CLIENT WORK & CASE STUDIES

Proven Engineering Results

Detailed technical breakdowns of AI automation, machine learning models, and high-scale applications delivered worldwide.

[CS-01]AI AutomationLogistics Enterprise — Germany

AI agent that processes 4,000+ freight invoices monthly with 97% accuracy — replacing a 3-person manual team

The Challenge:

A mid-sized freight forwarding company had three full-time operators manually extracting line-item charges from carrier PDFs in different languages and entering approved invoices into ERP.

The Solution:

We built a multi-stage AI agent pipeline: document classification, structured data extraction into validated JSON schema, rules engine comparison against PO records, and auto-approval workflows.

LangChainGPT-4oTesseract OCRAWS ECSPostgreSQL
Quantifiable Results:
  • ✓97.3% straight-through processing rate — only 2.7% require human review
  • ✓Processing time reduced from 10 minutes to under 40 seconds per invoice
  • ✓3-person operator team redeployed to exception management
  • ✓Handles 10× volume spikes with zero added headcount
Key Outcome:
3-Person Team Redeployed
[CS-02]Machine LearningE-commerce Marketplace — India

Product classification ML model that auto-tags 95% of new SKUs — cutting manual turnaround from 3 days to 4 hours

The Challenge:

An e-commerce marketplace onboarding 1,000+ products weekly required manual taxonomy categorization, causing 4-day delays and low search relevance.

The Solution:

We built a multi-label text classification model fine-tuned on 140,000 historical product records, featuring multi-task prediction heads and confidence-scored human review queues.

PyTorchHugging FaceFastAPIPostgreSQLMLflow
Quantifiable Results:
  • ✓95.1% auto-tagging accuracy across 6-level taxonomy
  • ✓87% of products live without human review
  • ✓Product launch turnaround reduced from 4 days to 4 hours
  • ✓+34% increase in search conversion relevance
Key Outcome:
3x Product Growth, Zero Added Headcount
[CS-03]Web DevelopmentB2B SaaS — United Kingdom

Next.js 16 full SaaS rebuild — 100/100 Lighthouse score and 67% organic traffic increase

The Challenge:

A UK B2B SaaS platform suffered from an 11-second page load time, lack of SSR, and a fragile deployment pipeline.

The Solution:

Complete rebuild using Next.js 16 App Router, React Server Components, clean component library, Prisma + PostgreSQL, and automated CI/CD.

Next.js 16React Server ComponentsPrismaSupabaseGitHub Actions
Quantifiable Results:
  • ✓Lighthouse Performance score: 34 → 98
  • ✓Initial page load: 11.2s → 1.4s LCP
  • ✓+67% organic search traffic in 90 days
  • ✓Zero downtime production releases
Key Outcome:
+67% Traffic, 0 Incidents
[CS-04]Mobile AppsField Services — Australia

Offline-first React Native app for 120 technicians — eliminating paper job sheets

The Challenge:

Facilities management company relied on paper job sheets and physical signatures, causing massive administrative delays.

The Solution:

Cross-platform offline-first React Native application using SQLite with local timestamp sync, digital touchscreen signatures, and real-time backend updates.

React NativeSQLiteNode.jsPostgreSQL
Quantifiable Results:
  • ✓8 hours of daily manual entry eliminated
  • ✓Same-day invoice generation from job completion data
  • ✓98% photo capture compliance rate
  • ✓4.9★ technician App Store rating
Key Outcome:
8 Hours/Day Saved

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