Claude API Integration Services

Simple single-feature integrations (e.g. a summarisation endpoint) ship in 4–6 weeks. Multi-feature integrations with tool use, multi-turn sessions, and per-tenant prompts ship in 8–12 weeks. The hardening sprint at the end is non-negotiable; we have seen too many integrations skip it and pay for the omission in production.

Direct Anthropic gives you fastest model access and feature parity at the cost of a separate vendor relationship. Bedrock/Vertex/Azure give you IAM, VPC, and procurement consolidation at the cost of a few weeks of feature lag. Regulated clients usually pick a hyperscaler; speed-of-feature-adoption clients pick direct. We support all four; the recommendation comes out of the Discovery phase.

Three layers — application-level token budgeting, request-level retry with exponential backoff and jitter, and queue-level workload shaping that smooths spikes into the rate-limit envelope. For provisioned-throughput clients we add reservation-based scheduling so critical workloads do not starve.

It runs golden-set regressions against every prompt change, scores with a judge-LLM (Claude or another), and tracks drift over time. CI blocks prompt changes that drop eval-bar below threshold. Production prompts cannot be changed without an eval delta on record.

Defence in depth — input sanitisation, prompt-template isolation between system and user content, output validation against expected schemas, content moderation on outputs that flow back to users, and adversarial test suites in CI. We assume Claude will be jailbroken eventually; we make sure it does not matter when it is.

Yes — and it is the most common pattern. We add an abstraction layer that routes to either provider per use case, run both in parallel with the same evals, and migrate workloads as the eval data supports it. See /openai-to-claude-migration for the full migration path.

We work in all official Anthropic SDKs and have built integrations in Python, TypeScript, Go, and Rust. SDK choice is driven by your existing stack; we do not push a preferred language.

Yes. The Discovery phase produces a workload model with token estimates per request, request rate per use case, and a monthly cost projection. Most clients see 20–40% lower runtime cost than their initial estimate because the prompt design and caching are tuned during the build.