Software Powered By Artificial Intelligence

Developing Software with AI Capabilities

By AI capabilities we emphasise over building a new software or evolving existing software with an output of AI analytic results for users (e.g., demand prediction) or trigger specific actions based on AI analytics. (e.g., blocking fraudulent transactions). Artificial Intelligence technologies allow an application to automate business processes, personalize service delivery and drive business-efficient insights. According to the Deloitte AI Institute , 90% of seasoned AI adopters say that AI is critically important to their business success today.

The estimates rely heavily on the specifics of AI module development:
  • Data volume used for AI and the number of data sources to process.

  • Data type (unstructured data is more expensive to work with than structured).

  • Data origin (there may be a need to buy external data) and whether it needs labeling (tagging data samples with the desired output).

  • Data quality (issues in data require more resources for cleansing).

  • Required accuracy rate for AI (the higher it is, the more time-consuming and expertise-demanding ML model tuning will be).

  • Complexity of ML algorithms.

  • Deployment type (AI outputs are in batches or in near-real-time).

  • AI maintenance costs (AI operating in a changeable data environment, e.g., feeding on dynamic user data, needs regular retraining).

  • Infrastructure costs.

Cloud Services to Speed Up Development of Software with AI Features:

AI helps in quick setting up, shared infrastructure for AI use cases, automated management of each stage in the AI module development with pre-configured infrastructure and workflows. Considering platforms, major cloud providers that Sutraa Techno recommends: Amazon, Microsoft, and Google. All of them are leaders in Gartner’s Magic Quadrant for Cloud AI Developer Services and offer integrated development environments (IDEs) with the following capabilities:

  • AI workflow orchestration and management.
  • Advanced security.
  • Automated model tuning.
  • Model performance monitoring.
  • Autoscaling of compute resources.
  • Bias detection, explainability features, etc.
  • Custom modeling with R/Python and supported frameworks (TensorFlow, PyTorch, scikit-learn, and others).

Use Cases for Software with AI Capabilities:

  • Business Process automation

  • Product management

  • Finance management

  • Customer analysis

  • Risk Management

  • Supply chain management

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