What Atharva demonstrated

Built and verified an end-to-end modernization of a legacy fleet-service repo: fixed the wear calculation, handled missing service readings safely, repaired the nightly report, corrected a hidden mileage-conversion bug in helper code, and added the missing regression test. Also produced a data-driven breakdown-risk analysis that ranks cars by the factors the dataset actually supports, and documented the AI agent’s mistakes and the acceptance checks used to catch them.

• Leakage-Free Bug Diagnosis
• Directed AI Code Review
• Honest Acceptance Verification
• Data-Driven Risk Analysis