It depends on the task. We pick per workload using an eval set built from your real inputs, and the abstraction lets you run both or switch later. Long-context and careful tool use often favor Claude; we still measure rather than assume.

Do you use LangChain?

No. We integrate against the model API directly, the same way we integrate against Postgres. Frameworks add abstraction and breakage we do not need for production reliability.

★ ClaudeLLM Models8-week engagement

ANTHROPIC CLAUDE · DIRECT INTEGRATION

Claude, integrated like any other dependency.

Anthropic's Claude is strong at long-context reasoning, careful instruction-following, and tool use. We integrate it directly against the API, with evals and a thin abstraction so you are never locked in.

LLM APIEval pipelinesTypeScriptPrompt governance

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Cycle

8 weeks · fixed price

Stack

Claude API, direct

Output

Production code + eval suite

Handoff

Full source ownership

[THE SHORT VERSION]

A frontier model with a careful temperament.

Claude is particularly good at long documents, structured reasoning, and following nuanced instructions, with strong tool-use support. As with every model, the engineering that matters is around it: prompt design, evals, retries, cost control, and a vendor-neutral abstraction. We integrate directly, no LangChain in the path.

When it fits

Long-context tasks: documents, transcripts, large codebases
Agentic tool use and structured, careful reasoning
Workloads where instruction-following quality matters

When it does not

Cases where an open-weight model on-prem is mandated
Tasks a much cheaper or smaller model handles just as well

[HOW WE BUILD IT]

How we build with Claude.

Direct API, thin abstraction

We call the Claude API directly behind a small provider interface. Swapping to another model is a config change, not a rewrite.

Prompts as versioned artifacts

Prompts are code: version-controlled, reviewed, and tied to the eval suite that measures them.

Evals before you trust it

An eval set that reflects your real tasks. We measure quality and regressions on every prompt or model change.

Cost, latency, and fallback

Token budgets, caching, streaming, and a fallback model path. Observability on every call.

[WHAT YOU GET]

What the engagement leaves behind.

Direct

No orchestration framework

Eval-gated

Quality measured, not assumed

1 swap

Vendor change is config

Observed

Every call, cost and latency

[COMMON QUESTIONS]

Questions we get asked.

Claude or GPT?: It depends on the task. We pick per workload using an eval set built from your real inputs, and the abstraction lets you run both or switch later. Long-context and careful tool use often favor Claude; we still measure rather than assume.
Do you use LangChain?: No. We integrate against the model API directly, the same way we integrate against Postgres. Frameworks add abstraction and breakage we do not need for production reliability.

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