Artificial Intelligence Value Chain

Small Action Models leveraging genAI

 

• Leverage LLM models to drive UI based applications through knowledge graphs or state machines as quasi-RAG inputs delivering SAMs (small action models)
• Drive automated customer support using SAMs
• Automate both systems integration / flow testing as well as UAT (user acceptance testing)
• Drastically reduce ticket lifecycles as well as reduce test cycle times while delivering high quality

Automated requirements generation using genAI

• AI based automated software requirements generation using:
  • • inputs from client, business and tech team 
    • ticketing / bugs system(s)
    • field inputs from social user groups and marketing teams
    • User journeys analysis to streamline requirements
    • Market analysis for technology trends and revenue potential
    • Feature adoption analysis to influence prioritization of functionality 
    • Sprint velocity analysis to help plan requirements per sprint / epic / release timelines or in MVP

AI Based Ambiguity Analysis

 

• Well defined software requirements are the biggest contributors to successful project completion
• We have an NLP based solution to analyzing ambiguity and defining clear requirements
• The success rates have increased by over 30% by deploying the solution with our clients and in our own software products design
• Requirements overlap and / or dependency analysis

AI based automated Data migration

 

• Automated schema discovery and mapping leveraging AI
• Rules bank for data transformation and data cleansing
• Solution operability across database types
•  Huge productivity gain in finishing migration projects well before time and with high quality
• Works well for small setups or large enterprises across technology stacks

Leverage AI to predict faults

 

• Decipher patterns and anti-patterns in data (server logs etc)
• Corelate to fault conditions based on ML from past data
• Predict faults that may happen in future
• Proactively rectify so that faults do not happen, contributing to high availability (~99.9%)

AI powered accelerated SDLC cycle

 

• Enforce coding standards that go beyond rules-based reviews through AI
• Analyze code for potential errors 
• Use AI algorithms to intelligently orchestrate CI/CD pipelines, optimizing build, test and deployment process
• AI for vulnerability detection and security
• Predictive analytics for infrastructure or environment management

AIOps and MLOps

 

• Dynamically deploy ML models in operations based on need, monitoring alerts or goal definitions
• Dynamically interpret SDLC metrics to bolster speed to deployment through AI co-relations
• Identify process optimization opportunities
• Synchronize systems of record for SDLC (bug tracking) and Operations (ticketing system)

For any queries, please reach out to AI_practice@ushertechnology.com