AI Migration
Moving from one AI provider or architecture to another without disrupting what's already working.
AI migration covers two scenarios: switching from one provider to another (OpenAI to Gemini, a deprecated model to its successor) and migrating from an early integration built for speed to a production architecture built for reliability. Both require more care than a search-and-replace on API endpoints.
Provider migrations involve evaluating the new provider's output quality on your actual data before committing, updating prompt structures where the new model responds differently, and running the old and new integrations in parallel to catch regressions before the cutover.
Architecture migrations — from a quick prototype to a maintainable production system — typically involve adding proper error handling and logging, moving prompts out of code into a manageable structure, and adding cost controls that weren't necessary at prototype scale but matter at production volume.
Migration scenarios we handle
Provider Switch
Quality evaluation on your data, prompt adaptation, and parallel running before cutover.
Model Version Upgrade
Regression testing when a model version is deprecated or a new one performs differently.
Prototype to Production
Error handling, logging, cost controls, and maintainable prompt management added to early integrations.
Zero-Downtime Cutover
Old and new integrations run in parallel until the new one is proven — no hard switch.