If your legal team requires auditable vectors, don't DIY without a three-year TCO model — off-the-shelf vector stores plus managed LLMs usually win for mid-market. This post gives an apples-to-apples cost model, vendor tradeoffs (Pinecone, Weaviate, Milvus), managed LLM SLA realities (OpenAI, Anthropic, Vertex), and the contract clauses that cause the bills to spike.
Read MoreStop choosing on ideology. Use latency, throughput, and power-costs to decide whether to run inference on NVIDIA Jetson, Google Coral, or AWS Panorama for factory vision.
Read MoreIf your fraud stack is rules-first, don’t bolt on ML blindly — use a checklist: scale, labels, latency, explainability, and regulatory constraints determine whether to replace, hybridize, or retire rules.
Read MorePick a voice platform on operational guarantees, not NLU demos — wrong choices add latency, surprise PSTN bills, and vendor lock that kills scale. Use this checklist and ballpark pricing to decide.
Read MoreIf your voice AI can't prove cost-per-handled-call and booking lift inside 90 days, it's a pilot — not a product. This playbook gives the exact metrics, worked calculator examples, vendor tradeoffs, and operational KPIs to force a financial SLO before you sign.
Read MorePer-page accuracy pricing hides the real bill. This post breaks OCR TCO line-by-line — licensing, annotation, human-in-loop, infra, audit trails — and maps a real invoice ROI example.
Read MoreMost defect-detection pilots fail because teams design for lab cameras and perfect data, not the end-state sensor, compute, and operations constraints that run 24/7 on a shop floor.
Read MoreChoose Einstein when the ML feature is part of Salesforce and time-to-value matters; choose custom models when you need >10% ARR lift, strict data ownership, explainability, or peak performance.
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