Anthropic Claude
Claude is our default model for production AI work — and it has been since the Claude 3 family shipped. The reasons are practical, not religious: Claude is the most cost-effective model for the kind of long-context, instruction-following workloads our clients actually run (document RAG, agent reasoning chains, structured output extraction). Anthropic's safety posture and Australian data-residency story stand up to compliance review at APRA-regulated organisations, and the API has been stable enough for us to commit production load without watching for breaking changes every quarter. We use Sonnet for nearly everything, Haiku for high-volume classification tasks where the cost per call matters, and Opus selectively when the reasoning depth justifies it.
What you get
Real examples
Document RAG over 50,000+ enterprise documents
Illustrative scenario: an Australian financial services firm needs employees to query 12 years of policy documents in natural language. Claude Sonnet handles the answer generation with citations; Haiku handles the query rewrite + reranking. Response latency under 4 seconds at the 95th percentile.
Production AI agents with tool use
Illustrative scenario: a growing logistics company automates supplier invoice triage. Claude orchestrates a multi-step workflow — extract line items, match to PO, flag anomalies, route exceptions — processing 2,000+ invoices per month with auditable reasoning at each step.
Content generation at scale with brand-voice guardrails
Illustrative scenario: a SaaS company replaces a manual content team with a Claude-powered pipeline. Brand voice and accuracy maintained via an evaluation harness that runs every draft through automated checks before publish.
Common questions
Why default to Claude over GPT or Gemini?
For the production workloads we run most — long-context RAG, structured agent reasoning, agentic coding, brand-voice content — Claude gives us the best accuracy-per-dollar with the most stable instruction following, and the current Claude generation leads the frontier on coding benchmarks. We use GPT-5 selectively for general-purpose breadth and creative generation, and Gemini for multimodal and very-large-context tasks. The right answer is always 'route the model to the task'.
Can Claude run in Australian data residency?
Yes. Anthropic offers data-residency commitments for enterprise customers, and we typically route Claude through AWS Bedrock in Sydney for the strictest compliance contexts. For Privacy Act 1988 + APP 11 obligations, this clears the bar we've seen from APRA-regulated clients.
How do you keep Claude costs under control?
Three levers. One, route to Haiku for high-volume classification — it's ~5× cheaper than Sonnet and accurate enough for most of those tasks. Two, prompt caching on the system prompt and any large context (~90% savings on cached tokens). Three, evaluation pipelines that catch over-spending early so we can re-tune prompts or model selection before the bill arrives.
What if Anthropic pricing changes or the model is deprecated?
Every system we build is model-agnostic at the abstraction layer — swapping Claude for GPT or an open-source model is a few-day exercise, not a rebuild. We maintain evaluation harnesses precisely so we can prove the swap doesn't degrade accuracy.
Do you fine-tune Claude?
Anthropic doesn't currently offer fine-tuning. Where we'd otherwise consider it, we use prompt engineering plus RAG to get the same effect for most use cases. If the client genuinely needs a fine-tuned model, we'd recommend an open-source base (Llama, Mistral) on dedicated infrastructure rather than waiting on Anthropic.
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