Client: Amazon eCommerce (Global Scale)
Challenge:
Amazon manages millions of products and billions of transactions daily. When customers sought support for returns, refunds, or product-related issues, customer care teams faced an overwhelming challenge — it was impossible for human agents to read and recall millions of product manuals in real time. The result: inconsistent resolutions, slower response times, and customer dissatisfaction.

Solution Implemented:
We designed and deployed a Generative AI–driven RAG (Retrieval-Augmented Generation) architecture on AWS Cloud, integrating Natural Language Inference (NLI), LLM-as-a-Judge architecture, and multimodal reasoning pipelines to achieve zero hallucination responses.

Key Components:

• AWS Bedrock Titan, Anthropic Claude Sonnet, and OpenAI GPT models were benchmarked with BLEU, ROUGE, and F1 scoring for accuracy and semantic integrity.
  
  

• Amazon Transcribe converted customer voice calls to text with near-human precision.
  
  

• Pinecone VectorDB and Hugging Face embeddings enabled contextual retrieval across product manuals, FAQs, and customer histories.
  
  

• Dual-LLM Orchestration: Primary LLM generated responses; Secondary LLM validated them using NLI and entailment scoring, ensuring factual correctness and sentiment alignment.
  
  

• Latency Optimization: AWS Fast Embedding Models and edge compute caching achieved sub-100ms response time across regions.
  
  

Outcome:

• 100% factual accuracy (verified via NLI benchmark and human audit)
  
  

• 80% reduction in customer response time
  
  

• Zero hallucinations across 10 million+ customer queries
  
  

• 35% uplift in customer satisfaction (CSAT) within 90 days of deployment

Client Context:
Global mortgage and title firms like ALAW (American Legal & Assurance Work, LLC) faced an exponential rise in document complexity and regulatory pressure. Attorneys were required to issue Attorney Opinion Letters (AOLs) with 100% accuracy — but traditional workflows couldn’t keep pace with digital lending velocity or audit scrutiny.

The Challenge:
Each AOL required analyzing hundreds of legal, title, and compliance documents — often leading to delays, inconsistencies, and compliance risks. The client needed a self-learning AI system capable of understanding legal nuance, reasoning like an attorney, and generating fully auditable outputs.

The Solution: MAX — Machine Augmented eXecution
I architected MAX, an Agentic + Generative AI ecosystem that fused legal cognition, reasoning, and document automation into a single intelligent layer.

• Perception Layer: Multimodal AI ingested contracts, mortgage files, and OCR documents, normalizing data across legacy systems.
  
  

• Reasoning Engine: Dual LLM architecture (Primary + Judge) used Natural Language Inference (NLI) to validate legal statements, assess contradictions, and ensure factual integrity.
  
  

• Action Core: RAG with contextual grounding dynamically generated Attorney Opinion Letters aligned with federal and state-level compliance frameworks.
  
  

• Learning Loop: Continuous feedback from attorney edits was captured via Policy Gradient Learning, enhancing model precision over time.
  
  

Integrated Technologies: AWS Bedrock (Titan), Anthropic Claude, Pinecone VectorDB, Amazon Textract + Transcribe, and Hugging Face Embedding Models.

Results:

• 99.8% factual accuracy in legal document generation
  
  

• 92% reduction in turnaround time for AOL preparation
  
  

• 60% cost optimization across legal operations
  
  

• Zero compliance deviations across 10,000+ audited documents

Client: iNurture Education Solutions partnered with Jain University, Bangalore
Challenge:
Amid the COVID-19 pandemic in 2021, educational institutions faced unprecedented challenges in transitioning to remote learning environments. Jain University, in collaboration with iNurture, sought to migrate their on-premise infrastructure to the cloud to ensure seamless delivery of online education, scalability, and enhanced student engagement.

Solution Implemented:
I led the AWS Cloud Migration Strategy, encompassing:

• Assessment & Planning: Conducted a comprehensive audit of existing infrastructure to identify migration needs.
  
  

• Architecture Design: Designed a scalable, secure, and cost-effective cloud architecture leveraging AWS services.
  
  

• Data Migration: Utilized AWS Data Migration Service to transfer databases with minimal downtime.
  
  

• Application Migration: Employed AWS Elastic Beanstalk and EC2 instances for application hosting.
  
  

• Security & Compliance: Implemented AWS Identity and Access Management (IAM) and AWS Shield for enhanced security.
  
  

• Training & Support: Provided training to university IT staff and ongoing support post-migration.
  
  

Outcome:

• Seamless Transition: Achieved a smooth migration with minimal disruption to ongoing academic activities.
  
  

• Scalability: Enabled the university to scale resources dynamically based on student demand.
  
  

• Cost Efficiency: Reduced infrastructure costs by 30% through optimized cloud resource utilization.
  
  

• Enhanced Learning Experience: Improved student engagement through reliable and responsive online platforms.

Client: British Dsire
Challenge:
British Dsire sought to scale their online retail operations globally. They needed a full-stack eCommerce ecosystem that could handle product onboarding, seasonal merchandising, sales strategy, marketing campaigns, and customer engagement — all while ensuring seamless integration with existing business systems and cloud infrastructure. The challenge was not only technical but strategic: optimizing the entire customer journey from discovery to delivery, across multiple channels.

Solution Implemented:
I led the design and implementation of a complete end-to-end eCommerce platform integrating cloud-native microservices, marketing automation, and data-driven sales optimization. Key components included:

• eCommerce Architecture: Built on Shopify + Amaze Commerce, with cloud-native microservices enabling scalability, flexibility, and real-time analytics.
  
  

• Payment & Transaction Integration: Integrated Razorpay, Salesforce CRM, and ERP systems for seamless order and payment management.
  
  

• Customer Engagement: Leveraged MoEngage, Mailchimp, SMS, and email campaigns for hyper-personalized marketing.
  
  

• UX & Merchandising: Designed seasonal UX strategies, 7P marketing, branding, and advertising campaigns to maximize conversions.
  
  

• Supply Chain & Fulfillment: Implemented optimized delivery networks and inventory management integrated with cloud microservices.
  
  

• Analytics & Optimization: Used FMEA and continuous A/B testing for conversion optimization and risk mitigation.
  
  

Outcome:

• 50% increase in online sales within the first 6 months
  
  

• 30% reduction in cart abandonment through enhanced UX and personalized marketing
  
  

• Seamless integration with CRM and payment systems, reducing operational overhead
  
  

• Scalable platform capable of handling seasonal demand spikes without downtime

Client: Chevening (UK Government Scholarship & Policy Program)
Challenge:
Chevening sought to develop forward-looking global policies that integrate autonomous driving technologies with AI ethics, sustainability, and international compliance standards. The objective was to align these policies with the UN SDG 2030 Agenda, ensuring that advancements in autonomous vehicles contribute positively to global goals such as Sustainable Cities and Communities (SDG 11), Industry, Innovation, and Infrastructure (SDG 9), and Climate Action (SDG 13).

Solution Implemented:
I led the formulation of comprehensive policy frameworks that addressed the technical, ethical, and regulatory aspects of autonomous driving:

• AI and Machine Learning Integration: Defined specifications for computer vision, image processing, radar (200m), LiDAR (50m 3D mapping), and ultrasonic sensors (1m to 1mm resolution) to standardize safety and performance benchmarks for autonomous vehicles.
  
  

• Compliance with Global Standards: Ensured alignment with international regulations and standards, including GDPR, HIPAA, CCPA, CPRA, and ISO 27001, to promote data privacy, security, and ethical AI deployment.
  
  

• Sustainability Alignment: Developed guidelines that link the adoption of autonomous vehicles with carbon reduction, energy efficiency, and long-term environmental goals, contributing to the achievement of the UN SDG 2030 Agenda.
  
  

• Policy Implementation Strategy: Proposed a phased approach for policy adoption, including pilot programs, stakeholder engagement, and continuous monitoring to assess the impact and effectiveness of the policies.
  
  

Outcome:

• AI Policy Framework: The developed policies were presented to international stakeholders, including government agencies, automotive manufacturers, and environmental organizations, for consideration and adoption.
  
  

• AI Governance: Established clear guidelines for the ethical deployment of AI ESG in autonomous vehicles, ensuring that technological advancements align with societal values and global sustainability goals.
  
  

• AI Regulatory Compliance: Provided a roadmap for compliance with international regulations, facilitating the global acceptance and integration of autonomous driving technologies.

Client: Suzuki Motor Corporation (Hamamatsu R&D Center)
Challenge:
In the pursuit of next-generation mobility solutions, Suzuki aimed to lead in autonomous driving, connected vehicle technologies, and sustainable automotive systems. The challenge was to integrate AI, IoT, and high-performance computing (HPC) into comprehensive vehicle architectures, ensuring compliance with global sustainability standards and positioning Suzuki as a leader in the evolving automotive landscape.

Solution Implemented:
I spearheaded Suzuki's AI-driven R&D initiatives, focusing on:

• Autonomous Driving Systems: Developed AI-based perception algorithms utilizing camera vision, LiDAR, and radar to enable Level 2 autonomy, enhancing safety and reducing human error.
  
  

• Connected Vehicle Platforms: Engineered IoT-enabled ECUs with edge computing capabilities, facilitating real-time data processing and communication within smart city ecosystems.
  
  

• Sustainable Powertrain Development: Led the design of electric and hybrid powertrains, incorporating AI-driven energy management systems to optimize performance and reduce emissions, aligning with Vision 2050 goals.
  
  

• Advanced Simulation Techniques: Conducted 1D, 2D, and 3D, SIL PIL HIL Plant Modeling, CAE, CFD, and FEM analyses to simulate and validate vehicle dynamics, NVH characteristics, and crashworthiness, ensuring compliance with global safety standards.
  
  

• AI-Enhanced Fuel Efficiency: Implemented LSTM deep learning models to predict and optimize fuel consumption patterns, contributing to Suzuki's commitment to carbon neutrality by 2050.
  
  

• Collaborative Innovation: Led cross-functional teams with global Tier 1 suppliers, including Bosch, Denso, Siemens, Continental, Magneti Marelli, and DuPont, to integrate cutting-edge technologies and materials into Suzuki's vehicle platforms.
  
  

Outcome:

• Leadership in Autonomous Mobility: Positioned Suzuki at the forefront of Level 5 autonomous driving technology, setting industry benchmarks for safety and innovation.
  
  

• Enhanced Connectivity: Established a robust connected vehicle ecosystem, enabling seamless integration with smart city infrastructure and enhancing user experience.
  
  

• Sustainable Innovation: Achieved significant advancements in electric and hybrid powertrains, contributing to Suzuki's carbon neutrality objectives and compliance with global emissions regulations.
  
  

• Accelerated Development Cycles: Streamlined R&D processes through advanced simulation techniques, reducing time-to-market for new vehicle platforms.