Selected work
CASE STUDY / AI Systems

Racing Challenge & AI Pricing Platform

OSYSTIC engineered a robust racing challenge platform featuring a Next.js frontend, FastAPI backend, and automated data processing workflows. The solution integrates scheduled updates, result-integrity checks, and Monte Carlo pricing logic.

Next.js 14ReactFastAPIPython
“Very good would recommend.”
Mitchell · Australia
Client feedback
Concept illustration
01 / CONTEXT

The challenge

A racing challenge platform required coordination of scheduled race-data processing, participant and result management, challenge scoring, and an operational browser interface while maintaining data consistency and calculated pricing across recurring updates.

02 / ENGINEERING

What OSYSTIC built

OSYSTIC delivered a separated architecture utilizing a Next.js 14 and React frontend paired with a FastAPI backend. The solution incorporates SQLAlchemy persistence, data-ingestion modules, scheduled background processing for result and price refreshes, and Monte Carlo pricing logic.

Engineering Highlights

  • Separated browser presentation from backend domain and persistence logic.
  • Added recurring jobs for result refresh, price refresh, meeting-state updates, missed-result recovery and integrity verification.
  • Retained explicit result-integrity and recovery utilities for incomplete external feeds.
  • Implemented Monte Carlo pricing as a defined backend calculation path.
  • Preserved operational backup and recovery documentation alongside application source.

Capabilities

  • Full-Stack Development
  • Data Ingestion & Processing
  • Automated Scheduling
  • Algorithmic Pricing
  • Result Integrity Management

Public Evidence Boundary

This repository contains sanitized public proof only. The confidential delivery/source repository remains private. Implementation source, client identity, credentials, private infrastructure, payment/contract details, private conversations, and protected raw evidence are intentionally excluded.

03 / DELIVERY

Outcomes and validation

  • Successfully validated maintained frontend build and backend Python baseline via repository CI.
  • Delivered structured scheduling, result-integrity controls, and Monte Carlo pricing code.
CLIENT PERSPECTIVEPROJECT-LINKED CLIENT FEEDBACK
“Very good would recommend.”
MitchellAI Pricing / Analytics · Australia
Racing Challenge & AI Pricing Platform
Project-linked client feedback
OSYSTIC / PROJECT CHANNELOPEN
START A PROJECT

Have a similar challenge?

Tell us what you need to build, the constraints, and where your team needs engineering support.

Send a Technical Brief