Batch Processing & Repetitive Tasks

Last verified: 14 February 2026 | Applies to: Pro, Max, Team, Enterprise (requires Cowork for file operations)

In 30 seconds

Every business has tasks that are identical in structure but done one at a time — renaming files, extracting data from invoices, reformatting reports, generating personalised emails from a list. Claude handles these in bulk through Cowork: you define the pattern once, point Claude at the pile, and let it work through the lot. This is where operators report the most dramatic time savings, with hours of manual work reduced to minutes.

What it does

Batch processing in Claude means applying a repeatable action to many items. Cowork can:

Process files in a folder — read, rename, reformat, extract data, or convert files in bulk
Transform data row by row — take a spreadsheet and apply logic, categorisation, or enrichment to each row
Generate documents from a template — produce personalised letters, emails, reports, or certificates from a data source
Extract and structure information — pull specific fields from PDFs, invoices, or contracts and compile them into a spreadsheet
Apply quality checks — review a batch of documents against a set of criteria and flag the ones that don’t pass

The pattern is always the same: define the rule, point Claude at the batch, review the output.

How to set it up

Step 1: Organise your input

Batch processing works best when your inputs are structured. Before starting:

Put all files in a single folder that Cowork can access
If using a spreadsheet, ensure each row represents one item and columns are clearly labelled
If using documents (PDFs, Word files), ensure they share a consistent format

Step 2: Define the pattern with one example

Start with a single item so Claude understands the pattern before scaling up:

I have a folder of 85 supplier invoices as PDFs at /Documents/invoices/february/. Here's what I need from each one:
- Supplier name
- Invoice number
- Invoice date
- Due date
- Total amount (including GST)
- GST amount

Start with just the first invoice so I can check your extraction is correct.

Claude processes the first invoice and shows you the result. Review it — this is your quality gate.

Step 3: Scale to the full batch

Once the pattern is confirmed:

That extraction looks correct. Now process all 85 invoices in the folder. Output the results as a CSV file with one row per invoice, using those same columns. Flag any invoices where you couldn't confidently extract a field.

Claude works through the folder and produces a structured output. Flagged items get your manual attention; the rest are done.

How operators actually use it

Invoice data extraction

Process all PDF invoices in /Documents/invoices/february/. For each invoice, extract: supplier name, invoice number, date, due date, line items (description, quantity, unit price, total), subtotal, GST, and total. Output as an Excel file with one tab per invoice and a summary tab with all invoices listed. Flag any invoices where the total doesn't match the sum of line items.

An accounts payable task that takes a bookkeeper half a day gets done in minutes. The flagged invoices are the ones that actually need human attention.

File renaming and organisation

I have 200 client documents in /Documents/client-files/ with inconsistent naming — some use client codes, some use full names, some have dates, some don't. Here's our naming convention: [ClientCode]_[DocumentType]_[YYYY-MM-DD]. Here's our client code lookup:
- Acme Corp = ACM
- Baxter Industries = BAX
- Meridian Group = MER
- TechPrime = TPC

Rename all files to match the convention. Where you can't determine the client or document type, move the file to a /needs-review/ subfolder instead of guessing.

Personalised email generation

Here's a CSV of 45 customers whose contracts are up for renewal in March [upload file]. Columns: company name, contact name, contact email, contract value, renewal date, account manager.

For each customer, generate a personalised renewal reminder email. The email should:
- Address the contact by first name
- Reference their specific renewal date and current contract value
- Mention their account manager by name
- Suggest a 15-minute call to discuss renewal
- Tone: professional, warm, not pushy

Output all 45 emails in a single document, clearly separated, with the subject line and recipient email for each.

Claude generates 45 personalised emails — not mail-merged templates with [First Name] placeholders, but emails that read naturally. Review the batch, make any adjustments, and hand them to your team or paste them into your email tool.

Report reformatting

I have 12 monthly sales reports in /Documents/reports/ — one per month, all in different formats because three different people created them. Standardise all 12 to match this format:
- Page 1: Summary metrics (total revenue, units sold, average order value, top 5 products)
- Page 2: Revenue by region (table + bar chart description)
- Page 3: Notable trends and commentary
- Output each as a formatted Word document

Start with January so I can approve the format.

Data enrichment

Here's a CSV of 300 leads [upload]. Columns: company name, website, contact name, contact title. For each row, add:
- Industry (based on the company name and website)
- Estimated company size (small: 1-50, mid-market: 51-500, enterprise: 500+)
- Country (from the website domain)
- A lead quality score from 1-5 based on: our ICP is mid-market professional services companies in Australia

Output an enriched CSV with the original columns plus the new ones. Flag any rows where you had low confidence in the enrichment.

Quality audit

I have 30 customer proposals in /Documents/proposals/. Check each one against our quality standards:
- Does it include our standard terms and conditions section?
- Is the pricing formatted correctly (itemised, with subtotal and GST)?
- Does it reference the correct company name throughout (no copy-paste errors)?
- Is it under 10 pages?

Produce a checklist table: proposal file name, pass/fail for each criterion, and notes on any issues found. Sort by number of failures — worst first.

Start with one item, then scale. Never run a batch without first confirming the pattern on a single example. This is your quality gate — skipping it risks propagating the same error across hundreds of items.
Cowork usage adds up. Batch processing uses Cowork, which consumes more of your plan’s usage than standard chat. Processing 200 files will use meaningfully more quota than processing 20. Check your usage dashboard if you’re on a Pro plan with limited Cowork hours.
Keep backups of your original files. If Claude is renaming, reformatting, or modifying files in place, make a copy of the original folder first. This is standard practice but worth emphasising.
Very large batches may need chunking. If you have 500+ files, Claude may need to process them in batches of 50-100 to avoid context window limits or session timeouts. Claude will usually suggest this itself, but be prepared to split the work.
PDF extraction quality depends on the PDF. Scanned PDFs (images of documents) are harder than native PDFs (generated digitally). For scanned invoices, extraction accuracy drops. Consider using native PDF exports from your accounting or procurement tools where possible.
Review flagged items manually. When Claude flags items it’s uncertain about, that’s a feature, not a failure. Those flags are the items that would have been errors if Claude had guessed. Give them your attention.

File Organisation — organising and structuring files and folders
Document Creation — generating reports, proposals, and formatted documents
Data Analysis — analysing the data you’ve extracted from batch processing
Cowork — understanding the environment where batch processing runs

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