FinTech

FinTrack Pro

How Albos rebuilt a legacy trading analytics stack into a cloud-native platform handling millions of daily transactions.

40% faster load timekey outcome
202410 months14 engineersFinTrack Pro (Series B FinTech)
FinTrack Pro — FinTech case study
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The challenge

The client's monolithic dashboard could not keep pace with intraday trading volume. Page loads exceeded 8 seconds, on-call incidents were frequent, and product teams could not ship features without risking production stability.

Our solution

We designed an event-driven microservices architecture on AWS, introduced read-optimized PostgreSQL replicas, and rebuilt the front end in React with aggressive code-splitting and edge caching. Observability (metrics, traces, SLO dashboards) was implemented from day one.

40%

Load time improvement

10M+

Daily transactions

<100ms

P99 API latency

-62%

Production incidents

ReactNode.jsPostgreSQLAWS

Overview

FinTrack Pro needed a platform that could scale with trading volume without sacrificing analyst experience. Albos Technologies Pvt Ltd partnered with their product and infrastructure teams to deliver a full rebuild.

The challenge

  • Legacy monolith with tightly coupled reporting modules
  • No clear ownership boundaries between teams
  • Slow release cycles and high regression risk
  • Growing data volume from real-time market feeds

Our approach

Architecture

We decomposed the system into domain-aligned services: ingestion, normalization, analytics, alerting, and admin. Kafka streams decouple peak load from user-facing APIs.

Frontend performance

The React application uses route-based splitting, virtualized data grids, and memoized chart layers. Critical dashboards load under two seconds on median hardware.

Reliability

Blue/green deployments, automated rollback, and error budgets per service gave leadership confidence to accelerate the roadmap.

Results

Within three months of launch, FinTrack Pro reported materially faster dashboards, fewer overnight incidents, and a 40% improvement in median load time across top workflows.

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