back_office_ops · saas · workflow
Harvey: Resilient AI Infrastructure for Scaling and Managing Model Performance Across Millions of Daily Requests
Harvey needed to reliably manage bursty computational load across multiple AI model deployments serving millions of daily requests, while enabling fast onboarding of new model versions and providing granular real-time attribution of every model call.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Request enters centralized client library
A centralized Python library abstracts all model interactions and receives inference requests for both the product and developers.
Tools used
PythonRedisKubernetesSnowflakeOpenAI API
Outcome
Harvey achieved high availability across all model deployments through layered fallbacks and retries, a distributed rate limiter that handles bursty traffic without significant impact on throughput or latency, and runtime reconfiguration of limits across all geographically deployed clusters without restart and in just seconds.
Results
Time savedwithout any restart and in just seconds
Volumemillions of daily requests
Grounding & classification
Source type: technical build writeup
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