Calling an LLM in a demo is easy. Shipping it as a reliable API that your product depends on is a different problem.
Production AI APIs have to handle rate limits, manage costs that can spike without warning, stay available when a provider has an outage, and return consistent responses fast enough that users don't notice the model underneath. Getting all of that right from the start saves months of firefighting later.
We build AI-powered APIs that are production-grade from day one.
What's included
- OpenAPI-documented request and response schemas - your consumers know exactly what to expect and your contracts don't drift
- Caching and batching - reduce redundant model calls and cut costs without sacrificing response quality
- Rate limiting and quota management - protect your infrastructure and your budget from unexpected spikes
- Provider abstraction and failover - if your primary provider has an outage, traffic routes to a fallback automatically
- Cost controls and usage monitoring - real-time visibility into what you're spending and where, with hard limits you can enforce
How we work
1. Design. We start with your use case and define the API surface before touching a model. What does the caller send? What do they get back? What latency is acceptable? What does failure look like?
2. Build. We implement the API with caching, routing, and observability built in. You get a service you can deploy, monitor, and debug - not a wrapper around a single model call.
3. Ship. We deploy with you, tune under real load, and hand off documentation and runbooks your team can operate without us.
Why this matters
Most AI API problems show up in production - not in testing. Costs spike because nobody added a cache. The API goes down because the model provider had an incident and there's no fallback. Response quality drifts because prompts changed and nobody noticed.
We've seen these failures. We build in the patterns that prevent them from the start, so you're not rebuilding your infrastructure six months after launch.
Good fit if
- You're building a product or feature that depends on an LLM and can't afford downtime
- You've shipped an AI integration and are starting to see cost or reliability problems
- You need a clean API layer between your frontend and your model provider