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Use case

Data Entry Automation

People copying data from one screen into another is the purest form of operational waste — and the largest hidden line item in most admin-heavy P&Ls.

What can be automated in data entry automation? With current AI and integration tooling: extraction from documents, emails, and portals; field mapping and validation between systems; direct system-to-system integration where apis exist; exception queues for anything ambiguous. What should remain under human control: review of flagged mismatches; judgment calls on conflicting source data; approval of changes to master data. Olyra implements this with confidence scoring, audit logging, and human-review gates matched to risk.

The manual reality

What this work looks like today

Who does it

Everyone, a little: admin staff as a job, skilled staff as a tax on every task.

Typical systems

Any pair of systems that don't talk: email→ERP, PDF→CRM, portal→spreadsheet, spreadsheet→everything.

The cost

Measured before we touch anything: Hours of entry per week · Touches per transaction · Error rate downstream

What changes

What gets automated — and what stays human

Automated (with logging):

  • Extraction from documents, emails, and portals
  • Field mapping and validation between systems
  • Direct system-to-system integration where APIs exist
  • Exception queues for anything ambiguous

Stays under human control:

  • Review of flagged mismatches
  • Judgment calls on conflicting source data
  • Approval of changes to master data
Measurement & governance

How value and risk are managed

Measure

What we baseline and track

Hours of entry per week · Touches per transaction · Error rate downstream · Cycle time

Governance

Risks designed for

Garbage-in prevention via validation rules; segregation of duties preserved; every automated write logged with source.

Typical implementation path: Highest-volume entry path first; integration replaces extraction wherever a real API exists. Implementation follows the Olyra 120: pilot on real historical volume, parallel run, then measured cutover.

Find out what AI can actually do for your operations.

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