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Proof of work

Four builds. Different worlds, same standard.

Enterprise and federal engagements are shown with real metrics and anonymized clients, per engagement agreements. EnChaptered is our own product — built, shipped, and public.

Web UI & REST API · 4 months · Federal agency

Rebuilding a regression suite the team had learned to route around

  • Rebuilt the framework on DRY principles — class count cut 70%
  • Centralized config management across environments
  • Automated smoke & regression gates on build and UAT pipelines
  • GitLab Runners + Docker for parallel execution
  • Automated reporting integrated directly into CI/CD
96.5%
Pass rate (from 45–50%)
65 min
Full run (from 10+ hrs)
650+
Test cases (from 350)
2×/mo
Releases (from monthly)
"This overhaul turned our flaky regression suite into a robust quality gate integrated into modern DevOps pipelines." — Automation Architect, federal agency engagement
ETL Validation · Big Data QA · 3 months · Federal healthcare data program

Turning a five-day manual regression cycle into an automated afternoon

  • Built a custom PySpark + AWS EMR validation framework from scratch
  • Parameterized SQL and YAML-config-driven test scenarios
  • Automated EMR cluster creation and auto-shutdown
  • Automated DataFrame comparisons — exact row/column mismatch detection
  • Centralized reporting with full audit trail
24×
Faster regression (6 days → 6 hrs)
350+
Test cases (from 128)
40+
Scenarios (from 26)
16M+
Rows automated
"We built the first bridge between traditional QA and Big Data validation — enabling scalable, automated quality assurance for massive datasets." — Lead QA Consultant, federal healthcare data program
Salesforce Lightning + Experience Cloud · 4 months (ongoing) · Enterprise program

VisionQA: an AI-powered generator that stops broken automation before it ships

  • IR architecture — a single JSON source of truth, edit once, propagates everywhere
  • Rex validator: 16 semantic rules, blocks generation in CI on failure
  • Auto-detects hardcoded record IDs before they break in staging
  • Locator healing re-ranks fallbacks after a platform release, no code touched
  • Six documented interaction patterns, reusable across every app
<5 min
Framework generation
16
Validation rules enforced in CI
0
Manual edits for locator healing
6
Patterns documented & reusable
"What used to take two weeks to patch after a platform release now takes an afternoon. The CI gate catches what our code reviews were missing." — Lead SDET, enterprise Salesforce engagement
Consumer AI Product · Next.js + Gemini API · Public project

EnChaptered — an AI storybook generator, built and shipped solo

  • Full-stack Next.js app: landing page, live demo, waitlist capture
  • Gemini API generates a personalized, age-appropriate story from a child's name and interests
  • Resend email pipeline for parent and internal notifications
  • Mobile-first build — most traffic is parents on their phones
  • Deployed on Vercel and iterated against real signups
<10s
Time to a generated story
Solo
Design → API → deploy, one builder
2–8
Target age range
Live
Shipped on Vercel, real users
github.com/ferkat0316/EnChaptered

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