Overview
We built a centralized AI routing and observability platform that standardizes how every AI request flows through the system, selecting the best provider and model for each task while capturing full logging, cost, and usage insight. We partnered to replace scattered, inconsistent provider integrations with a single, reliable, and extensible routing layer.
Challenges
- Teams were calling AI providers directly in inconsistent, ad hoc ways, with no shared standard for how requests and responses should be handled.
- The same provider integration patterns were being duplicated across the codebase, multiplying maintenance effort and the chance of divergence.
- There was no centralized logging or correlation tracking, leaving gaps in visibility into what AI requests ran, when, and with what result.
- Token usage and the real cost of AI calls were not measured, making spend and efficiency impossible to reason about.
- Provider and model choices were static and disconnected from task complexity, leading to suboptimal decisions on latency and cost.
- Async and specialized AI operations were not properly supported, and async execution paths were prone to integration errors.
- When a provider call failed there was no consistent fallback or failover behavior, leaving requests exposed to single points of failure.
- Migration and compatibility problems made it risky to evolve integrations or onboard new providers.
- Without standardized, enforceable integration rules, every new AI feature reintroduced the same inconsistencies.
Solution highlights
- Built a centralized AI request routing system that funnels every AI call through one consistent, governed entry point.
- Added async AI request handling so long-running and specialized operations are supported alongside standard synchronous calls.
- Delivered unified APIs for image, audio, transcription, text-to-speech, and vision workloads under one standardized interface.
- Implemented AI request logging with correlation tracking so every request can be traced end to end.
- Created a token usage and cost calculation system that quantifies spend and consumption across providers and models.
- Engineered a provider and model selection engine that picks the right backend based on task complexity.
- Added rule-based intelligent routing workflows with configurable routing policies and constraints.
- Built routing fallback and failover logic so requests reroute gracefully when a provider is unavailable.
- Shipped usage analytics and administrative reporting, plus linting and enforcement, migration tooling, and developer documentation to standardize every integration.
Outcomes
- Every AI request now flows through one centralized router, giving the organization a single, consistent way to integrate AI.
- Provider and model selection adapts to task complexity, so routing decisions are optimized for both latency and cost.
- Full request logging and correlation tracking deliver structured tracing and auditing across all AI activity.
- Token usage and cost are now measurable, giving clear visibility into AI spend and consumption.
- Fallback and failover logic keep AI requests resilient, handling provider failures without breaking the workflow.
- Async and specialized operations such as vision, audio, and transcription are reliably supported through standardized APIs.
- Consistency across AI integrations is enforced, eliminating duplicated provider patterns and fragmented handling.
- Observability and monitoring coverage improved, so teams can see, audit, and report on AI behavior with confidence.
- Linting enforcement, migration tooling, and documentation make the platform easier to maintain and extend as new providers and use cases arrive.