FinTech · Case Study 01

How We Built a Full FinTech
Trading Platform in 6 Weeks

A fintech startup needed a complete CFD trading platform with real-time price feeds, margin calculations, and an admin dashboard. Competitors quoted 6+ months and $150k+. We delivered in 6 weeks, under budget.

Get Results Like These Read the case study
Delivered under budget AI scaffolded 70% of boilerplate 1,200+ edge cases tested
6 wksFrom Kickoff to Live
$1.2MTrading Vol Month 1
4XFaster Than Competitors
99.98%Platform Uptime
Built withReactTypeScriptNode.jsWebSocketsPostgreSQLRedisAWSDocker
99.98%Platform uptime
$1.2MTrading volume, month 1
1,200+Edge cases tested
01 The Challenge

The Problem

Multiple dev shops quoted $150k+ and 6-month timelines. The fintech startup had zero trading volume but needed a fully-integrated platform yesterday: real-time price feeds via WebSockets, margin calculations with live risk engine, and an admin dashboard to monitor positions. Traditional dev teams were quoting timelines measured in months. The startup was bleeding runway.

Before$150k+Quoted by multiple dev shops
Before6+ monthsQuoted timelines
BeforeZeroTrading volume at the start
02 Our Approach

AI + Human
Strategy

AI handled
  • API structure
  • Database schemas
  • Test suites
Humans owned
  • Trading logic
  • Risk algorithms
  • Margin engine

AI scaffolded 70% of the boilerplate. While traditional teams waste weeks on repetitive API structure, database schemas, and testing frameworks, we let AI generate proven patterns in hours. Senior engineers focused only on the 30% that matters: critical trading logic, risk algorithms, and margin engine precision. QA teams used AI-generated test suites covering 1,200+ edge cases around order execution, position updates, and margin calls.

70%Boilerplate scaffolded by AI
30%Senior engineers on trading logic
1,200+Edge cases in AI-generated tests
03 The Process

5 Phases of Execution

Phase 01

Discovery

AI + Human Review

Analyzed trading workflows, competitor platforms, and regulatory requirements. AI mapped the tech architecture; humans validated against fintech best practices.

Phase 02

Architecture

System Design

Designed real-time WebSocket infrastructure, PostgreSQL schemas for positions/orders, Redis caching for price feeds, AWS deployment with auto-scaling.

Phase 03

AI-Scaffolded Build

Machine Speed

Generated REST APIs, WebSocket handlers, database migrations, and front-end components. AI produced 10,000+ lines of clean, tested code.

Phase 04

Human QA & Logic

Expert Review

Senior traders verified margin engine. Engineers tested order execution under high load. Full integration testing against live market data feeds.

Phase 05

Launch & Monitor

Go Live

Deployed to production with live monitoring. 24/7 ops team on standby. Real-time performance dashboards and incident response ready.

04 The Results

Live in 6 Weeks,
Not 6 Months

From zero trading volume to a production platform with live price feeds and margin calculations.

6 wksFrom Kickoff to Live

Competitors quoted 6+ months.

$1.2MTrading Vol Month 1

Starting from zero trading volume.

4XFaster Than Competitors

AI scaffolded 70% of the boilerplate.

99.98%Platform Uptime

24/7 ops team on standby.

“Tyvonix didn't just build us a platform — they rebuilt our entire operation. The trading engine handles live price feeds and margin calculations flawlessly. We went live in 6 weeks instead of 6 months.”
Marcus R.CEO Capital
05 Technical Stack

Built with Modern Tools

Frontend

ReactTypeScriptTailwindCSSWebSockets

Backend

Node.jsExpressWebSocketsJWT Auth

Data & Cache

PostgreSQLRedis

Infrastructure

AWSDockerCI/CD
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