Claude API Integration
Claude wired into your product properly, with streaming, tool use, prompt caching and structured outputs rather than a bare HTTP call in a controller.
Getting more out of Claude than a single call
A first Claude integration is usually one request in a controller with the whole conversation pasted into a prompt. It works, and it is also leaving most of the platform on the table: prompt caching, tool use, streaming, extended thinking and structured outputs all change what the integration can do and what it effort.
Prompt caching alone is often the difference between an affordable feature and one that gets cut. A long system prompt or a large document re-sent on every turn is billed every time unless it is cached, and most teams discover this from an invoice rather than the docs.
We build the integration as a proper client layer: typed requests, streamed responses, tool definitions with validated arguments, retries with jitter and a scope meter per feature. The patterns are the ones in our Claude API integration guide.
Teams that start here often pair it with AI integration, AI agent development and OpenAI integration.
Prompt caching is the single most common thing teams miss, and it is usually the difference between a feature that ships and one that gets cut on scope.
Where Claude integrations leave value behind
Four things we find in almost every integration review.
Long system prompts and reference documents are re-sent on every call and billed in full, when a cache breakpoint would cut input scope dramatically.
The whole completion is awaited before anything reaches the user, so a response that streams in two seconds feels like an eight-second stall.
Tool calls are executed on whatever arguments arrive, so a malformed call reaches a production system with no schema check in between.
A rate limit or overload response fails the user request outright instead of retrying with backoff against a fallback model.
What a Claude integration engagement covers
A client layer your team can extend, with the platform features actually switched on.
Requests and responses typed end to end, so a model or version change surfaces as a compile error rather than a runtime surprise.
Cache breakpoints placed around stable system prompts and reference material, with hit rate reported so the saving is visible.
Token streaming to the client with correct backpressure and cancellation, so abandoned requests stop scopeing money.
Tool schemas defined and arguments validated before execution, with idempotency on anything that writes.
Responses constrained to a schema and parsed safely, so downstream services never receive prose where they expect a record.
Per-feature token scopes, spend dashboards and alerting, so the bill is understood before it arrives.
From first call to production integration through controlled rollout
One feature done thoroughly, then the pattern repeats cheaply.
We review the intended feature, the data it touches and the latency and scope scope it has to live within.
Typed client, retries, streaming and caching built and tested against the real API before feature logic starts.
The feature implemented on top, with tool schemas and structured outputs defined up front.
Realistic load testing with cache hit rate and scope per request measured, then tuned.
Behind a flag on a traffic slice, widened as latency, error and spend dashboards hold steady.
The stack behind our Claude integrations
Provider-agnostic where it matters, Claude-specific where it pays.
Claude for reasoning-heavy work, with routing to smaller models for high-volume simple calls.
Typed, streamed and resilient, with retries and fallback built into the transport rather than the feature code.
Tool definitions validated against a schema, with idempotency keys on every write path.
Latency, cache hit rate, token spend and error class visible per feature.
What this looks like in production
Cutting inference spend by two thirds without touching quality
A B2B product had shipped a Claude-powered assistant that customers liked and finance did not. The system prompt was four thousand tokens, the customer knowledge base was pasted in on every turn, and none of it was cached.
We restructured the prompt so the stable material sat behind a cache breakpoint, moved conversation history to a summarised rolling window, and routed simple intent classification to a smaller model.
Nothing about the user-facing quality changed. The invoice dropped by roughly two thirds and the feature stopped being a line item anyone argued about.
Integration engineering, not prompt tinkering
Most of the win in a Claude integration comes from the layer around the call: caching, streaming, validation and routing. That is engineering work, and it is what we do.
Caching, streaming, tool use and structured outputs are switched on and measured, not left as things to look at later.
A working demo and a written note every Friday covering what shipped, what slipped and what it means for the date. No status theatre.
Nothing goes live in one jump. We run in parallel or behind a flag until the numbers say it is safe to switch over.
