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Increase data science productivity by 2-5x and start seeing results in 6-8 weeks

Value Proposition

TurboPipeAi transforms enterprise data into a powerful asset by streamlining AI/ML adoption through pre-integrated AWS native services using scripts. This enables the delivery of data, ML, and MLOps platforms in a matter of weeks. Additionally, TurboPipeAi offers an out-of-the-box solution with 15+ pre-built high-value ML solutions that can be launched in weeks

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Faster time to market in 6-8 weeks

Eliminate deployment and integration complexity

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Increase productivity by 2-5x

with infrastructure scripting as code

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Save 80% + in total cost
of ownership

Rapid Time to Value

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Disparate data sources, no problem

Structured, Unstructured,
to Scattered data

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Responsible Ai

Transparency and Explainability

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We do the heavy lifting from ingestion to insights

Why is TurboPipeAi better?

TurboPipeAi stands out because it accelerates the time to value for AI/ML projects by streamlining AWS services with infrastructure scripting. Its pre-built, high-value ML-powered solutions boost productivity, and cloud-native infrastructure and automation enhance scalability. Once the environment is set up, TurboPipeAi's ready-to-use solutions jumpstart the process, making it quicker and easier to achieve success

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  • Improved Productivity: TurboPipeAi streamlines AI/ML adoption with pre-integrated AWS services and infrastructure scripting, resulting in 2-5x higher productivity
  • Accelerated Time to Value: TurboPipeAi offers an out-of-the-box solution with pre-built ML models, reducing the time required to implement AI/ML projects
  • Enhanced Scalability: TurboPipeAi leverages cloud-native infrastructure and automation, allowing for greater scalability and flexibility than traditional AI/ML adoption
Traditional IT Solutions
Traditional IT Solutions
  • Integration Challenges: Traditional AI/ML adoption faces delays and challenges when integrating different platforms
  • Cost and Time: Hidden costs from inconsistent infrastructure and pipelines due to insufficient automation and standardization
  • Unclear ROI: Traditional AI/ML adoption lacks a clear path to successful outcomes and ROI