Each use case includes a problem statement (who, what, why), recommended solution approach (RAG, agentic, fine-tuning, or classical ML), expected ROI/KPI signals (latency, accuracy, cost, user satisfaction), execution challenges (governance, data prep, labeling cost, hallucination), and a pilot-to-production rollout path.

Banking compliance assistant, insurance claim-file summarization, e-commerce product-recommendation explainer, legal contract red-flag detection, healthcare clinician-note summarization, manufacturing SOP Q&A assistant — most of these titles map to your sector too. For cases not listed, discovery calls can surface additional patterns.

These are not theoretical proposals. Most are anonymized patterns extracted from real customer engagements. Detail pages cover the rationale for architectural choices, vendor comparisons, and where appropriate, reference implementation snippets.