AI Data Entry
Anything your team retypes from one place into another — extracted, validated and written into the system of record with an audit trail.
What it does
Somewhere in your company, a person reads from one screen and types into another. Every day. The pipeline replaces the retyping, not the person: documents, emails and chat messages get read, extracted, validated against your rules, and written into the system of record.
- Layout-independent extraction. OCR plus LLM reads any document like a person would — new supplier, new format, day one. No template library to maintain.
- Validation before writing. Sums must add up, references must exist, values must be in range. Failures queue for review with the data pre-filled.
- Writes, not exports. Records land in the ERP, CRM or database via API — no CSV purgatory in between.
- Full audit trail. Source, extraction, checks, reviewer — every record can answer "where did this number come from?"
How it runs at Oido
Data entry is the shared engine behind invoice processing and order processing: the same OCR service, extraction models and n8n workflows on the platform, pointed at whatever your team retypes. One pipeline pattern, many document types.
What to expect
The metric is the zero-touch rate — the share of records posted with no human involvement — and it climbs over the first months as validation rules absorb your documents' quirks. The full argument: AI data entry automation explained and the document processing guide.
Where it fits
Everywhere, honestly — but the heaviest wins are in accounting firms, logistics, insurance and corporate operations.
Count the retyping hours, then talk to us — or estimate with real cost numbers.
FAQ
What kinds of data entry can AI automate?
The retyping class: documents into the ERP, emails into the CRM, spreadsheets into the database, portal downloads into accounting. If a person reads from one screen and types into another, the pattern applies.
How accurate is it?
High on clean digital documents, lower on photos and scans — and the pipeline is built around that: validation rules catch inconsistencies, low-confidence extractions queue for review with fields pre-filled, and nothing posts silently below threshold.
How is this different from OCR software?
OCR turns pixels into text. The pipeline goes further: an LLM extracts meaning from that text regardless of layout, validation logic checks it against your business rules, and integrations write it where it belongs. No per-layout templates to maintain.
Does it keep an audit trail?
Every record traces back to its source document, extraction, validation result and reviewer (if any). That's a stronger trail than manual keying ever had.
What does it cost?
Single-workflow deployments typically run €5,000–€20,000 setup plus €200–€800/month. The payback math is simple: documents per month × minutes per document × loaded hourly cost.