Drive Innovation and transformation through STL Digital’s AInnov ™, a Generative AI solutioning fabric for enterprises


STL Digital’s AInnov™ empowers enterprises to leverage AI for innovation and business value realization. AInnov is a comprehensive AI solution framework with a human feedback loop. It integrates software libraries, model data engineering methodologies, model management, data structures and algorithms. It also includes 360-degree image-to-insights processing capabilities

STL Digital’s

AInnov™ Business Model



Built on Top of Industry-Leading AI Platforms

Foundation Models provide a fantastic combination of accuracy and versatility

Customization by fine-tuning these models for enterprise-specific data

Ready to use AInnov™ solutions that can be quickly implemented for your business needs



End-to-end solutioning capability, focusing on transforming raw data into meaningful insights, content, and recommendations tailored to specific use cases.

AInnov’s data ingestion, data preprocessing, and feature extraction modules are meticulously designed to handle the intricate process of converting enterprise data into a suitable format for model training while prioritizing data security and privacy.

AInnov's extensive model library includes prediction models, recommendation models, and more, all designed to meet the unique requirements and generate the appropriate content for the next best action

Industry Trends

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The market size in the Generative AI market in 2023.
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The market size is expected to grow by 2030 with an annual CAGR of 24.40%
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of large enterprise finance teams will rely on internally managed and owned Generative AI platforms trained with proprietary business data by 2026



Industry Value Realization

STL Digital’s AInnov™ solution on Gen AI + Digital Twin modeling, through leveraging AI 
platforms, has the potential to increase productivity, improve predictive maintenance, and 
enhance security for multiple industries like energy, resources, life sciences, manufacturing 
& consumer goods. 

Increase productivity by generating synthetic data, optimizing processes, and automating tasks

Improve predictive maintenance by predicting equipment failure and identifying anomalies

Enhance security and safety by detecting fraud and preventing accidents



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