Eliminated 8 hours/week of data entry.
87% accuracy. Plan inventory 3 months out.
4 hours ā 15 minutes. Daily reports, zero manual work.
Cut query time from 45s to 1.2s. 97% faster.
Processes 10K events/hour. Real-time sync.
Sends 50K emails/day. Personalized at scale.
Prevented $3K/month in overages. Smart throttling.
Catches errors 3 hours before users notice.
Converts 1K files/hour. All formats handled.
Production systems solving real problems
Per week across all implementations
Weekly processes running without intervention
Production-ready automation and ML systems
These aren't vanity metrics. Each number represents actual business value delivered through automation and ML.
Looking for someone who can ship working code? Available for internships starting January 2026.
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Live examples of automation in action. Click any demo to run it.
# Invoice Parser - Automation Example
import pandas as pd
from pdf2text import extract
def process_invoice(pdf_path):
# Extract invoice data
text = extract(pdf_path)
# Parse key fields
data = {
'invoice_id': parse_field(text, 'Invoice #:'),
'amount': parse_field(text, 'Total:'),
'date': parse_field(text, 'Date:')
}
# Validate and save
if validate(data):
save_to_erp(data)
return {'status': 'success', 'data': data}
return {'status': 'error'}
# Process all pending invoices
invoices = get_pending_invoices()
for invoice in invoices:
result = process_invoice(invoice)
log_result(result)
2.3s
87%
1,523
Automatically extracts invoice data from PDFs, validates fields, and enters information into ERP system. Handles 200+ invoices weekly with 98% accuracy, eliminating 8 hours of manual data entry.