Call Center

Zero-Hallucination RAG Pipelines for Customer Support Architecture

Executive Summary Deploying Large Language Models (LLMs) into customer-facing support roles presents a major operational risk: hallucinations. When a support bot invents a non-existent refund policy or gives incorrect technical instructions, it destroys customer trust and creates legal liabilities. Standard out-of-the-box LLM prompts cannot reliably prevent hallucinations. Achieving enterprise-grade accuracy requires a guarded Retrieval-Augmented Generation …

Megaphone

Integrating Voice AI into Legacy Telephony Stacks: An Architectural Blueprint

Executive Summary Deploying LLMs for web chat or text workflows is relatively straightforward. However, bringing real-time, conversational Voice AI into legacy telephony infrastructure—such as traditional PBX systems, SIP trunks, and PSTN lines—introduces a major technical bottleneck: latency. Human conversation relies on turn-taking pauses between 200ms and 800ms. If an automated voice agent takes 2 to …

networking

Building an Agentic AI Lead Qualification Engine with RAG and Vector Search

Traditional B2B lead qualification is fundamentally broken. Standard web forms push static contact details into a CRM, where sales reps waste hours manually researching prospect websites, qualifying buyer intent, and sending repetitive scheduling emails. On the flip side, basic chatbot widgets rely on rigid rule trees that frustrate high-value prospects and fail when asked complex …