If 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.
Read MoreOCR is not a checkbox — it's a regulated system that must meet audit SLAs and an error budget. Pick a document stack by error budget, not hype, and build an E2E pipeline with Snowflake + dbt + MLflow and active learning.
Read MoreReplace your IVR with a voice AI agent only when measurable throughput, handle-time, or lead-conversion gains exceed integration and run costs; otherwise keep IVR and automate the handoff. This post gives the ROI math, the data to collect, vendor tradeoffs (Twilio+Google CCAI, Amazon Connect, Genesys), deployment costs, latency traps, and a checklist that tripled booked leads in 90 days.
Read More
