Initiative #152
Data Validation Tool
Listed in the registry Pilot Original language : English
About the initiative
Description of the initiative
Use large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose corrections and normalize data into a standardized format.
AI family
Automation and Decision Support
Bucket rationale Not translated
Automatisation de la validation et de la correction de données structurées.
Lifecycle
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Ideation
Stage status : Completed
June 1, 2025
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Assessment
Stage status : Completed
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Approved
Stage status : Completed
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Development
Stage status : Completed
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Pilot
Stage status : In progress
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Production
Stage status : Upcoming
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Archived
Stage status : Upcoming
Schedule for this initiative
- Completed
- In progress
- Planned
- Not applicable
- Today
- Ideation
A similar need?
Nobody has come forward yet. If your team faces the same problem, say so: it helps bring initiatives together and share a solution.
Contacts
- Requester
- Smith, Melannie
- Sector contact
- Smith, Melannie
- Subject matter expert
- Winegardner, Amanda
- Smith, Melannie
Data and tools
- Sector
- Ecosystems and Oceans Science
- Region
- National Capital Region
- Could this initiative be shared with the Treasury Board Secretariat?
- Yes
- Submitted on
- June 1, 2025
Primary users
- Departmental employees
Government priorities and declaration
Declared to TBS Recorded as transmitted to TBS; not editable here.
- Declared name of the AI system
- Data Validation Tool (See B6, B22)
- TBS registry identifier
- 2526-DFO-MPO-013
- Status declared to TBS
- In development
- Sent to TBS on
- October 10, 2025
- Purpose of the system, as declared
- Problem: Digitizing handwritten or non-machine-readable documents using optical character recognition (OCR) often introduces transcription errors, which can compromise data quality and usability. These inaccuracies make it difficult to rely on the extracted information for analysis or integration into standardized systems. Objective for AI: Leverage large language models to detect and correct OCR-induced errors by applying intelligent validation rules. The goal is to propose accurate corrections and normalize the extracted data into a consistent, standardized format, ensuring higher reliability and interoperability across systems.