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You will get a custom, repeatable business workflow automated using Python, APIs, and AI
Project details
You will get a custom, repeatable business workflow automated using Python, APIs, browser automation, and AI-assisted reporting.
This project is designed for clearly defined manual processes such as data collection, API pulls, website research, CSV cleaning, report generation, lead research workflows, or internal business summaries.
Depending on the package, I can deliver source code, setup files, structured outputs, README instructions, AI-assisted summaries, and handover notes. Higher tiers can include browser automation, multiple input sources, Docker/cloud-ready setup, and more detailed documentation.
My focus is practical automation, not vague AI demos. The final workflow is built to be reviewable, reusable, and understandable after delivery.
This project is designed for clearly defined manual processes such as data collection, API pulls, website research, CSV cleaning, report generation, lead research workflows, or internal business summaries.
Depending on the package, I can deliver source code, setup files, structured outputs, README instructions, AI-assisted summaries, and handover notes. Higher tiers can include browser automation, multiple input sources, Docker/cloud-ready setup, and more detailed documentation.
My focus is practical automation, not vague AI demos. The final workflow is built to be reviewable, reusable, and understandable after delivery.
AI Algorithms
Autoencoder, Large Language Model, Multimodal Large Language Model, Regression Analysis, Transformer ModelAI Applications
AI Content Creation, AI-Enhanced Classification, AI-Generated Code, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Synthetic Data Generation, Text Recognition, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$200
|
Standard
$550
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$75 - $300
Additional Revision
+$50
Extra data/API source
(+ 1 Day)
+$125
Cloud deployment setup
(+ 3 Days)
+$350Frequently asked questions
About Stefan
AI Automation & Cloud Data Engineer | Python, AWS, APIs
Melbourne, Australia - 1:48 am local time
I’m strongest when a workflow involves messy data, multiple APIs, recurring reports, or a process that needs to run reliably in the cloud.
Common projects I can help with:
- API integrations, webhooks, and two-way data syncs
- Python ETL/data pipelines from APIs, CSVs, databases, and spreadsheets
- AWS/cloud automation for scheduled jobs, storage, API backends, and deployment
- n8n, Make, Zapier, Google Sheets, Airtable, HubSpot/CRM, Shopify, Slack, and internal workflow automation
- Reporting automation for ecommerce, operations, finance, and sales teams
- Browser automation and public data extraction
- AI-assisted research, enrichment, routing, and report generation
- FastAPI, Docker, and cloud deployment when the workflow needs a backend
My process is simple: map the manual workflow, identify the inputs and failure points, build the smallest reliable working version, test it with real/sample data, then deploy or hand it over with clear notes so your team can rerun or maintain it.
I work best on projects with a concrete output: a working automation, a clean dataset, a recurring report, a tested API connection, a deployed internal tool, or a documented workflow your team can use.
Steps for completing your project
After purchasing the project, send requirements so Stefan can start the project.
Delivery time starts when Stefan receives requirements from you.
Stefan works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and confirm scope
I review your workflow, inputs, outputs, access needs, and success criteria before starting the build.
Build the automation workflow
I create the Python/API/browser automation logic and structure the workflow around your required inputs and outputs.