LLM Integration Architecture
I help teams evaluate LLM integration approaches for products and engineering workflows, including provider tradeoffs, API boundaries, operational guardrails, cost and reliability considerations, and integration into existing systems.
The Approach
What I Address
Teams integrating LLMs without architectural discipline face unpredictable costs, fragile integrations, inconsistent behavior, and operational blind spots that undermine trust in production.
Without evaluation of integration patterns, failure handling, and ownership, LLM usage becomes ad-hoc rather than a maintainable part of the engineering stack.
How I Work
I provide LLM integration architecture guidance focused on practical engineering adoption:
- Assess integration goals across products and internal workflows
- Evaluate provider options, API boundaries, abstraction, and fallback strategies
- Define operational guardrails, validation, and observability expectations
The Value
Outcomes
- Clear LLM integration approach aligned with product and engineering goals
- Provider and integration choices grounded in operational tradeoffs
- Defined guardrails and failure handling for production reliability
- Improved confidence in shipping and operating AI-powered capabilities
- Reduced risk of brittle, expensive, or unmaintainable integrations
Deliverables
- LLM integration assessment and architecture recommendations
- Provider and integration pattern guidance with tradeoff analysis
- API boundary, guardrail, and operational documentation
- Cost, reliability, and ownership considerations
- Implementation roadmap and follow-up consultation
The Fit
Who This Is For
Engineering teams planning or reviewing LLM-powered features, product teams evaluating AI capabilities, and organizations adopting LLMs in internal tools.
Ideal timing includes when scoping first integrations, during architecture reviews of existing AI features, when changing providers, or when scaling usage across multiple workflows.
Why LLM Integration Architecture Matters
LLM capabilities can add real value in products and internal tools, but integration decisions affect cost, reliability, and long-term maintainability. Without clear architecture and operational boundaries, teams ship brittle features that are expensive to run and hard to own in production.