1. Business Process Automation

Project: Automated Excel & Email Reporting System

Client Problem:
A sales team spent 2 hours every morning exporting data from their CRM, cleaning it in Excel, and emailing daily summaries.

Solution:

• Wrote a Python script using pandas and openpyxl to clean and format CRM exports automatically.

• Used smtplib to email reports as Excel attachments every morning at 8 AM.

• Scheduled the automation with cron (Linux) / Task Scheduler (Windows).

Results:

• Saved ~10 hours/week.

• Reports delivered 100% on time with zero human input.

Tech Stack: pandas, openpyxl, smtplib, schedule, dotenv

3. API Integration & Automation

Project: CRM & Payment Gateway Integration

Client Problem:
Customer data was scattered across Stripe (payments) and HubSpot (CRM). No unified record.

Solution:

• Used requests to connect to both APIs.

• Created a Python automation that fetches new Stripe transactions daily and updates HubSpot contacts automatically.

• Logged all actions in a Google Sheet for transparency.

Results:

• Unified customer view saved 2+ hours of admin time daily.

• No more duplicate data entries or missed follow-ups.

Tech Stack: requests, HubSpot API, Stripe API, gspread, logging

5. Data & Reporting Automation

Project: Financial KPI Dashboard

Client Problem:
Finance department manually consolidated data from multiple SQL tables every month.

Solution:

• Built Python ETL pipeline to query data, clean it, and push results to Power BI.

• Set up daily automation for updating metrics (cash flow, P&L, capital ratio).

• Added auto-generated summary emails with visual attachments.

Results:

• Monthly close reports now generated daily.

• Management saved ~20 hours/month.

Tech Stack: pyodbc, pandas, Power BI API, smtplib, cron

7. Finance & Accounting Automation

Project: Automated Sensitivity Model

Client Problem:
Finance team manually ran 60 versions of an interest rate sensitivity model monthly.

Solution:

• Automated the runs with a Python loop controlling model parameters.

• Exported results into Excel summary sheets and Power BI dashboards.

• Logged results to SQL database for historical tracking.

Results:

• Cut processing time from 6 hours to 20 minutes.

• Allowed real-time scenario testing.

Tech Stack: pandas, SQLAlchemy, openpyxl, schedule

9. Computer Vision & OCR Automation

Project: Invoice Data Extractor

Client Problem:
Accounting staff manually typed invoice data from PDFs into spreadsheets.

Solution:

• Used Tesseract OCR + OpenCV to extract vendor name, date, and amount.

• Cleaned text and matched vendors automatically.

• Exported data into QuickBooks via API.

Results:

• Processed 100 invoices in under 5 minutes.

• Eliminated human data-entry errors.

Tech Stack: opencv-python, pytesseract, pandas, QuickBooks API

11. Automation Audit & Consulting

Project: End-to-End Automation Assessment

Client Problem:
Startup wanted to identify inefficiencies in their back-office operations.

Solution:

• Conducted automation audit of 12 workflows.

• Provided a ranked automation roadmap with estimated time savings and ROI.

Results:

• Identified $18k/year in potential labor savings.

• Signed ongoing automation retainer.

Deliverables:
Audit Report (PDF), Automation Priority Matrix, Sample Scripts

2. Web Scraping & Data Extraction

Project: Competitor Price Tracker

Client Problem:
An e-commerce business needed to monitor 15 competitors’ prices daily.

Solution:

• Built a Python scraper using requests, BeautifulSoup, and lxml to fetch prices and availability.

• Exported data to Google Sheets using the Google Sheets API.

• Added matplotlib visualizations showing price trends.

Results:

• Client optimized pricing strategy, increasing sales margin by 7%.

• No manual checking required.

Tech Stack: BeautifulSoup, requests, gspread, matplotlib, time

4. Email & Notification Bots

Project: Stock Market Alert Bot

Client Problem:
Investor wanted instant alerts when specific stocks hit target prices.

Solution:

• Used yfinance to pull real-time prices every minute.

• Added logic for threshold alerts (price < buy_target or > sell_target).

• Sent text messages using Twilio SMS API.

Results:

• Automated trade alert system — zero manual checking.

• Made 3 profitable trades in first week based on bot alerts.

Tech Stack: yfinance, Twilio, schedule, dotenv

6. AI Chatbot & NLP Automation

Project: FAQ Chatbot for Local Business

Client Problem:
A Charlotte-based business was overwhelmed by customer inquiries (hours, pricing, etc.).

Solution:

• Used OpenAI GPT-4 API + Flask to create a chatbot trained on the company’s FAQ data.

• Integrated with website and WhatsApp using Twilio.

• Added fallback logic for complex queries → send to staff email.

Results:

• Reduced repetitive inquiries by 80%.

• Increased conversions by 20%.

Tech Stack: Flask, OpenAI API, Twilio, HTML/CSS

8. AI Text & Document Processing

Project: Document Summarizer & Classifier

Client Problem:
Client needed to process hundreds of legal documents weekly.

Solution:

• Built a Python pipeline that reads PDFs, extracts text with PyPDF2, and uses GPT-4 API to summarize and tag key clauses.

• Results stored in a searchable SQLite database.

Results:

• 10x faster document review cycle.

• Consistent and accurate summaries.

Tech Stack: PyPDF2, OpenAI API, sqlite3, pandas

10. Workflow Integration Bots

Project: Slack + Google Drive Workflow Bot

Client Problem:
Marketing team needed to upload files to Google Drive and notify everyone on Slack manually.

Solution:

• Created a Python bot using Slack API and Google Drive API that automatically uploads campaign files and sends formatted Slack messages.

Results:

• Reduced update lag from 2 hours to instant.

• Improved communication flow.

Tech Stack: slack_sdk, google-api-python-client, dotenv