Practical post‑deployment controls for airewrite that cut token spend 62% and reduced inaccurate summaries 78% — runtime prompt slicing, Pinecone caching, adaptive temperature gates, cohort rollouts, and predictive monitoring.
Read MoreA bot that drops leads or spikes your cloud bill will cost more than a delayed roadmap. This checklist gives production SLOs, realtime cost alerts for Vertex/OpenAI, decision rules for putting logic in Salesforce vs the voice agent, and five field-mapping mistakes that silently lose revenue.
Read MoreAn audit‑ready contract reader must prove provenance, show deterministic failures, and hold a measurable error budget; this post gives an engineering spec, vendor patterns, and an actionable audit checklist.
Read MoreIf your AI receptionist doesn’t write calls, intents and disposition data back to Salesforce in a usable way, it’s a toy. This post gives a production-ready reference architecture, canonical field mappings for Leads/Contacts/Cases, and three integration patterns with cost and consistency tradeoffs.
Read MoreShort answer: sometimes — but most call centers get the legal and technical parts wrong. This checklist ties consent, PCI/HIPAA, watermarking, and audit trails to measurable risk reduction.
Read MoreIf your RAG prototype has no SLOs, cost allocation, or automated kill‑switch, it will blow the POC budget. Practical guardrails for latency, accuracy, token budgets, vector-store spend, autoscaling, and hard safety fences.
Read MoreIf 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.
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