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We're building retrieval over clinical notes and medical literature. General-purpose embedding models (OpenAI, Cohere) perform worse than we expected on domain-specific terminology

We're building a document QA system over internal financial reports. We don't have labelled question-answer pairs and building a ground-truth dataset would take months. How are tea

We keep reading that RAG is the right default and fine-tuning is for style/format, not knowledge. But we've had cases where a fine-tuned model on domain-specific data outperformed

We've built a multi-step agent for contract review that works well in demos but fails unpredictably in production — wrong tool calls, missed steps, hallucinated outputs. What does

We've outgrown ad-hoc prompt editing in code. Engineers are stepping on each other's changes, we have no audit trail, and we can't run A/B tests on prompt variants systematically.

We launched an AI feature that's getting heavy adoption. Inference costs have gone from predictable to alarming. We've looked at caching, smaller models for classification steps, a