Overview

The Challenge: Transaction Latency Was Compromising Scale

As the fintech company’s platform grew, so did the demand for its database. Unfortunately, insert operations for critical payments began to slow down, affecting not just the financial layer but every system upstream. The real kicker? Their engineering team couldn’t access detailed database metrics due to permission constraints on RDS Performance Insights. With no clear line of sight into database internals, they were flying blind. That’s where we came in, not as external consultants, but as embedded team members ready to work shoulder-to-shoulder with their developers.

The Goal: Restore Performance and Preserve Business Logic

The mission was simple: identify and eliminate the source of latency without disrupting existing workflows or compromising data integrity. But doing that from inside an existing system, without metrics or admin access, required hands-on experimentation, deep SQL expertise, and trust.

Our Solution: Embedded Engineers, Lasting Results

Working From Within

We joined the client’s engineering team, directly pairing on feature work, joining standups, and writing code inside their repositories. This let us move quickly and align with their priorities in real time. Our first move was to build a Go-based load simulation tool that mimicked production transaction volume, enabling us to test insert performance under realistic conditions and reveal patterns that weren’t obvious from logs alone.

Refactoring the Right Way

After identifying several redundant and inefficient SQL triggers, we worked closely with the backend team to carefully refactor or remove them. Rather than offloading everything to the database, we shifted appropriate validations to the service layer, streamlining logic while maintaining compliance. We made sure every refactor passed functional and load tests before deployment, and collaborated on the rollout plan to minimize risk.

Leveling Up the Team

Beyond the fix, we helped implement internal processes and patterns for performance validation. Our custom Go ingestion tool remained in place for regression testing, and we helped define internal checklists for future trigger design.

Outcomes That Made a Difference

Insert Latency Cut by 90%

The platform could now process transactions at speed, even during peak periods. What was once a bottleneck became a non-issue.

Fewer Triggers, Cleaner Architecture

The most problematic parts of the system were replaced with simpler, more testable logic freeing up the database engine for what it does best.

Empowered Engineering Team

By working inside the team, not outside it, we ensured that the solution lived on past our engagement. Engineers now had the knowledge and tooling to prevent regressions and build forward.

Building for the Future

Staff augmentation at Ardan Labs is more than temporary help; it’s a strategic partnership. We help your team move faster and get stronger. For this fintech company, that meant:

• Production-grade fixes that restored performance
• Better backend patterns that supported scale
• Confidence to keep building without fear of hidden latency

Rethinking Staff Augmentation

Too often, staff augmentation means disconnected contractors or short-term fixes. We believe in embedding engineers who deliver production value, mentor teams, and leave systems and people better than we found them. This project proves that approach: a permanent performance improvement, delivered through shared effort.