Case Studies
Real results from real projects. Explore how we've helped companies transform their operations with AI and data solutions.
PSX Market Intelligence Platform Enhancement
Client: Pakistan Stock Exchange Analytics Client
Redesigned and enhanced the ML predictions interface of a PSX financial intelligence dashboard — transforming a single-company technical snapshot into a fully interactive, sector-wide analytics hub with real-time buy/sell/hold signals and a fault-tolerant CSV fallback architecture.
Signal Types
3
BUY / SELL / HOLD across all sectors
Architecture
Serverless
Cloudflare Workers + Supabase
Uptime
100%
CSV fallback on DB failure
Outcome: Analysts can now scan ML-driven buy/sell/hold recommendations for all PSX-listed companies simultaneously, filter by sector or signal, and rely on a fault-tolerant backend that stays operational even during database outages.
AI-Powered Roblox Market Intelligence Platform
Client: US-based Gaming Client
Built a full-stack analytics platform that tracks Roblox games, surfaces breakout opportunities, estimates revenue potential, and generates AI-powered market insights.
Search Modes
4
Name • URL • Place ID • Universe ID
Data Freshness
Daily
Automated pipeline updates
Core Insights
AI + Revenue
Breakout scoring, market intel
Outcome: The client gained a centralized dashboard to discover breakout Roblox games faster, monitor creator activity, and evaluate monetization opportunities in one place.
AutoMarkets Australia — Automotive Data & Image Pipeline
Client: Australian Automotive Marketplace
Built a full-stack automotive platform combining an automated image processing pipeline and a backend vehicle data system for AutoMarkets Australia, a specialty car marketplace serving 25,000+ collector vehicles.
Vehicles Indexed
25K+
Collector, exotic & auction cars
Pipeline Stages
4-Step
Scrape → Process → Store → Serve
Automation
100%
Zero manual image handling
Outcome: AutoMarkets Australia gained a scalable backend and image automation system, reducing manual listing work and powering a searchable specialty car marketplace with consistently formatted media.
Jemico Insights Hub — Dynamic Resource Platform
Client: Church & Ministry Leadership Consulting Client
Built a lightweight Next.js and Supabase resource platform that extends the existing Jemico Consulting website with a public insights page, admin review panel, content approval workflow, and Vercel deployment — without touching the live main site.
Deployment
Vercel
Subdomain — insights.jemicoconsulting.com
Workflow
4-Stage
Pending → Review → Approve → Publish
Content Types
6+
Articles, guides, ministry, leadership
Outcome: Jemico Consulting gained a dynamic, database-powered resource hub that extends their static website with curated leadership content — without rebuilding their existing Canva/GoDaddy site.
High/Low Forecasting Platform for 1-Second Trading Data
Client: EU-based Trading Analytics Client
Built a machine learning forecasting system that predicts daily highs and lows from first-3-day, 1-second market data using feature engineering, walk-forward validation, and specialized model variants.
Model Variants
12
4 tiers × high, low, both
Input Data
1-Second
First 3 trading days
Accuracy Goals
±15% / 95%
Forecast band and day-hit target
Outcome: The client received a production-ready forecasting workflow with engineered feature stores, optimized models, automated inference, and clear performance reporting for high/low prediction accuracy.
Real Estate Comparable Analysis & Valuation Dashboard
Client: UAE-based Real Estate Client
Built a structured valuation workbook that benchmarks The Acres against comparable UAE residential communities using transaction data, AED/sq ft analysis, market summaries, and regression-based pricing models.
Communities Analyzed
8+
Comparable UAE developments
Core Metric
AED / sq ft
Pricing benchmark analysis
Modeling Layer
Regression
Feature-based valuation support
Outcome: The client gained a centralized analytics workbook to compare communities, evaluate price positioning, and support property valuation decisions with structured market data.
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