Solution Design & Architecture

Translate business problems into Claude-based AI solutions

Map stakeholder goals, constraints, owners, and success metrics to a Claude-shaped scope — decide where language judgment beats deterministic software before picking models, tools, or RAG.

Design end-to-end architectures (input → processing → output → feedback loops)

Design the full path from intake through processing and delivery, plus the feedback loop that captures outcomes, failures, and human review for continuous improvement.

Select appropriate architectural patterns (workflow, agentic, augmented LLM)

Choose workflow, agentic, or augmented-LLM patterns from task structure, control needs, latency, and unpredictability — not from buzzwords.

Design multi-agent systems and orchestration strategies

Split roles, pass context explicitly, and orchestrate specialists through a coordinator — no inherited memory, no peer mesh that hides accountability.

Apply decomposition techniques for complex problem solving

Break complex work into units that fit model attention, tool scope, and verification — adaptive when structure is unknown, fixed when dependencies are clear.

Align solutions to business value pillars (efficiency, transformation, productivity, cost, performance SLAs)

Tie every architecture choice to named pillars — efficiency, transformation, productivity, cost, and performance SLAs — with measurable trade-offs stakeholders accepted.