Large language models in products
We treat the LLM as a component. The product is retrieval, UX, evaluation, and operations around it.
Buyers often ask for GPT vs Claude vs Llama. The useful question is: latency budget, data residency, tool-calling reliability, and what happens when the vendor is down. We record that in the plan.
We do not train foundation models. Fine-tuning is optional and late. Default: RAG vs fine-tuning.
LLM development service · What is an LLM?
If the problem maps to work we actually ship, we will say so in 20 minutes.
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