Design governance, evaluation, and orchestration systems that route tasks across heterogeneous AI models while balancing cost, latency, and reliability.
Before building controllers, catalog the models in play. Create a capability atlas that captures each model’s strengths and limitations across several axes.
Tag models with confidence scores for each capability dimension. Use empirical evidence from benchmarking rather than marketing claims. Maintain the atlas as a living artifact; new releases, fine-tunes, or regulatory updates shift capabilities frequently.
Deliver highest general reasoning quality, but carry premium cost and slower response times.
Target domains such as finance, healthcare, law, or customer support with tuned vocabularies and compliance.
Optimize for speed and cost, serving autocomplete, low-stakes summarization, or retrieval tasks.
Enable offline functionality, privacy-sensitive workflows, or low-latency experiences on constrained hardware.
Balance portfolios by pairing frontier models with specialists and utilities. Excess reliance on a single provider creates concentration risk; diversification increases resilience.