Assertion AI automates the full machine‑learning workflow by turning a data dictionary or schema into a ready‑to‑run Jupyter notebook containing production‑grade SQL and Python code. Users specify their prediction target, and the platform selects the appropriate modeling framework, generates transparent, editable code, and supports deployment via a web portal or API, eliminating manual feature engineering and extensive coding.
Funding
Funding not disclosed
Founders
Product
Problem
Developing production‑ready machine learning models typically requires extensive data preparation, manual feature engineering, and custom coding, which can be time‑consuming and demand specialized expertise. Organizations often struggle to translate their data schemas into accurate predictive models quickly, limiting the ability to act on insights.
Solution
Assertion AI automates the end‑to‑end ML workflow by converting a user‑provided data dictionary or schema directly into a complete, editable Jupyter notebook containing production‑grade SQL and Python code. Users specify the prediction target and modeling intent, and the platform automatically selects the appropriate modeling framework, identifies relevant variables, and generates code optimized for accuracy. The resulting notebook is transparent and interpretable, allowing teams to run, modify, and deploy models within their existing environments. Both a web portal and an API are available, enabling integration into diverse workflows without requiring raw data uploads.
Target Audience
Primary customers are data science, analytics, and engineering teams in enterprises that need to accelerate predictive model development without extensive coding effort.
Features
- Upload or import a data dictionary (YAML, warehouse metadata) defining tables, relationships, and column details
- Automatic interpretation of prediction goals (e.g., churn, sales forecast) and selection of suitable modeling techniques
- Generation of a full Jupyter notebook with production‑ready SQL queries and Python code, ready for execution and customization
- End‑to‑end pipeline from schema ingestion to model code, eliminating manual feature engineering and coding steps
- Accessible via an all‑in‑one web portal or programmatic API to fit different team workflows
- Transparent, interpretable code output that supports auditing and further development