Integration work that survives production
Claude API engagements that go beyond the first working demo: caching, tool use, retrieval, batch, observability, and the unglamorous plumbing that keeps Claude working at scale.
Scope of engagement
- Claude API integration into existing enterprise systems: back-end services, queues, and customer-facing products.
- Prompt caching strategy to cut cost and latency on production workloads.
- Tool use, structured outputs, citations, and streaming wired correctly the first time.
- Batch and Files APIs for bulk workloads and large document flows.
- Retrieval patterns: vector, keyword, hybrid, and operational-data retrieval.
- Observability for prompts, cache hit rate, tool calls, cost, and quality over time.
Built by engineers who ship production systems
Claude integration work fails in exactly the places distributed systems always fail: retries, idempotency, backpressure, observability. Our team arrives having already built those foundations for Cassandra and Kafka clients. The result is integration code that copes with real operational pressure, not code that only works on a clean demo day.
A predictable path from scope to running system
Integration design
Walk the intended flow, identify boundaries, decide on caching, streaming, tool use, and retrieval before writing code.
Build
Implement the integration end to end with the right SDK, proper error handling, backoff, and retries.
Optimise
Prompt caching, model selection, tool ergonomics, and evaluation tuning once the baseline is running.
Operationalise
Hand over with observability dashboards, runbooks, and a defined ownership model for the integration.
What clients walk away with
Production integration, not a notebook
Claude running inside your services with proper error handling, observability, and ops ownership.
Healthy cache hit rate
Prompt caching configured deliberately so cost and latency both land where they should.
Evaluations you trust
A regression harness you can run before every change instead of hoping nothing has drifted.
Common questions
Which languages and SDKs do you work in?
We regularly build in TypeScript, Python, and Go, and integrate against the Anthropic SDKs as well as raw HTTP where needed.
Will you work on prompt caching for an existing integration?
Yes. Tuning cache hit rate on an existing integration is one of our most common shorter engagements and usually pays back quickly.
How do you handle long-running document or batch workloads?
Batch API for throughput-oriented workloads, Files API where document reuse matters, and careful queue design so retries and idempotency are explicit.
Do you build new retrieval systems or integrate with existing ones?
Both. For most enterprises the right answer is layering retrieval onto existing data systems rather than standing up a new store. We design accordingly.
Start a conversation
Tell us about the system you're building or the decision you're trying to make. We'll match you with a specialist.