Obviously AI

What is Obviously AI?

Obviously AI is a user-friendly platform that simplifies data science. It offers a one-click solution for building machine learning models, understanding their outputs, and making predictions — all without writing code. It includes tools for preparing data, training models, and monitoring performance. Its standout feature is the ability to explain the insights models uncover in plain terms, making them accessible to anyone.

Who created Obviously AI?

Obviously AI was co-founded by Nirman Dave and Tapojit Debnath. It is a B2B startup focused on predictive analytics, based in San Francisco, CA. Its goal is to give non-technical business analysts the ability to quickly run predictions on historical data, leading to better business decisions.

What is Obviously AI used for?

The platform covers the core predictive analytics workflows:

• Classification: Predicting categories such as customer churn, lead conversion, loan repayment likelihood, and fraud detection.
• Regression: Predicting numerical values like sales figures, dynamic pricing, crop yield, deal size, and costs.
• Time Series Prediction: Forecasting values over time from historical, time-stamped data — e.g., sales over the next six months or a stock price tomorrow.
• Model Building: Creating AI models in minutes without coding.
• Model Deployment: Putting models into production with a single click.
• Model Monitoring: Automatic checks on models, plus automations such as “if-this-then-that” workflows using Zapier.
• Integration & Sharing: Connecting models to other applications via real-time REST APIs, embedding AI into apps, or displaying prediction data in existing tools.
• Robotic Process Automation (RPA): Using AI to automate repetitive tasks.
• Expert Support: Access to dedicated data scientists for data merging, enrichment, cleaning, and statistical analysis.
• Collaboration: Sharing projects with your team, working together, and giving feedback.

Key takeaways

Obviously AI targets non-technical users who need predictive analytics without a data science team. It handles the full lifecycle — data preparation, model building, one-click deployment, monitoring, and API-based integration — while explaining model insights in an understandable way. Dedicated data scientist support is available for more advanced data work, making it a practical option for business analysts who want fast, code-free predictions.