TTL AI in NPI Solution Suite on AWS - Professional Services

Tata Technologies' AI in NPI Solution Suite is a professional services offering designed to help automotive OEMs deploy and operationalize AI-enabled engineering and manufacturing processes on AWS. Built on Tata Technologies' Chromosome.ai framework, the solution leverages AWS services including Amazon EKS, Amazon EC2, Amazon S3, Amazon RDS, Amazon EFS, DynamoDB, AWS MQ, AWS WAF, and Amazon ECR to provide scalable, secure, and enterprise-grade deployments. The engagement includes assessment, architecture design, implementation, integration, migration, customization, governance, training, and operational support for customer environments running on AWS. The solution supports the following key capabilities:

1. Intelligent Requirements Engineering (Product Definition) AI agents parse large, unstructured requirements and run feasibility and consistency checks, flag conflicts and gaps, and decompose requirements through system and sub-system levels with full traceability. Work that traditionally takes weeks of manual review is compressed into hours, giving teams a validated requirement baseline far earlier. Expected Outcome: up to 30% reduction in requirements engineering effort

2. Generative Design & CAD Automation (Product Engineering) AI generates, evaluates, and optimizes engineering concepts and CAD geometry against defined constraints, producing and comparing design variants in a fraction of the time manual modelling requires. By automating repetitive modelling and surfacing reusable design knowledge, teams converge on viable designs faster and focus expertise on innovation. Expected Outcome: up to 30% faster design cycles and up to 40% reduction in manual modelling effort.

3. AI-Enabled Test Generation & Validation (Verification & Validation) AI agents generate test cases directly from requirements and design models, eliminating the weeks engineers spend authoring tests by hand and ensuring every test traces back to specification. Expected Outcome: up to 30% reduction in test authoring effort

4. Intelligent Failure Mode Analysis (Manufacturing & Process Engineering) AI-powered process enables risk assessment and control planning to proactively identify manufacturing failure modes and improve production quality and operational efficiency. Expected Outcome: up to 30% reduction in risk analysis effort and 30–40% faster process validation.

5. Vision-Based Quality Inspection (Operations & Production) AI-powered visual quality assurance proactively identifies manufacturing and assembly defects to support faster and more reliable product launches. Expected Outcome: up to 70% fewer quality escapes and 40–50% faster inspection cycles.

6. Warranty Intelligence & Field Analytics (Service & Field Feedback) AI-powered warranty and field failure intelligence proactively identifies emerging quality issues; accelerate engineering close-loop actions, and drive continuous product quality improvement across the lifecycle. Expected Outcome: up to 70% reduction in warranty analysis effort.