Deepflow, the AI System Designed to Predict Prices, Demand, and Inventory with Unusual Precision

Bringing a new product to market has always been a gamble. Even the most experienced manufacturers can misread market demand, overproduce, or miss a critical pricing window. The result is a high failure rate for new products, wasted resources, and unsold inventory. Traditional forecasting methods—often based on a simple three-month rolling average—struggle to anticipate the fast-changing realities of global markets.

For manufacturers, the lifecycle of a product—launch, production, sales, and eventual discontinuance—hinges on accurate predictions. Misjudging demand can mean tying up millions in excess stock, or worse, losing customers to competitors when shelves go empty. In raw material procurement, poor forecasting can lead to buying at the wrong price or holding too much inventory, eroding profitability.

Impactive AI’s upcoming platform, Quantum Deepflow, aims to address these challenges head-on. Slated for its public demonstration at CES 2026, the system combines advanced AI demand forecasting with quantum machine learning. The goal: predict not just sales patterns, but the entire product lifecycle, while also optimizing raw material procurement.

Deepflow can forecast raw material prices like copper, aluminum, or steel, up to six months ahead with claimed accuracy rates of 97–98%. For predicting the commercial success of a product, accuracy ranges between 70–80%. The platform also supports inventory management, with one industrial client reporting a 35% reduction in inventory and freeing up roughly 31 billion KRW ($22M) in cash.

The system uses a custom-built AI model trained on more than 60,000 variables, combining pattern recognition with proprietary algorithms. While many companies still rely on historical averages, Quantum Deepflow incorporates a broader set of signals, including market trends, seasonal patterns, and macroeconomic factors.

The company says it is using quantum computing through cloud-based quantum resources. According to Impactive AI, this allows for faster training times and more nuanced predictions in complex, multi-variable scenarios. This is an area still under intense research and few companies are using this tool in day to day business.

For raw material forecasts, the platform does not require customer data, making onboarding easier. Inventory optimization, however, involves creating a custom model using client-specific historical data.

The business goal is clear, and if the data presented is realistic, then the accuracy improvement is the main selling point. During a live demonstration for a German company, Quantum Deepflow reportedly predicted daily copper and aluminum prices during a five-day exhibition with around 97% accuracy. Another case with a steel manufacturer saw the company improve inventory forecast accuracy to 75%, far surpassing its in-house methods. That’s clear added-value.

Most companies today use a mix of statistical models, enterprise resource planning (ERP) tools, and machine learning systems running on classical processors. Forecast horizons are often short (weeks or a few months) and accuracy can degrade quickly in volatile markets. Raw material buyers often hedge against uncertainty by keeping large safety stocks, which ties up working capital. Quantum Deepflow aims to alleviate these constraints with better forecast.

Founded in 2021, Impactive AI focuses on demand forecasting across industries. The company’s leadership includes seasoned experts with experience at Samsung Electronics, Merrill Lynch, and academic credentials from institutions like Harvard. Its Deepflow platform has already been deployed in sectors ranging from pharmaceuticals to ingredients manufacturing, earning it recognition as one of the “Top 20 AI Solution Companies” by CIO Outlook.

With Quantum Deepflow, Impactive AI hopes to raise the bar for predictive analytics and transform how manufacturers plan, price, and produce.

Filed in General. Read more about AI (Artificial Intelligence), Business, Korea, Machine Learning and Startups.