If 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.
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.
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