AI AUTOMATION FOR VERIFYING DATA ACCURACY
OVERVIEW
An ERP system relied on accurate records for a key object across thousands of accounts. The client migrated from a periodically synced legacy source to a real-time API but needed evidence that the new data surfaced in the application was more accurate. Fiduciary Tech QA engineers used AI-assisted automation to scale data comparisons dramatically, uncover defects quickly, and help drive accuracy from the low 90s to near-perfect levels.
THE CHALLENGE
For all accounts in the system, the object’s data had historically been synced from an external source on a regular cadence. The sync became stale whenever existing accounts were updated internally, new accounts were created between cycles, or workflow complications disrupted the sync altogether. After shifting to a real-time API-based provider, the client needed a statistically meaningful comparison between the legacy dataset and the new live values.
Initial QA validation relied on manual, field-by-field comparisons across two application views. Even a 20-account sample was slow, tedious, and error-prone—yet far too small to represent thousands of records. QA engineers expanded the manual sample to 40 accounts to capture more variation, which further increased effort and time.
More critically, because the new data was being delivered via real-time API calls, any inaccuracies or formatting inconsistencies within the provider would immediately appear in the application, creating risk that could not be detected through spot checks alone.
OUR SOLUTION
We built an automation tool using the client’s approved internal AI coding assistant to eliminate repetitive comparison work and validate the real-time provider at scale. The tool programmatically:
• Reads object fields and values from the legacy system view
• Reads the same fields from the real-time API view
• Compares values and flags mismatches
• Calculates match percentage (accuracy rate)
• Exports results to CSV for fast reporting and analysis
Running this across a broad, diverse data set enabled us to detect not only UI-level mismatches but also systemic issues within the real-time API provider itself—including missing elements, inconsistent formatting, and stale values that smaller samples would not have exposed. We logged these defects, collaborated with both the application and API provider teams, and reran automated validation repeatedly to confirm fixes.
KEY FEATURES
- • AI-assisted scripting to accelerate tool development and iteration
• Field-level comparison across all required attributes for every account
• Automated accuracy-percentage calculation
• CSV export for stakeholder reporting and defect analysis
GLOBAL IMPACT/RESULTS
- • Tool built in ~2 days by two engineers (≈8 points), compared to 1.5+ sprints estimated without AI/automation
• Sample size expanded from 20 planned accounts → 40 manual → 640 accounts via automation
• Data accuracy improved from ~92% to 99.5% through repeated runs and defect resolution
• In UAT and higher environments, targeted account comparisons were completed in ~30 minutes after each deployment, enabling true data-quality validation instead of superficial spot checks
TECHNOLOGIES & SERVICES
Client internal AI coding assistant (IDE-integrated) — rapid automation tool development
UI automation framework — scripted extraction and comparison
CSV reporting — automated export of comparison results
Real-time API service verification — validating live source data for accuracy
CONCLUSION
AI-assisted automation transformed a slow, error-prone QA task into a scalable data-quality verification engine. The client gained statistically meaningful insight, surfaced defects earlier, and confirmed that the real-time migration materially improved data quality and reliability—establishing a foundation for continuous monitoring as account volumes grow.