Deepflow, the AI System Designed to Predict Prices
In raw material procurement, poor forecasting can lead to purchasing at the wrong price or holding excessive inventory, thereby eroding profitability.
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 rapidly changing realities of global markets accurately.
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 purchasing at the wrong price or holding excessive inventory, thereby eroding profitability.
Impactive AI’s upcoming platform, Quantum Deepflow, aims to address these challenges head-on.
Raw material procurement
Slated for its public demonstration at CES 2026, the system combines advanced AI demand forecasting with quantum machine learning.
The goal is to predict not just sales patterns, but the entire product lifecycle, while also optimising raw material procurement.
Deepflow can forecast raw material prices, such as copper, aluminium, or steel, up to six months ahead with claimed accuracy rates of 97–98%.
For predicting the commercial success of a product, the accuracy ranges from 70% to 80%.
The platform also supports inventory management, with one industrial client reporting a 35% reduction in inventory, which freed up roughly 31 billion KRW ($22M) in cash.
The system utilises a custom-built AI model trained on over 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 states that it is utilising 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.
Custom model
This is an area still under intense research, and few companies are utilising this tool in their day-to-day business.
For raw material forecasts, the platform does not require customer data, making onboarding easier.
Inventory optimisation, 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 aluminium prices with around 97% accuracy over a five-day exhibition.
Another case with a steel manufacturer resulted in the company improving its inventory forecast accuracy to 75%, far surpassing the accuracy achieved with its in-house methods.
That’s clear added value.
Most companies today utilise a combination of statistical models, enterprise resource planning (ERP) tools, and machine learning systems that run on classical processors.
Academic credentials
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 tie up working capital.
Quantum Deepflow aims to alleviate these constraints by providing a more accurate forecast.
Founded in 2021, Impactive AI specialises in demand forecasting across various industries.
The company’s leadership comprises seasoned experts with experience at Samsung Electronics, Merrill Lynch, and academic credentials from institutions such as 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. - https://www.ubergizmo.com
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 rapidly changing realities of global markets accurately.
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 purchasing at the wrong price or holding excessive inventory, thereby eroding profitability.
Impactive AI’s upcoming platform, Quantum Deepflow, aims to address these challenges head-on.
Raw material procurement
Slated for its public demonstration at CES 2026, the system combines advanced AI demand forecasting with quantum machine learning.
The goal is to predict not just sales patterns, but the entire product lifecycle, while also optimising raw material procurement.
Deepflow can forecast raw material prices, such as copper, aluminium, or steel, up to six months ahead with claimed accuracy rates of 97–98%.
For predicting the commercial success of a product, the accuracy ranges from 70% to 80%.
The platform also supports inventory management, with one industrial client reporting a 35% reduction in inventory, which freed up roughly 31 billion KRW ($22M) in cash.
The system utilises a custom-built AI model trained on over 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 states that it is utilising 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.
Custom model
This is an area still under intense research, and few companies are utilising this tool in their day-to-day business.
For raw material forecasts, the platform does not require customer data, making onboarding easier.
Inventory optimisation, 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 aluminium prices with around 97% accuracy over a five-day exhibition.
Another case with a steel manufacturer resulted in the company improving its inventory forecast accuracy to 75%, far surpassing the accuracy achieved with its in-house methods.
That’s clear added value.
Most companies today utilise a combination of statistical models, enterprise resource planning (ERP) tools, and machine learning systems that run on classical processors.
Academic credentials
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 tie up working capital.
Quantum Deepflow aims to alleviate these constraints by providing a more accurate forecast.
Founded in 2021, Impactive AI specialises in demand forecasting across various industries.
The company’s leadership comprises seasoned experts with experience at Samsung Electronics, Merrill Lynch, and academic credentials from institutions such as 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. - https://www.ubergizmo.com
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