Service guides / AI agents

Data quality checks · AI agents

Document comparison: data quality checks

Agree what a valid source record looks like. Start with two approved document versions and plan a cited change summary.

Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.

Explore the service →Discuss this project

The project in view

Start with
two approved document versions.
Proposed result
a cited change summary.
A decision to resolve
formatting changes obscuring a substantive edit.

This is a planning example. It does not describe a completed client engagement.

Data quality checks for this project

  • Check two approved document versions for completeness, conflicting versions and consistent references.
  • Add a separate validation rule for formatting changes obscuring a substantive edit.
  • Keep unresolved records separate from a cited change summary; record the reason rather than silently filling a gap.

Questions to answer together

  • Which fields, references or measurements must be present?
  • How are duplicates, contradictions and missing values reported?
  • Which corrections require confirmation from the source owner?

What to have at the end

A validation checklist with examples of valid, rejected and unresolved records.

Record the decision on formatting changes obscuring a substantive edit alongside the source material. If it is still unresolved, identify who can answer it and what work depends on the answer.

Prepare your enquiry

Use this as a starting point. Add your current process, available examples and constraints before sharing.

Email Ian

Send your completed brief to hello@iansamonte.com or visit the main page to discuss your project.