Legacy Codebase Stabilized: From 23 Monthly Bugs to Near Zero

The Situation

the platform had grown to 500,000 active users on the back of a fast-moving engineering team that prioritized feature velocity over code quality — a common and understandable trade-off at the growth stage. By the time they reached out to Kovil AI, the technical debt had compounded into a real business problem.

In the three months before engagement, they'd experienced 23 P1 production incidents, including two checkout outages during peak sales periods. Their small internal team was spending 60% of their time on bug triage instead of building.

The Challenge

The codebase had several structural problems that were generating recurring issues:

• No staging environment — all testing happened in production
• MongoDB queries without indexes on high-traffic collections, causing timeouts at scale
• A custom caching layer built 3 years ago that was inconsistently applied and frequently stale
• Zero automated testing coverage on the checkout flow
• No alerting — incidents were discovered by users, not the team

The most critical issue was the checkout service: a monolithic Node.js function handling order creation, inventory decrement, payment processing, and email confirmation all in a single try/catch. When any step failed, the behavior was unpredictable.

Our Approach

Kovil AI began with a thorough codebase audit in the first week — reading through the most critical services, running load tests, and mapping out the dependency graph. We prioritized fixes not by complexity but by blast radius: what was most likely to affect the most users if it failed.

The maintenance retainer structure meant we had both a reactive component (fix issues as they arise, within SLA) and a proactive component (systematic improvement over time). We didn't try to refactor everything at once — we worked methodically, starting with the pieces most likely to cause customer-facing incidents.

The Solution

In the first 30 days, we completed the highest-impact interventions:

• Monitoring and alerting: Deployed Datadog with custom dashboards for checkout funnel health, error rates, and response time percentiles. The team could now see problems before users did.
• Checkout service decomposition: Broke the monolithic checkout function into discrete steps with proper error boundaries. Payment failures no longer corrupted inventory state.
• Database indexing audit: Added indexes on 11 high-query collections. Average query time dropped 73%.
• Staging environment setup: Established a proper staging environment using anonymized production data. No more testing on live users.

Over the following 60 days, we added automated test coverage for all critical paths, refactored the caching layer to be consistent and predictable, and implemented a proper deployment pipeline with rollback capability.

Results

Within 30 days, monthly P1 bugs dropped from 23 to 8. Within 90 days, they were at 4 — a reduction of over 80%. Uptime improved from 97.2% to 99.9%, eliminating the checkout outages that had been costing the platform an estimated $15K per incident in lost revenue and customer support load.

The internal engineering team — now freed from constant fire-fighting — shipped their first major new feature in 4 months. They called it "the best investment we made this year."