unrealistsLLM fine-tuning & custom AI models
Teams searching for LLM fine-tuning consulting or a custom language model for their product often jump to training too early. We help you decide between prompt engineering, retrieval (RAG), supervised fine-tuning, and fully custom models—then implement the option that matches your data, latency, inference cost, and compliance needs.
Fine-tuning vs RAG vs custom
Hosted LLM APIs excel at many tasks with strong prompts and context. Fine-tuning an open-weight or commercial model helps when you need consistent tone, output format, or domain terminology at scale. Smaller custom models or classical ML still win for edge deployment, strict data residency, classification, or extraction where a general chat model is the wrong tool.
Fine-tuning done properly
Curated training data, held-out evaluation sets, and metrics tied to real user tasks—not leaderboard scores. We prepare datasets, run fine-tuning experiments, compare against a RAG baseline and a no-fine-tune baseline, and document retraining triggers. Solid data engineering keeps production PII out of training pipelines.
Beyond LLM fine-tuning
Embeddings, rerankers, hybrid retrieval, and traditional ML classifiers remain part of custom model work. We have shipped document extraction and self-improving document workflows where the answer was not “train a bigger LLM.”
Into production
Model hosting, inference cost control, drift monitoring, rollback, and human review for high-stakes outputs. We validate custom model ideas through a PoC or pilot before you commit to long-running GPU spend.