What’s New at Flieber

Inventory History Report

Planning is easier when you understand the past. The new Inventory History Report shows how the stock levels of each product have changed at every month, revealing not only how stock levels evolved but also how efficiently inventory is being managed. It’s full visibility into the progress of your planning decisions.

New Public API (GraphQL)

Flieber’s planning engine can now connect directly to your systems through a brand new GraphQL API. Pull sales, forecasts, inventory recommendations etc. into your spreadsheets or BI tools, and keep your teams aligned with real-time inventory insights wherever they work.

Google Sheets App

Spreadsheets remain a daily tool for most teams, so we built a direct integration to make your life easier. With the new Google Sheets App, you can automatically export data from Flieber to Sheets and even set up a schedule to updated it, combining the power of Flieber with the flexibility of a spreadsheet.

New Forecast Accuracy Metrics (backend-only)

Forecasting is only as good as the ability to measure it. Flieber has introduced a new forecast accuracy process to help you understand how your forecasts are performing. These metrics are helping refine our models today and will soon power new customer-facing features to allow you to also clearly understand where to focus on. For now, this feature is restricted to the backend, but it will be released in the frontend in October. 

Auto-mapping of SKUs and products

Inventory is always evolving — products get discontinued, new ones launch, sales channels open and close. Flieber adapts automatically so you don’t have to. With auto-mapping, workflows detect changes in SKUs across your sales channels and apply those updates directly in Flieber based on a set of rules. This means no more manual setup or endless reconfiguration — just a system that stays aligned with your business in real time.

Forecast accuracy (backend-only)

Getting forecasts right is one of retail’s biggest challenges. That’s why Flieber partnered with Nixtla — the leading experts in time-series forecasting — to build a state-of-the-art AI transformer model that raises the bar for accuracy. Now we’re taking it further. Flieber makes it easy to measure forecast accuracy in real time, so you always know which products you can trust and which ones need fine-tuning. For now, this feature is available only in the backend, but it will soon be part of the customer-facing app.

Enhanced editing interface

Inventory planning is complex, with countless variables to manage. That’s why Flieber has always offered an Excel-like interface for editing any information — because who doesn’t love the familiarity of a spreadsheet? Now, we’ve taken it further. With a brand-new editing library packed with powerful new capabilities, Flieber makes it faster and easier than ever to update data, configure rules, and keep your planning process in sync.

AI Replenishment Simulator (beta)

Replenishment decisions are high-stakes — too little and you risk stockouts, too much and you tie up cash in excess inventory. That’s why we built the AI Replenishment Simulator: a tool that lets you stress-test your plans before committing. With automated checks and scenario analysis powered by AI, Flieber highlights potential blind spots and shows you the impact of each decision — so you can place orders with confidence, avoid surprises, and trust your planning process. This feature is currently in Beta, but will soon be released to all customers.

AI-ready database

Behind every AI feature in Flieber is a huge amount of work to make our database AI-ready. Sales, inventory, and operations data come from dozens of fragmented sources, each with its own quirks. To unlock the power of AI, we’ve built pipelines that clean, normalize, and connect this data into a single, structured source of truth. This foundation not only improves the accuracy of forecasts and recommendations, but also ensures that every new AI capability we roll out is grounded in reliable, high-quality data.

Improvements to past sales pre-processing

Accurate forecasts start with clean and reliable historical data. That’s why we invested in improving the way Flieber processes past sales, applying smarter pre-processing steps that remove noise, adjust anomalies, and normalize data across channels. These improvements make the inputs feeding our forecasting models more robust, resulting in predictions that are not only more precise but also more resilient to sudden shifts in demand.

Expansion of bulk editing functionality

Until now, Flieber only allowed bulk editing for a limited number of products at once. With this update, there are no limits — users can bulk edit as many products as they need. Even better, edits can now be applied not just by selecting products individually, but also by using filters or targeting entire attributes (like category, color, or model). This makes it faster and more flexible than ever to keep product data accurate and aligned across the board.