arthai-marketplace

/solution-architect

Design AI solutions for consulting engagements.

Synopsis

/solution-architect <client-name> [--initiative name]

When to use it

Quickstart

/solution-architect acme-corp

What you’ll see: solution designs for the client’s top 3 initiatives by score, technology selection matrices, build-vs-buy analyses, Mermaid architecture diagrams, a Gantt-charted implementation plan, and a 3-year cost model, written to the client’s architecture directory.

Examples

/solution-architect acme-corp                                  # architect the top 3 initiatives by score
/solution-architect acme-corp --initiative "Support Chatbot"   # architect one named initiative

Arguments & flags

Flag Values Default What it does
--initiative initiative name top 3 by score Filter the design work to a single initiative

What it does

  1. Loads context, synthesizes prior outputs from the client’s discovery/ and assessment/ directories: discovery/market-research.md, discovery/competitive-landscape.md, discovery/industry-trends.md, assessment/current-state.md, assessment/ai-readiness.md, assessment/stakeholder-map.md, and assessment/opportunity-matrix.md (the ranking source for “top 3 by score”); extracts objectives, data requirements, integration points, constraints, and budget per selected initiative.
  2. Technology selection matrix, evaluates options per component (LLM provider, vector database, orchestration, data pipeline, monitoring, frontend) on capability, cost, hosting, data privacy level, integration effort, and lock-in risk. Data privacy levels: 1 = public data only (cloud OK), 2 = internal data (SOC 2 required), 3 = PII/sensitive data (encryption at rest/transit required), 4 = regulated data. HIPAA/GDPR/CCPA (on-prem or private cloud required). Level 4 data rules out cloud-only and SaaS options outright, so it’s worth confirming a client’s privacy level before expecting cloud recommendations.
  3. Build vs buy analysis, for each major component: build custom vs SaaS vs open source, compared on cost, time to deploy, customizability, maintenance burden, data control, vendor risk, and skill required, with a recommendation tied to the client’s constraints.
  4. Architecture design. Mermaid system architecture and data pipeline diagrams (chosen from five reference templates: RAG pipeline, ML prediction service, document processing, chatbot/agent, analytics dashboard), an integration points table, and a security and privacy design covering auth, data protection, compliance, and AI-specific risks like prompt injection.
  5. Implementation plan, a Mermaid Gantt build plan, resource requirements table, a risk register with mitigations and owners, and a testing strategy including AI-specific tests (golden-dataset benchmarks, hallucination detection, prompt robustness).
  6. Cost model, 3-year TCO by year, break-even analysis, and a cost scaling projection from pilot to full scale.

A quality checklist runs before output: diagrams render, selections justified, timeline achievable, risk mitigations actionable, security matches the client’s compliance needs.

Output & artifacts

Written to clients/<client-name>/architecture/:

Troubleshooting

Problem Fix
Designs reference data the prerequisites should provide This skill reads discovery and assessment outputs. Run /client-discovery first (writes discovery/), then /opportunity-map (writes assessment/, including opportunity-matrix.md): both must complete before /solution-architect has what it needs
--initiative doesn’t match anything Use the exact initiative name from assessment/opportunity-matrix.md
A Mermaid diagram doesn’t render Re-ask for the diagram: valid, renderable Mermaid is part of the skill’s quality checklist
Recommendation conflicts with the client’s compliance needs Check the data privacy level assigned (1-4): regulated data (level 4) forces on-prem or private cloud options