We undertake industrial research and share strategic insights to drive business process modernization for the Generative AI era

Our team is engaged in pioneering industrial research focused on harnessing the potential of Generative AI to revolutionize business operations. By exploring cutting-edge AI technologies, we aim to drive innovation and efficiency across various industries.

Our research provides insights to streamline operations, helping businesses reduce time and resource costs through automation and optimized workflows

Our findings on Generative AI empower businesses to create new products and services, keeping them ahead of market trends and responsive to customer needs.

Our research offers AI-driven strategies that lower operational costs by minimizing manual processes and enhancing resource allocation.

Multi-agent Orchestration

Empower your enterprise with agent-driven Gen AI solutions. This guide demystifies the role of agents in modern AI systems-exploring key concepts, technologies, and architectures that enable secure, scalable, and intelligent business interactions. From legacy system integration to real-world implementation strategies, discover how agents are transforming enterprise AI.

  • What is an agent?
  • What are the different types of agents?
  • What is the difference between an agent and an LLM?
  • What kind of intelligence does an agent provide to a business?
  • What is the role of agents in GEN AI?
  • How do agents interact with LLM?
  • What are the Agent market trends and business demand?
  • Discuss about the Business benefits of an agent
  • How does an agent fit into a complex legacy system environment?
  • Discuss how the GEN AI Solutions are at risk without an agent
  • GEN AI Solution with agent and without agent

  • Agent enable open source technologies
  • Agent enabled platform
  • Comparative analysis of technologies and platforms
  • Which technology is most effective for implementing Agent at scale?
  • Architectural components of the platform and technology
  • How Bedrock agents enable secure and safe interactions with business processes?

  • Case Study -1- Pubic Services
  • Case Study -2- Energy
  • Solution architecture
  • Summary of the implementation step
  • Detailed implementation steps
  • Source code organization

Guardrails

Build safer, more reliable Gen AI systems with Guardrails. This in-depth guide explores the concepts, technologies, and real-world applications of guardrails in LLM deployments-covering foundational principles, Amazon Bedrock integration, public sector use cases, and step-by-step implementation. Perfect for enterprises aiming to ensure responsible, secure, and compliant AI adoption.

  • 1.1 What Are Guardrails?
  • 1.2 Why Are Guardrails Necessary?
  • 1.3 Types of Guardrails
  • 1.4 Core Components of Guardrails
  • 1.5 Data and Type Support by Guardrails
  • 1.6 Risks of Deploying LLMs Without Guardrails
  • 1.7 Guardrails That Businesses Can Use
  • 1.8 Applications of Guardrails in Real-World Scenarios
  • 1.9 Developing the Guardrail Business Case and Stakeholders

  • 2.1 Technologies That Enable Guardrails
  • 2.2 Key Benefits of Amazon Bedrock Guardrails
  • 2.3 Strategic Implementation of Guardrails
  • 2.4 Developing and Testing Guardrails
  • 2.5 Challenges in Implementing Guardrails

  • 3.1 Comprehensive Controls in Amazon Bedrock Guardrails
  • 3.2 Protective Actions Enabled by Guardrails
  • 3.3 Advanced Features and Use Cases for Government Services
  • 3.4 Implementing Guardrails in the Public Sector
  • 3.5 Building the Business Case for Public Services Applications
  • 3.6 Why Amazon Bedrock Guardrails Lead the Industry

  • Implementation Steps
  • Scope of Technology
  • Source Code Organization
  • Applications Navigations
  • Conclusion

LLM as a Judge

Build trust and accountability in Gen AI with LLM-as-a-Judge and Human-in-the-Loop Feedback (HILF). This guide explores evaluation mechanisms, feedback loops, and integration strategies to enhance the reliability and fairness of AI systems. With best practices, implementation steps, and ethical insights, it empowers enterprises to create more accurate, transparent, and human-aligned AI solutions.

  • 1.1 Overview of LLM as a Judge
  • 1.2 LLM Evaluation Mechanisms
  • 1.3 Evaluation Tools and Platform Providers
  • 1.4 Crafting Custom Prompts for Evaluation Metrics
  • 1.5 Advantages of Using LLMs for Evaluation
  • 1.6 Challenges and Limitations
  • 1.7 Future Directions and Research Opportunities

  • 2.1 Introduction to Human-in-the-Loop Feedback
  • 2.2 Mechanisms of Human Feedback in Conversational Q&A
  • 2.3 Iterative Refinement of Responses
  • 2.4 Updating Knowledge Bases with Improved Responses
  • 2.5 Challenges and Considerations in HILF Implementation
  • 2.6 Best Practices for Implementing HILF

  • 3.1 Conceptual Framework for Integration
  • 3.2 Implementing LLM-as-a-Judge in HILF Systems
  • 3.3 Challenges and Considerations in Integration
  • 3.4 Case Studies of Integrated Systems
  • 3.5 Best Practices for Integration
  • 3.6 Ethical and Social Implications of Integration

  • 4.1 Implementation of LLM as a Judge
  • 4.2 Implementation of Human-in-the-Loop Feedback (HILF)

Semantic Search

Unlock the future of search with semantic intelligence across text, images, audio, and video. This comprehensive guide introduces foundational concepts like vector databases, embedding's, and multidimensional data, then walks through real-world implementations of unified semantic search. Ideal for business and technical leaders, it provides architecture insights, code organization, and proven strategies to deploy AI-powered search systems at scale.

  • What is semantic search?
  • Types of semantic search
  • Driving factors for semantic search in business
  • Business values of semantic search
  • What is a vector?
  • What is vector database?
  • Role of vector databases in semantic search
  • What is embedding?
  • Role of embedding in semantic search
  • What is multidimensional data in a vector database?
  • In business, why do we need vector databases?
  • How do vector databases differ from traditional databases?
  • Leading vector databases
  • Vector index
  • Vector search
  • Vector ranking
  • Vector space or embedding space
  • Vector Database Metadata
  • Querying a Vector Database
  • Vector database operations: Insert, update, and delete
  • Storing data in a vector database operation: Text, image, audio, and video
  • Business benefits of vector database
  • Challenges in implementing semantic search and vector databases
  • How it works?-Semantic Search
  • How it works?-Vector Database
  • Lessons learned

  • Text-based semantic search
  • Image-based semantic search
  • Video-based semantic search
  • Audio-based semantic search

  • Semantic search based on text, images, audio, and video

AI Ethical Framework

Ensure responsible AI development with a practical guide to AI Ethics. This guide offers a structured approach to ethical AI-from foundational principles and global frameworks to bias mitigation, transparency, and governance. Through real-world case studies and industry tools, it empowers enterprises to build secure, fair, and trustworthy AI systems that align with human values and societal impact.

Table of Contents

  • 1. Introduction
  • 2.Understanding AI Ethics Frameworks
  • 3. Governance and AI Oversight
  • 4.Addressing Bias and Ensuring Fairness
  • 5.Transparency and Interpretability in AI
  • 6.Privacy, Security, and Ethical AI Deployment
  • 7.Automation and AI Ethics Management
  • 8.Industry Applications and Use Cases
  • 9.Conclusion
Competitive Advantage: Stay informed with our research to adopt cutting-edge AI, positioning your business as an industry leader.
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Prompt Engineering

Master the craft of Prompt Engineering to unlock the full potential of Generative AI. This practical guide covers prompt types, creative techniques, and advanced prompting methods for content creation, expert simulations, and character play. With structured exercises and implementation-ready code, it's your go-to resource for building intelligent, high-impact AI interactions.

Table of Contents

  • 1. Executive Summary
  • 2. An Introduction to Prompt Engineering
  • 3. Questionnaires for Master Prompt Engineering
  • 4. Prompt Types
  • 4.1. Art of Prompt
  • 4.2. Prompting Methods
  • 4.3. Writing Formats
  • 4.4. Expertise
  • 4.5. Character Play
  • 4.6. Content Creation
  • 5. Appendix - Implementing Prompt Engineering
  • 6. GitHub – Source Code to Implement Prompt Engineering

Conversational Artificial Intelligence

Bridge business goals and technical execution with this comprehensive guide to Conversational AI. Designed for both executives and developers, it outlines strategic benefits, key challenges, and real-world solutions, then dives into the technical architecture, implementation steps, and code. A complete blueprint for deploying AI chatbots that drive business impact.

Part-1 Executive View

  • Executive Summary
  • Problem Statement
  • Business Challenges
  • Proposed Solutions
  • Business Benefits

Part-2 Technical View

  • AI chatbots
  • Building Blocks
  • Architecture
  • Implementation Steps
  • Code Block
  • Contact

Data Science

Transform business data into actionable insights with this end-to-end guide to Data Science and Analytics. From data modeling and real-world business cases to advanced SQL techniques and reporting standards, this book equips professionals to analyze, implement, and scale data-driven solutions across industries.

Table of Contents

  • Unit 1.Business Analytics Industry View
  • Unit 2.Data Analytics Business Cases
  • Unit 3.Data Model
  • Unit 4.SQL Commands (Part-1)
  • Unit 5.SQL Commands (Part-2)
  • Unit 6.Advanced SQL Commands
  • Unit 7.Data Analysis With SQL
  • Unit 8.Reporting and Analytics with Advanced SQL
  • Unit 9.Real-world Use cases Implementation
  • Unit 10.Developmental Standards And Best Practices

Python for Enterprise

Harness the full power of Python for enterprise-grade AI, analytics, and automation. This comprehensive guide covers everything from Python fundamentals to advanced applications in machine learning, NLP, computer vision, RPA, and deep learning. With real-world use cases and implementation-ready examples, it’s an essential resource for building scalable, intelligent enterprise solutions.

Table of Contents

  • Unit 1. Python Fundamentals
  • Unit 2. Big Data Processing with Python
  • Unit 3. Developing AI Models using Python
  • Unit 4. Developing Learning Algorithms using Python
  • Unit 5. Implementing Predictive Analytics using Python
  • Unit 6. Developing Machine Learning Models using Python
  • Unit 7. Data Visualization with Python
  • Unit 8. Simple Use Case Implementations using Python
  • Unit 9. Industry Use Case Implementations using Python
  • Unit 10. Python for Data Processing
  • Unit 11. Advanced Topics in Python
  • Unit 12. Python for Tensor Flow
  • Unit 13. Python for NLP (Natural Language Processing)
  • Unit 14. Python for Computer Vision
  • Unit 15. Python for RPA (Robotic Process Automation)
  • Unit 16. Python for Deep Learning
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