Enhancing CRM Development with GenAI SDLC Toolkit

Company : ERPONE

ERPOne, a leading CRM solution provider, faced significant challenges in transforming business requirements into structured software components.

Business Challenge

ERPOne, a leading CRM solution provider, faced significant challenges in transforming business
requirements into structured software components. The key issues included:

Complex Business Requirements: The need to map intricate customer relationship workflows
into a robust CRM architecture.
Slow Requirement Processing: Manual translation of customer needs into technical
specifications resulted in long development cycles.
Inconsistent Data Model Validation: Ensuring business logic alignment across different CRM
modules was labor-intensive and error-prone.

How GenAI SDLC Toolkit Helped

By integrating the GenAI SDLC Toolkit powered by Amazon Bedrock, ERPOne automated and
streamlined its CRM development:

Automated User Story Generation: AI analyzed customer workflows and generated structured
user stories for CRM entities.
Logical Data Model Extraction: AI-driven validation ensured business rules were consistently
applied across modules.
Domain-Specific Filters: Applied indirect and direct filters to refine business logic and improve
CRM workflow accuracy.

Success Criteria

40% Faster Business Requirement Processing: AI-driven automation reduced manual effort
and accelerated the transition from idea to implementation.
30% Cost Savings: Reduction in manual validation and documentation efforts led to
operational efficiency.
Improved Code Consistency: AI-driven validation mechanisms ensured data integrity and
logical flow across CRM modules.

Lessons Learned

AI-Driven Development Improves Speed: Automating requirement translation significantly
reduced development time.
Data Governance Is Key: Ensuring structured data validation across CRM workflows improved
reliability.
Continuous AI Learning Enhances Accuracy: Iterative improvements in AI-driven logic models
helped refine CRM functionalities over time.

Enhancing CRM Development with GenAI SDLC Toolkit

Company : ERPONE

ERPOne, a leading CRM solution provider, faced significant challenges in transforming business
requirements into structured software components. The key issues included:

Business Challenge

ERPOne, a leading CRM solution provider, faced significant challenges in transforming business
requirements into structured software components. The key issues included:

Complex Business Requirements: The need to map intricate customer relationship workflows
into a robust CRM architecture.
Slow Requirement Processing: Manual translation of customer needs into technical
specifications resulted in long development cycles.
Inconsistent Data Model Validation: Ensuring business logic alignment across different CRM
modules was labor-intensive and error-prone.

How GenAI SDLC Toolkit Helped

By integrating the GenAI SDLC Toolkit powered by Amazon Bedrock, ERPOne automated and
streamlined its CRM development:

Automated User Story Generation: AI analyzed customer workflows and generated structured
user stories for CRM entities.
Logical Data Model Extraction: AI-driven validation ensured business rules were consistently
applied across modules.
Domain-Specific Filters: Applied indirect and direct filters to refine business logic and improve
CRM workflow accuracy.

Success Criteria

40% Faster Business Requirement Processing: AI-driven automation reduced manual effort
and accelerated the transition from idea to implementation.
30% Cost Savings: Reduction in manual validation and documentation efforts led to
operational efficiency.
Improved Code Consistency: AI-driven validation mechanisms ensured data integrity and
logical flow across CRM modules.

Lessons Learned

AI-Driven Development Improves Speed: Automating requirement translation significantly
reduced development time.
Data Governance Is Key: Ensuring structured data validation across CRM workflows improved
reliability.
Continuous AI Learning Enhances Accuracy: Iterative improvements in AI-driven logic models
helped refine CRM functionalities over time.