How to automate

How to Automate Data Entry

Data entry is the universal time-sink. Every business has someone re-typing values from one place to another — emails to CRM, PDFs to ERP, spreadsheets to billing. Here's how to make that re-typing disappear.

Common challenges

Hours per week typing the same data into multiple systems

Errors and inconsistencies between systems

No audit trail of what was typed where

Workflow stalls when the typist is on leave

Concrete steps

  1. 01

    Map every place data enters your business

    Inbound emails, web forms, PDFs, supplier portals, paper, phone calls. List the top 5 sources and the systems they end up in.

  2. 02

    Centralize the inbox

    All inbound data lands in one place — dedicated email, form, or upload portal. From there it can be processed uniformly.

  3. 03

    Extract with AI

    LLMs read structured and unstructured input (PDF, image, free text email) and output the fields you need: name, amount, date, line items. 95-99% accuracy on typical formats.

  4. 04

    Validate against rules

    Before pushing to your system: required fields present? Reasonable values? Match existing records? Auto-flag exceptions.

  5. 05

    Push to systems via API

    Validated data lands in CRM, ERP, billing — automatically. Audit trail kept of what was extracted, validated, and where it went.

  6. 06

    Human-in-loop for edge cases

    Confidence below threshold or validation failure → human reviews. Everything else flows.

Common tools

OpenAI GPT-4 Vision, Claude, Gemini for extractionGoogle Document AI, Azure Form Recognizer for specialized formsCustom validation rules in codeDirect API integration with your CRM/ERP

When to build custom

Build custom when you process 500+ data inputs per month, have non-standard input formats, or need cross-system validation. For low volume + standard CRM intake, off-the-shelf form-to-CRM tools suffice.

Frequently asked questions

Will AI hallucinate data? +

Configured properly, no. We use extraction prompts that ground the model in the source document and require explicit citation. Anything not extractable from the input is left as null — never invented.

How accurate is data extraction? +

For typical structured documents: 95-99%. For handwritten or poor-quality scans: 85-92%. Confidence scores attached to every field so you know what to trust.

What about data from phone calls? +

We can transcribe and extract from call recordings. Speaker diarization included. Depends on call quality and language.

Can it handle multiple languages? +

Yes. Modern LLMs handle major languages natively. We test on real samples in your languages before launch.

What's the ROI? +

Typical: 70-90% time reduction on the targeted process. ROI depends on volume and the cost of the person currently doing it.

Related case study /#/case/maritime-bill-of-lading-reconciliation →

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