AI Engineering

We work with the Arcadia Model Based System Engineering method aiming to achieve several important outcomes:

1. Create a comprehensive system understanding by moving iteratively down and up through five system engineering architectual perspectives: (1) What is it that the users and stakeholders want to have done (system is not identified) > (2) What is the system expected to deliver in this context, what is its boundaries? > (3) Organize the system’s functions  into logically components > (4) Distribute the logical functions and components on the envisioned tech stack > (5) 

Design the "contracts" for development, acquisition, and integration. that ensures that the system  is built, integrated, and validated successfully.

2. Improved stakeholder alignment - The different perspectives support various stakeholders (business analysts, system architects, hardware/software engineers) with views that match their specific concerns and vocabulary.

3. The model-based approach allows testing design decisions before committing resources to implementation, catching problems or misconceptions when they're easy and cheap to fix.

4. Enhanced traceability - Clear connections between requirements and implementation help teams understand the impact of changes and maintain consistency.

5. Reduced integration issues - By modeling interfaces and interactions at multiple abstraction levels, teams can identify integration problems before the build process starts.

6. Knowledge preservation - The structured models become a knowledge repository that outlasts individual team members, preserving design decisions and rationale.

7. Efficient collaboration - Common modeling languages and interfaces allow specialists from different domains to work together effectively.

8. Regulatory compliance - The systematic approach and traceability features help demonstrate that requirements have been properly implemented, supporting certification in regulated industries.

These outcomes collectively aim to address the common challenges in complex systems engineering: scope creep, integration problems, communication gaps, and cost overruns.

Detail Engineering Architecture

The Detail Engineering Architecture (DEA) process translates high-level architectural concepts into implementable specifications, bridging the system engineering architecture with detailed design elements for final implementation.

- Ensures organizational objectives are translate to implementable design by aligning system engineering architecture with detail engineering architecture.

- Reduces implementation errors through comprehensive specifications

- Maintains traceability from requirements throughout implementation

Foundation and Preparation

   - Review system engineering  architecture 

   - Identify candidates for implementation of subsystems, components, and interfaces

   - Establish technical standards and frameworks

Component Specification Development

   - Define subsystems and components boundaries and responsibilities

   - Document API contracts and data models

   - Specify error handling and logging requirements

Interface  Design

   - Define  communication patterns and data flow

   - Define endpoints and payload structures

   - Specify authentication mechanisms and security controls

Implementation Planning

   - Develop coding standards and design patterns

   - Create deployment and runtime configuration specifications

   - Document testing strategy and performance benchmarks

Design Review

   - Verify alignment with system requirements

   - Confirm compatibility with CI/CD processes

Building components and architectural skeletons with the objective to gather information about known unknowns as well as unknown unknowns.  

Finalizing the specifications or "contract" to detail engineering for building the solution.

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