AI-powered Data Cleaning Assistant

A single process to rule them all

"Tidy datasets are all alike, but every messy dataset is messy in its own way.” Hadley Wickham (cf. Leo Tolstoy)."

Messy data come in countless forms, so a one-size-fits-all rule set rarely works. Each dataset demands decisions that depend on its own context. A wholly deterministic system struggles with that variety. We therefore call on AI models for the subtasks that need domain knowledge or judgement.

More effortless cleaning is what we need

As the number of datasets grows, automating cleaning becomes ever more valuable. Yet several hurdles keep manual work high:

💰 High Labelling Cost - Spotting errors still requires many hours from data scientists, engineers, analysts, or subject-matter experts (SMEs).

😩 Low Enthusiasm - Data cleaning feels like grunt work. People prefer modelling, building pipelines, or answering business questions, so cleaning gets delayed or ignored until it turns critical.

🤷 SME Limitations - Experts know the domain but may lack SQL or coding skills. No-code tools help, but adoption is patchy and features such as version control are often missing.

🧠 Expertise Gap - Good cleaning is more than basic checks. Without training or interest, practitioners fix only obvious errors and miss subtler issues.

Despite this inherent challenges, advancements in the field of Large Language Models (LLMs) offer promising solutions for automating the identification of straightforward data issues and uncovering more intricate data quality problems.

Be my wingman

We are data practitioners too, and we are building an assistant that:

• Cleans your data automatically.
• Generates a profiling report so you see what changed and why. Food for Thought.
• Lets you keep full control over final fixes.

Spend less time fixing data and more time on the analysis that matters.