Objective: Modernize and replatform existing applications to make them better consumers of AI-driven insights.
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Key Activities

Assessment and Strategy: Evaluate existing applications for AI integration potential and develop a replatforming strategy.
 

Architecture Redesign: Redesign application architecture to support seamless integration with AI models and data pipelines.

API Development: Implement APIs for interaction with AI services and data sources.

Cloud Migration: Migrate applications to cloud platforms like AWS, Azure, or Google Cloud for better scalability and access to AI tools.

Integration with AI Tools: Integrate with AI and machine learning platforms (e.g., TensorFlow, PyTorch, AWS SageMaker).

Outcomes: Enhanced application capabilities to leverage AI insights for improved decision-making and user experience.

Objective: Develop applications that generate high-quality data for training AI models and RAG (Retrieval-Augmented Generation) datasets.

Key Activities:

Data Pipeline Design: Design and implement robust ETL/ELT pipelines to ensure high-quality data flow from source to destination.

Data Collection: Develop mechanisms within applications to collect relevant and high-quality data.
 

Data Annotation: Implement data annotation processes to label data for supervised learning tasks.
 

Data Validation and Cleaning: Ensure data integrity through validation and cleaning processes.
 

RAG Data Preparation: Prepare and optimize data for retrieval-augmented generation tasks to improve AI model outcomes.
 

Outcomes: Improved data quality and pipeline efficiency, leading to more effective AI training and enhanced model performance.

Objective: Assess the level of application maturity for AI projects and provide a roadmap for enhancement.

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Key Activities

 

Maturity Model Development: Develop a maturity model tailored to the organization’s AI capabilities and goals.

 

Current State Assessment: Conduct a thorough assessment of current applications and AI integration levels.

 

Gap Analysis: Identify gaps between current capabilities and desired maturity levels.

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Roadmap Creation: Develop a detailed roadmap with prioritized actions to achieve higher maturity levels.

 

Benchmarking: Compare with industry standards and best practices to set realistic and competitive goals.

 

Outcomes: Clear understanding of AI readiness and a strategic plan to enhance application maturity for AI adoption.

Objective: Develop an application engineering approach that heavily leverages AI in code development and continuous improvement.

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Key Activities

 

AI in Development Tools: Integrate AI-powered tools for code generation, bug detection, and performance optimization (e.g., GitHub Copilot, DeepCode).

 

Automated Testing: Implement AI-driven automated testing for continuous integration and deployment (CI/CD) pipelines.

 

Performance Monitoring: Use AI for real-time performance monitoring and predictive maintenance of applications.

 

Code Review and Refactoring: Employ AI for intelligent code review and refactoring to improve code quality and maintainability.

 

Outcomes: Accelerated development cycles, improved code quality, and enhanced application performance through AI integration.

Objective: Establish intellectual property and legal underpinnings and standards for engineering organizations leveraging AI.

 

Key Activities:

IP Strategy Development: Develop a comprehensive IP strategy for AI-driven developments.

 

Legal Compliance: Ensure compliance with relevant legal and regulatory frameworks (e.g., GDPR, CCPA).

 

Patent Filing and Management: Assist in filing patents for AI innovations and manage the IP portfolio.

 

Contract and Licensing: Develop contracts and licensing agreements that protect AI intellectual property.

 

Standards Development: Create and implement standards for AI application development and deployment.

 

Outcomes: Secure IP rights, legal compliance, and established standards to protect and enhance AI-driven innovations.

Client Consultation and Needs Assessment: Engage with clients to understand their specific needs and existing infrastructure.

Strategy Development: Create a tailored strategy for replatforming, data optimization, and AI integration.

Infrastructure Setup: Set up necessary infrastructure, leveraging cloud platforms and AI tools.

Development and Integration: Develop and integrate AI solutions into client applications.

Training and Support: Provide comprehensive training and ongoing support to ensure successful adoption.

Monitoring and Optimization: Continuously monitor and optimize AI solutions for performance and compliance.

 

By offering these comprehensive services, organizations can enhance their applications' ability to leverage AI, generate high-quality data, and ensure robust legal and IP frameworks, ultimately driving innovation and competitive advantage.