Forcastra AI
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Intelligent forecasting and informed decision making

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Business organizations today operate amid turbulent market conditions with rapidly changing market dynamics. Accurate forecasting is key to success in such scenarios. Forcastra AI addresses this challenge by empowering businesses with precise forecasts, eliminating guesswork and enhancing strategic decision-making. 

It uses ML algorithms and deep learning techniques to manage thousands of SKUs and deliver forecasts and valuable insights for advanced scenario planning and cost and operational efficiency. It enhances forecasting accuracy, robustness, and flexibility through efficient model selection, a multi-model approach, and streamlined MLOps workflows.

Forcastra AI simplifies intricate processes in large-scale and complex ecosystems. It makes use of Gen AI to create a natural language conversation interface and conversational nudges, making the entire process of forecasting much more approachable to the end users. 

 

platform benefits

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Enhanced accuracy

Optimizes forecast accuracy by analyzing data, selecting optimal models, reducing complexity, and simplifying the forecasting process.

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Faster time-to-market

Guarantees a faster time-to-market through an inbuilt schema, models, and pipelines that enable faster time to market.

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Lower total cost of ownership

Ensures a reduced TCO through streamlined implementation and serverless architecture components in the architecture.

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Flexibility for precision

Offers flexibility to adjust workflows and models to match the specific needs of your forecasts, making them more accurate and focused by working with changing data patterns.

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numbers in focus

 
"55% of sales leaders do not have high confidence in their forecasting accuracy" - Gartner
 
"67% of organizations lack a formalized approach to forecasting" - CSO Insights

platform USPs

Intelligent segmentation

Intelligent segmentation for lean models

Leveraging smart segmentation techniques grounded in time series characteristics, our platform clusters similar series together, reducing the necessity for numerous models.

 

Funnel methodology

Sieve/funnel methodology

Strategically sifts the data to identify optimal forecasting models, streamlining predictions for various scenarios.

Versatile modeling

Versatile models for diverse demand patterns

Provides capacity to employ a spectrum of models – from multi-time series to single-time series, zero-inflated, hierarchical, and more.

MLOps


MLOps

Addresses large-scale forecasting challenges by providing a structured and automated approach to managing the entire ML lifecycle.

 

GenAI-based consumption


GenAI-based consumption

Consumes natural languages and provides a granular analysis of forecasts and scenario-based planning to arrive at the next best action. And nudges users with specific forecast details to make timely decisions.

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Streamlined workflows

Revolutionizes engineering workflows, simplifying intricate processes like feature engineering, model selection, version control, and prediction pipelines.

success stories

Demand forecasting for a European airline

The client is a large airline carrying 100 M+ passengers annually. The airline operates in highly competitive markets, requiring careful consideration of factors such as price elasticity, holiday and event impact, and network effects. We developed a large-scale demand forecasting (using Forcastra AI) and price optimization engine that uses several datasets and external factors to enable revenue managers to estimate demand accurately. The airline’s demand forecasting accuracy and the average seat load factor improved significantly, enabling better planning and revenue management. 

 

Meet Nagarro experts at World Aviation Festival-1

Claim forecasting for a perishable goods supplier

Our client deals with perishable goods and requires accurate claim predictions to reduce costs and improve efficiency. Using Forcastra AI, we built a solution for modeling and simulations that helps the client analyze and assess the claims data to minimize losses due to exaggerated claims. 
 
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AI and ML-based forecasting for advanced scenario planning