Success Stories

How Lifepack use AI to predict inventory and enable sales

Lifepack is an anti-biotics company that uses Obviously AI to predict sales and inventory levels for their drugs so they’re never out of stock. Read more to learn how they do it.


Sales and inventory predictions are made at an hourly, daily and monthly rate

Faster Predictions

Built 700+ predictive models in 1 day instead of 6 months


Used Zapier

Series A

Lifepack is an anti-biotics company that delivers over the counter drugs, general supplements and medication for chronic illnesses at affordable prices.

Data Overview
Data Merging
Statistical Work
Adding Business Logic

The Problem

Lifepack has over 2 million rows of data. Previously they used outdated tools like excel, minitab and rapidminer for forecasting. While these tools yielded statistical models, they fell short in unforeseen circumstances and were often very inaccurate. Additionally, they have 700+ SKUs that meant building 700+ AI models that took 12-24 months to get to each one of them manually. Lifepack needed something that was accurate, instantaneous and something that would help them become proactive in managing inventory.

The Solution

Lifepack turned to Obviously AI to build their AI models. They tried several solutions and needed something that was easy to automate and can easily integrate with their existing stack - MySQL, Google Sheets and PowerBI. With Obviously AI they built 700+ AI models in days with each model taking only a minute or two to build. After these models were built, they used the Zapier integration to connect these predictions to Google Sheets and PowerBI allowing them to forecast inventory needs in real time

The Outcome

With Obviously AI, Lifepack went from 12 months to 2 days of work in building AI models. Allowing them to be more proactive than reactive and saving 1 year worth of time for the data science team. Today, the data science team focuses on heavy duty AI work and automates these repetitive everyday modeling tasks.

Now we have accurate product predictions across all regions, enabling sales and operations.

Natali Ardianto

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