AI based Asset Management

Operations Planning

Financial Planning

Empower Resources & Training

IoT data meets machine learning. Leveraging AI-twin, raw IoT data is refined into concise, human-usable concepts, unlocking new avenues for understanding and utilization.

Digital Twin

Bridge the gap between physical and virtual, brinign transparency and confidence. A twin with knobs that let you tweak things in virtual world evaluate impact before committing in real enevironment.

AI intelligence

Transform sensor signals into actionable moments. Elevate tens of Iot signals into meaninful narratives. Form a complete picture from Iot fragements and data points

Why it matter

For Business Analyst

Raw Signal Intelligence

AI Twin goes beyond simply collecting data. It ingests raw signals from across your factory floor, from sensors on machines to environmental indicators.

For Data Scientists

Experimentation Playground

Want to test new production tweaks without risking real-world disruptions? AI Twin's virtual environment lets you experiment safely and see the potential impact before making changes.

Well governed data

Data Lineage and Extensibility

To build a commercial data product, create a base data product. Then add extension to these data product by adding various types of transformation. However it lead to complexity as you have to manage Data Lineage. Use knobs for lineage and extensibility


Higher-Level Data Products:

Forget data overload! AI Twin transforms raw data into actionable insights in the form of easily digestible data products,

Knowledge worker

For Machine and Human

AI Twin's data products are designed for both humans and machines. Business users gain clear, actionable insights, while automated processes can leverage the data for real-time optimization. With AI Twin, you can:

Business and Finance

Extend equipment life

Implement predictive maintenance to avoid costly breakdowns and keep your machines running smoothly


Generative AI slides

  • Learn generative AI - applications, LLM, architecture
  • See best practices for prompt engineering
  • Evaluate whether you should use out of box foundation model, fne tune or use in-context learning
  • Most important - be aware of concerns, issues, challenges, risk of genAI and LLM
  • See vendor comparison - Azure, OpenAI, GCP, Bard, Anthropic. Review framework for cost computation for LLM

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    Fractional CTO for generative AI and Data Products

    Startup and enterprise who wish to build their own data prodct can hire expertise to build Data product using generative AI

  • Generative AI expertise
  • Machine Learning expertise
  • Data product building expertise
  • Cloud - AWS, GCP,Azure