Building a Scalable Digital Product
A consumer fintech startup had proved their product-market fit. Their lending platform was growing — but the architecture they’d shipped the MVP on wasn’t built to handle what came next. When user growth accelerated past projections, the platform began to buckle. Nematix was brought in to design and deliver a path to scale without a costly full rewrite.
The outcome: the platform scaled to 50,000 active users. Deployment time fell from four hours to under twenty minutes. Mean time to resolution dropped by 70%.
The Situation
The client had built their MVP on a monolithic Rails application deployed to a single cloud instance. For the first 500 users, it worked fine. When a partnership with a regional bank expanded their addressable market overnight, traffic spiked and the codebase couldn’t absorb it cleanly.
The engineering team was small — four developers — and had been moving fast with limited infrastructure investment. There was no CI/CD pipeline. Deployments were manual, nerve-wracking, and happened after hours to minimize user impact. There was no application performance monitoring. When something broke, the team found out from customer support.
The business needed to scale, but the team didn’t have the capacity to redesign the system while continuing to ship features. They needed outside expertise and a structured plan.
The Challenge
The core architectural problem was tight coupling. The application’s core functions — loan origination, user identity, repayment processing, and notifications — were all tangled together in a single codebase. A change to the loan engine touched the notification service. A bad deployment could take everything down at once.
Beyond architecture, the team faced three compounding constraints:
- No deployment safety net — no automated testing in the pipeline, no rollback strategy, no feature flags
- Zero observability — no way to trace a slow request, identify which component was failing, or understand the user impact of an incident
- Runway pressure — the startup had 14 months of runway and couldn’t afford a 6-month rebuild that produced nothing visible to customers
The solution had to be incremental, ship value along the way, and not require replacing the entire team’s tooling all at once.
Our Approach
Nematix began with a two-week technical assessment — reviewing the codebase, infrastructure, deployment history, and incident log. The goal was to understand the real bottlenecks, not just the obvious ones, before proposing any solution.
The assessment produced a prioritized remediation roadmap structured in three phases:
Phase 1 — Stability and observability (weeks 1–6) Before touching the architecture, we instrumented the application with Datadog APM, set up structured logging, and introduced a basic CI pipeline with test gates. This gave the team visibility they’d never had and stopped the bleeding from silent failures.
Phase 2 — Decompose the hot paths (weeks 7–18) We identified the two services under the most load — loan origination and repayment processing — and extracted them as independent services behind an API gateway. The remaining monolith continued to run unchanged. We used the Strangler Fig pattern: new traffic routed to the new services, old traffic still handled by the monolith until we were confident.
Phase 3 — Infrastructure and deployment pipeline (weeks 19–26) We migrated the platform to AWS ECS with auto-scaling policies, implemented blue-green deployments, and introduced feature flags via LaunchDarkly. The team could now deploy multiple times per day with confidence.
Outcome
Seven months after engagement start, the platform supported 50,000 active users — a 100× increase from when we started — with no major incidents attributable to architecture.
| Metric | Before | After |
|---|---|---|
| Active users | 500 | 50,000 |
| Deployment time | ~4 hours | ~18 minutes |
| Deployments per week | 1 (after hours) | 5–7 (daytime) |
| Mean time to resolution | ~3 hours | ~52 minutes |
| Uptime (trailing 90 days) | 97.2% | 99.91% |
The engineering team of four now ships faster than they did at one-tenth the user scale. The phased approach meant the business never had to pause feature development — new user-facing work continued throughout.
Key Takeaways
Observability before architecture. The instinct is to redesign the system. The right first move is to understand what the system is actually doing. Instrumentation revealed that 80% of latency issues came from two specific database queries — not the monolith structure itself.
The Strangler Fig works. Incrementally routing traffic to new services while keeping the monolith live meant zero big-bang risk. The team gained confidence with each extraction rather than betting everything on a cutover date.
Small teams can scale large systems. The constraint wasn’t headcount — it was process, tooling, and architecture. Four engineers with a proper pipeline and good observability outperform ten engineers deploying manually.
This case study is relevant to our Innovation & Product Development service. If your product is hitting similar growth constraints, get in touch.