Data Entry Automation
Somewhere in your company, a smart person is retyping PDFs into a spreadsheet. Modern AI reads documents, emails, and forms reliably enough to end that, if it is wrapped in proper validation. I build extraction pipelines that turn unstructured inputs into clean, structured data your systems can trust.
The real cost of copy-paste operations
- Invoices, delivery notes, and order confirmations are keyed in manually from PDFs.
- Data arrives by email and dies in inboxes instead of reaching systems.
- Typos in manual entry surface weeks later as accounting or inventory errors.
- Hiring plans include people whose actual job description is transcription.
What I build
Document extraction
PDFs, scans, and images are parsed by AI into structured fields with per-field confidence.
Inbox-to-system pipelines
Attachments and structured emails flow directly into your database, ERP, or accounting stack.
Validation gates
Business rules check every extracted record; only low-confidence items are queued for a human.
Audit trail
Every automated entry links back to its source document, so trust is verifiable, not assumed.
Common questions
How accurate is AI extraction?
High on well-designed pipelines, but I never rely on accuracy alone: validation rules and human-review queues catch the residual errors, which is what makes the system production-safe.
What about GDPR?
Pipelines are designed for European compliance: EU processing options, data minimization, and on-premise or self-hosted models where the data demands it.
What volumes make this worthwhile?
If someone spends more than a few hours weekly on repetitive entry, automation usually wins. The audit will tell you honestly if it does not.
See where your operations leak time
The audit takes a few minutes and tells me enough to map your highest-leverage automation.
Request a System Audit