use case

The average organization still pays around $10 to process a single invoice.

Best-in-class AP teams get that under $3, and the gap is almost entirely manual keying and the exception handling that follows it. The work is not judgment; it is transcription. That is exactly the task to hand to a tool that reads the document and writes the cells.

Source: Ardent Partners: Accounts Payable Metrics

This page is about getting structured rows out of unstructured documents: supplier invoices, bank and brokerage statements, remittance advices, lab reports, purchase orders, government filings, insurance schedules, and the endless supply of PDFs that arrive as attachments and leave as manual data entry. SheetAI reads the document you attach, extracts the fields you name, and writes them into a range you choose. It shows you where each value came from, flags the ones it is unsure about instead of guessing, and leaves every step reversible. It is not an OCR engine for a filing-cabinet backlog, and it will not silently invent a total that does not appear in the file; the boundaries section below is specific about both.

Why PDF data entry survives every wave of automation

Four numbers that explain why a task everyone agrees is beneath them is still on somebody's desk in 2026.

Enterprise data that is unstructured rather than tabular

~80%

Contracts, invoices, statements, reports, and scans. None of it is addressable by a formula until somebody moves it into a grid, which is why the grid is where the extraction should happen.

IDC / Gartner, widely cited

AP invoice cost gap: average vs. top quartile

$10 vs. $3

Ardent Partners' benchmarks consistently show a 3–4x cost spread between typical and best-in-class AP operations, driven overwhelmingly by touchless processing rates rather than headcount.

Ardent Partners

Finance staff time spent on data gathering rather than analysis

~50%

Repeated finance-function surveys put roughly half of analyst time on collecting and formatting data. Extraction is the first and largest slice of that.

Deloitte / APQC benchmarks

Accuracy of trained manual data entry, per field

~99.5%

Sounds excellent until you multiply it out. At 0.5% per field, a 20,000-cell extraction carries around 100 errors, and none of them announce themselves.

Standard data-entry QA benchmarks

The pattern: the bottleneck is not that the data is hard to understand; a human reads an invoice in four seconds. The bottleneck is that reading it and typing it are two different actions, and only one of them is automatable. Doing the extraction inside the sheet means the typing step disappears and the reading step stays reviewable.

Anatomy of a PDF extraction job

What actually happens when a folder of documents needs to become one table. If this is your Tuesday, you are not disorganized; you are doing the job as it currently exists.

Phase 1: Collect and triage the documents

›Pull attachments out of email, a shared drive, or a portal
›Sort by vendor, format, or period
›Identify which are native PDFs and which are scans

⚠ Native PDFs have a text layer you can select. Scans do not. The two need completely different handling and nobody labels which is which.

Phase 2: Find the fields in each layout

›Locate invoice number, date, net, tax, gross, PO reference
›Work out where each vendor puts each field
›Handle the ones that split a table across three pages

⚠ Every vendor has a different layout, and the vendor who changed theirs last quarter did not tell you. Template-based extractors break here every time.

Phase 3: Key it into the sheet

›Type or copy-paste each field into the right column
›Re-type the numbers the copy-paste mangled
›Fix the dates Excel decided were American

⚠ This is the hour that does not scale. It is also where the 0.5% error rate lives, and the errors are invisible until a total is wrong three weeks later.

Phase 4: Reconcile against the system of record

›Match extracted invoices to open POs
›Flag amounts that disagree with the ledger
›Chase the ones with no matching record

⚠ A structural join, done by eye. It is the part that actually needs a human and the part that gets the least time, because phase 3 ate the afternoon.

Phase 5: File the evidence

›Note which document each row came from
›Save the source files somewhere a reviewer can find
›Answer the auditor's question about row 412 six months later

⚠ Provenance is the first thing dropped when the deadline is close, and the first thing requested when the audit starts.

Cautionary tale

Why extraction accuracy is a control issue, not a convenience

In 2012, JPMorgan's Chief Investment Office lost more than $6 billion in what became known as the London Whale episode. The bank's own internal task force report found that a critical value-at-risk model had been implemented in a spreadsheet that required an analyst to copy and paste data between worksheets by hand, and that an operational error in that process (a division by a sum rather than an average) understated volatility by roughly a factor of two. The model was not wrong because the maths was hard. It was wrong because a human was moving numbers between documents by hand, at speed, without a check.

The lesson: Manual transcription is not a clerical detail; it is an unreviewed control point sitting in the middle of your reporting chain. The fix is not to transcribe more carefully. It is to make the transfer machine-performed and machine-recorded, so the number in cell D14 can be traced back to page 3 of a specific document without anybody's memory being involved. SheetAI writes that provenance alongside the value, and flags low-confidence reads instead of quietly filling them in.

Source: JPMorgan Chase: Report of the Management Task Force Regarding 2012 CIO Losses

Who it's for

If a recurring part of your week is reading one document and typing into another, this is the page.

Accounts Payable Teams

Key hundreds of supplier invoices a month across dozens of layouts, then handle the exceptions keying created.

Bookkeepers & Accountants

Turn client bank and card statements into a coded transaction list every month, per client, forever.

Financial Analysts

Pull figures out of filings, fund factsheets, and board packs to build a comparison that has to be right.

Operations & Procurement

Reconcile purchase orders, delivery notes, and invoices that agree with each other about 80% of the time.

Researchers & Policy Analysts

Extract tables from published reports and government PDFs that were never released as data.

Real extraction workflows

The exact prompt, what the agent does with it, and the state your sheet is in afterwards.

pdf-data-extraction_workbook.xlsx - SheetAI

You ask

“I've attached 12 supplier invoices. Extract invoice number, invoice date, supplier name, PO reference, net, VAT, and gross into Invoices!A1 with headers. Add a Source column naming the file and page each row came from. If a field is missing or unclear, write UNCLEAR rather than guessing.”

SheetAI does

✓Reads each attached document and identifies the field positions per layout.
✓Normalises dates to ISO and strips currency symbols so the amounts are numeric.
✓Writes one row per invoice with a source reference for each.
✓Marks unreadable or absent fields UNCLEAR instead of inferring them.

Formula written

=SUMPRODUCT((Invoices!G2:G500)*(Invoices!C2:C500=A2))

Result

12 invoices, 84 fields, written in under two minutes. Three rows flagged UNCLEAR (two missing PO references and one handwritten amendment) which is precisely the set a human should look at.

Everything you need to get a document into a grid

Attach the file, name the fields, and get rows with provenance attached. Numbers arrive numeric, dates arrive as dates, and anything ambiguous arrives flagged rather than filled in.

✓Native PDF, DOCX, and XLSX attachments

✓Multi-page and split-table handling

✓Per-row source and page references

✓Numeric and date normalisation on write

✓Low-confidence fields flagged, not guessed

✓Arithmetic self-checks on totals and balances

Plays well with your stack

PDF attachments (native text layer)
Word (.docx) and Excel (.xlsx) attachments
CSV exports from any document processor
Published reports fetched from a URL
Bank and card statement downloads
Accounting exports for reconciliation (Xero, QuickBooks, Sage)

What a month of AP extraction looks like

A representative finance team processing roughly 400 supplier invoices a month across 60 vendors, before and after the extraction step moves into the sheet.

Before SheetAI

~34 hours

Week 1
Key the first batch of invoices; fix date and currency mangling
~9h
Week 2
Second batch, plus the three vendors who changed their layout
~9h
Week 3
Match against open POs by eye; build the exception list
~8h
Week 4
Chase exceptions, re-key the disputed lines, file the evidence
~8h

✓

With SheetAI

~7 hours

Week 1
Attach batch, extract to register, review the flagged rows
~2h
Week 2
Second batch; layout changes handled without a template rebuild
~2h
Week 3
PO match runs as a join; exceptions arrive pre-sorted
~1h
Week 4
Chase the real exceptions; provenance already attached
~2h

~80% reduction in keying and exception hours on a representative 400-invoice month.

What SheetAI will not do

Extraction is a place where confident wrong answers do real damage, so it is worth being exact about where the tool stops.

OCR a low-quality scan and pretend it is certain

Documents with a text layer extract reliably. Photographs of paper, faxed scans, and handwriting do not, and the failure mode is a plausible wrong digit rather than an obvious blank. Where the read is uncertain SheetAI marks the field rather than filling it, and for a large backlog of poor scans a dedicated OCR pipeline is the right tool before this one.

Invent a figure that is not in the document

If an invoice shows net and gross but no tax line, the tax cell stays empty and flagged. Deriving it may well be correct, but a derived number that looks extracted is the single most dangerous output an extraction tool can produce. You can ask for the calculation explicitly, as a formula you can see.

Replace your three-way match controls

It can build the PO-to-receipt-to-invoice comparison and rank the exceptions, which is most of the labour. Approving a payment against that comparison is a control that belongs to a person with authority, and it should stay that way.

Process documents you are not permitted to process

Bank statements, medical letters, and HR files carry obligations that do not disappear because the file is an attachment. Check what your policy allows before uploading, and for anything covered by HIPAA see the guidance on the healthcare page.

“

We had a person whose actual job was retyping invoices. Not checking them, retyping them. Now the rows land in the register with the file name attached and she spends her week on the exceptions, which is the part that was always getting squeezed. The first month we found a duplicate payment that had been sitting there since March.

Financial Controller, distribution business (~400 invoices/month)

Frequently asked

What people ask before they trust a machine with their invoices.

How accurate is the extraction?

›

For PDFs with a real text layer (anything generated by a computer rather than photographed) extraction of clearly labelled fields is highly reliable, and the arithmetic self-checks catch most of what slips through. The number that matters more is what happens when it is unsure: it writes UNCLEAR and flags the row rather than guessing. That is the behaviour that makes the output reviewable, because your attention goes to the handful of flagged rows instead of spot-checking all 400.

Does it work on scanned documents?

›

Partially, and honestly it is the weakest case. A clean scan of printed text usually reads well. A phone photograph, a fax, a third-generation photocopy, or anything handwritten is unreliable, and unreliable extraction is worse than none because the errors look like data. If your backlog is mostly scans, run them through a dedicated OCR step first and bring the output here.

Can it handle invoices from different vendors with different layouts?

›

Yes. This is where it beats template-based extractors. Traditional tools need a template per vendor and break silently when a vendor changes their layout. SheetAI reads the document rather than matching positions, so a moved logo or a new column does not break it. When a layout changes materially it flags the rows rather than mapping fields to the wrong columns.

How many documents can I do at once?

›

Attach a batch and it processes them into one table. For very large volumes (thousands of documents a month) the practical pattern is batches of a few dozen, which also keeps each batch reviewable. If you are processing at that scale continuously, a dedicated AP automation platform is a reasonable thing to own; this is the tool for the long tail it does not cover.

Where does the data go?

›

Into the sheet you name, in the range you specify, in your account. The document is processed to produce the extraction and the result is written to your workbook. Nothing is published anywhere, and every write is reversible from the history.

Can I see where each number came from?

›

Yes, and you should ask for it. Adding a source column to the prompt gives you the file and page each row was read from, which turns an audit question from an afternoon of hunting into a lookup. It is the single most useful thing to include in an extraction prompt and the most commonly forgotten.

What about tables that span multiple pages?

›

Handled: continuation rows are appended rather than treated as a new table, and repeated headers are not written as data. Statements with carried-forward balances are the common case and the balance check catches it if a page was missing from the source.

Does it work in Excel and Google Sheets?

›

Yes. The extraction writes standard values and standard formulas, so the resulting workbook is portable. The Excel add-in and the Google Sheets add-on let you run the same prompts inside Microsoft 365 and Google Workspace.

Sources & further reading

Every benchmark and statistic on this page is drawn from publicly available research. We cite our sources because we read theirs.

Get started

Stop retyping documents.
Start reviewing them.

Attach a PDF, name the columns you want, and watch the rows land in your sheet with the page reference attached. No template setup, no per-vendor configuration.

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