The Trap: You have a messy process—PDF invoices, email leads, or spreadsheet rows. You feel overwhelmed. You hire a VA for $500/mo to "clean it up."
The Problem: Humans are bad at being robots. They get tired. They make typos. They quit.
In 90% of cases, "Data Entry" is just a missing API integration. Here is the architecture to replace that role.
Level 1: Structure Your Inputs
Data entry happens because your inputs are unstructured (e.g., a free text email). The fix isn't to hire a reader; it's to force a structure.
- Replace Emails with Forms: Use Tally or Typeform to force standardized input.
- Replace PDFs with Portals: Don't accept PDF orders. Build a client portal (Softr/Stacker) where they enter the data directly into your database.
Level 2: The "Parser" Middleware
If you MUST accept messy data (like vendor invoices), use an LLM parser, not a human.
"Extract the following fields from this OCR text: {Invoice_Date, Total_Amount, Vendor_Name}.
Output ONLY raw JSON format."
Cost: $0.004 per invoice.
Speed: 2 seconds.
Accuracy: Higher than a human at 4pm on a Friday.
Level 3: The Validation Layer
The biggest fear is "What if the bot is wrong?"
Simple: Confidence Intervals.
- If the automation is 99% sure (perfect regex match) → Auto-Commit.
- If the automation is unsure (fuzzy match) → Send to Slack for Review.
Now your human only reviews the 5% of edge cases, not the 95% of boring work.
A script doesn't need health insurance.
I replace "Data Entry" teams with code. It's cheaper, faster, and infinitely scalable.