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Built in-house at Automated & CO: a repeatable CSV/XLSX cleanup processor with an audit trail.
The workflow profiles a supplied file, applies a written rule set, checks the output in a separate verification pass, and packages the cleaned file, change log, exceptions and validation summary. It supports whitespace trimming, explicit value mappings, dates with a known convention, required-field checks and exact-row duplicate removal. Identifiers and leading zeros stay as text; conflicting keys and unknown values are flagged for review.
This is our own implementation, not a customer case study. The cover describes the service; it is not a before/after dataset or client-results claim. Complex workbooks, formulas, macros and fuzzy identity merging require a separate scope.
For an existing agency export, send column names, row/column count, required changes and deadline through Contra. We confirm eligibility and rules before funding. The published $99 package covers one flat table up to 5,000 rows and 20 columns, with up to five approved cleanup rules. AI tools assist where useful; outputs are checked against the original.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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I designed and built Cloudex Technologies as an AI-native company. It's staffed by 18 "Digital FTEs": AI agents with written roles, real tools and a reporting line. They sit in four departments: the Office of the CEO, Delivery, Inbound & Demand, and Outbound & Sales. They run on Paperclip with Claude Code as the main lane and Codex as an adversarial reviewer. The whole company is defined in a git repo (constitution, governance, per-agent instructions), so the runtime is a deploy, not a pet.
The rule it's built on: author ≠ verifier ≠ releaser. No agent approves its own work, every build passes five quality gates, and a human principal releases every outbound batch, which can be done by replying SEND or HOLD to an email digest. The sales department runs its own AutoGTM engine. It sources leads from the free Companies House register and researches each prospect's website, keeping only facts quoted word for word. It then drafts short, human emails and sends them within warm-up limits, with auto opt-outs and bounce stops. It runs privately on a VPS over a Tailscale mesh, with budget caps and a full audit trail.
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AutoML Forge is an end-to-end AutoML platform designed to simplify the machine learning workflow by automating key stages from data preparation to model evaluation.
The platform allows users to upload their datasets and build machine learning models without manually handling every step of the traditional ML pipeline.
Key Features
Automated data preprocessing and cleaning
Automatic feature handling and transformation
Classification and regression workflows
Anomaly detection
Model comparison and evaluation
Automated model selection
Hyperparameter optimization
Performance metrics and visualizations
Model prediction and inference
Easy-to-use interactive interface
Tech Stack
Python · PyCaret · FastAI · Scikit-learn · Pandas · NumPy · Streamlit
The project focuses on making machine learning more accessible while reducing repetitive experimentation and development time. It demonstrates practical experience in machine learning automation, model evaluation, data preprocessing, and building AI-powered applications.
Role: AI/ML Developer
Project Type: Machine Learning / AutoML / Data Science
Impressive to see automated data preprocessing and cleaning built into AutoML Forge - eliminating that tedious step speeds up experimentation dramatically. The integrated model comparison and evaluation visualizations make it easy to spot the best performer quickly.