Design governance, evaluation, and orchestration systems that route tasks across heterogeneous AI models while balancing cost, latency, and reliability.
Portfolios evolve: new models onboard, underperforming ones retire, and bespoke fine-tunes emerge.
Run the evaluation harness, compare against benchmarks, and document strengths.
Security analysis, privacy impact assessment, legal review.
Expose the model to a subset of traffic with shadow comparisons to incumbent models.
Confirm metrics, finalize contracts, and update documentation.
Retire models when performance degrades, costs spike, or contracts end. Provide migration plans, including re-routing traffic, updating downstream dependencies, and archiving historical data.
Track versions with semantic labels (major.minor.patch). Route based on explicit version policies rather than implicit provider changes. When a provider releases a new version, treat it as onboarding—a regression may hide inside a “minor” update.