AI Case Studies in Practice: Remediating Post-Migration Data Inconsistency in Production

No items found.

In real projects, engineering problems rarely come in a clean or predictable form. They appear late, affect multiple systems and often put delivery under pressure. AI can help in these situations, but not on its own.

You might also enjoy

Business Context

After a high-risk database migration, production traffic was already running on the migrated instance when a severe inconsistency was discovered. Around 5 percent of all queries were failing, creating major business and operational risk.

Situation

The migrated database contained hundreds of tables and approximately 2 TB of data. A rollback was highly complicated because new production data had already been written.

Why it was hard

Critical incident under live production load
Immediate need to remediate about 4,000 urgent records
Total inconsistency scope near 500,000,000 records
Escalation pressure increased every hour

How we used AI

We assembled full context (logs, product documentation, and source code) and used AI to localize the root problem.

Root cause area identified in about 20 minutes
AI-generated hotfix ideas used for rapid triage
AI-generated remediation scripts validated in test environments
Three remediation strategies compared: documentation path, hotfix-derived path, and configuration-based bypass

Outcome

Fast root-cause localization dramatically shortened diagnosis time
Documentation-only approach failed and caused a short outage
AI script for full production scope was too risky and slow
Final fix implemented manually by experts and production stabilized in about 30 additional minutes

Delivery Impact - What this means for software delivery

AI provided substantial acceleration in diagnosis and option exploration, but final incident resolution still depended on expert decision making. This case demonstrates both the value and limits of AI in high-stakes production recovery.

What made the difference

Complete technical context
Testing predefined hypotheses against supporting and contradicting evidence
Strict validation before production execution
Willingness to override unsafe AI output
Senior engineering expertise

AI accelerated understanding, but safe execution required human accountability and deep domain knowledge.

Explore More AI Use Cases

Each scenario reflects real-world pressure points where applied AI delivers measurable impact across engineering workflows.

Case 1: Handling a Late Architecture Change Before Release
Case 2: When Secure Communication Breaks. Resolving a Critical Java Issue
Case 3: Delivering a Complex Software Analysis Under Severe Time Constraints
Case 4: Understanding and Optimizing a Legacy System Without Documentation
Case 5: Evaluation of a Delayed Modernization Program
Case 6: Remediating Post-Migration Data Inconsistency in Production
Case 7: Delivery Based on an AI-Assisted Specification

Written by

No items found.

Read more insights

AI Adoption Is a Business Transformation. It Starts Where Deployment Ends

The real challenge of enterprise AI starts after the technology works. The next step is turning individual gains into a new operating model, with different ownership, processes, management practices and governance. Roman Marek, AI Architect at Trask, explains what separates experimentation from real adoption, and why the ultimate test is whether the organization starts working differently.

Faster Code, Slower Fixes: 3 Security Gaps AI Is Exposing

AI is changing both sides of software security. It helps adversaries find vulnerabilities faster, but it can also shorten the path from finding a problem to fixing it. It also exposes another issue: many companies can already find security problems. The harder part is deciding which ones matter, fixing them fast enough and getting those fixes safely into production without slowing down the business.

Case Study: From 16 hours to 30 Minutes – Trask AI Agent Helps Speed Up MONETA Bank's Payment Card Disputes

Modern banks face increasing pressure to resolve payment card disputes quickly, accurately, and in full compliance with Payment Services Directive 2 (PSD2). MONETA Bank, a leading CEE bank (formerly GE Money Bank), can now automate most of this process with an agentic AI solution co-developed with Trask – reducing handling time from ~16 hours to ~30 minutes.

Back to all insights

We’re hiring.

Join us at