We build custom LLM applications on Anthropic's Claude models — retrieval, context architecture, and evaluation built around your specific data and domain.
Custom Anthropic LLM Development Built Around Your Data
In short: Northell builds custom LLM applications on Anthropic's Claude models, with prompt architecture, retrieval, and evaluation built around your data.
Key takeaways
- Retrieval and context pipelines built around your actual data, not generic RAG.
- Evaluation sets built from your domain before anything reaches production.
- Prompt and system architecture designed for maintainability, not one-off tuning.
Frequently asked questions
Does 'custom development' mean fine-tuning the model itself?
Usually not — most production quality gains come from retrieval, context design, and prompt architecture rather than fine-tuning, which Anthropic restricts on Claude. We'll tell you honestly which lever actually moves your use case.
What kind of data can you build around?
Structured data (databases, APIs), unstructured documents, and mixed sources — the retrieval and context design differs by type, which we scope before building.
How do you evaluate quality without a fine-tuned model?
A domain-specific evaluation set built from real examples in your data, scored against clear pass/fail criteria — the same discipline you'd want for any fine-tune, applied to prompt and retrieval design instead.
Can this integrate with our existing data warehouse or CRM?
Yes — that's usually the first architecture decision: where the retrieval layer sits relative to your existing data infrastructure.
What's a realistic timeline?
6-10 weeks for a first production-ready use case, depending on data quality and how much retrieval infrastructure already exists.