Production Claude apps — not chat demos.
We build with the Anthropic SDK every day. Tool use, the Agent SDK, prompt caching, batch APIs, citations, files, memory — all of it composed into hardened systems that ship to your production stack.
from anthropic import Anthropic
from aether import tools, cache
client = Anthropic()
response = client.messages.create(
model="claude-sonnet-4-6",
system=cache(system_prompt, ttl="5m"),
tools=tools.crm + tools.calendar,
messages=[{
"role": "user",
"content": query,
}],
betas=["context-management-2025"],
)
# cache_read: 8,420 tok · cache_write: 412 tok
# latency: 1.1s · cost: $0.0042 / reqWhen Claude is the right choice — and when it isn't.
We're not vendor-locked. We ship Claude when the task rewards it: long context, structured reasoning, tool use, and creative writing.
Long-context reasoning
1M token context windows on Opus/Sonnet — ingest entire codebases, contracts, knowledge bases.
Tool use that actually works
Native parallel tool calls, structured outputs, and a SDK that handles retries and budgets out of the box.
Prompt caching
5-minute and 1-hour caching can drop costs 70-90% on agent loops. We build with caching from day one.
Model routing
Opus for hard reasoning, Sonnet for default, Haiku for high-throughput — routed per task, not per app.
Citations + files
Built-in citation API and file handling — auditable answers from real documents, not hallucinated facts.
Agent SDK
The Claude Agent SDK gives us memory, context management, and compaction without rebuilding plumbing.
From bot to autonomous agent — at the right altitude.
Most teams over-engineer their first AI feature. We start at the simplest pattern that solves the problem and only add agency where it pays for itself.
- RAG chat over your knowledge base, with citations
- Tool-use bots that call your internal APIs
- Agent loops that plan, execute, and self-correct
- Batch pipelines for high-volume classification & extraction
- Coding agents that ship PRs against your repo