Landon Ferguson
Landon leads platform engineering at AgentTech I/O and owns the systems that make sub-second voice AI feel boring: WebRTC media plumbing, real-time call processing, transcription and LLM pipelines, telephony integrations, and the infrastructure that keeps thousands of concurrent agent sessions stable. He writes about the engineering trade-offs behind a modern AI dialer—latency budgets, audio quality, model selection, and what actually breaks at scale.
Articles by Landon Ferguson
15 Questions Insurance Agency Tech Buyers Should Ask Every Dialer Vendor in 2026
A vendor-evaluation rubric for agency principals and IT buyers — compliance posture, billing transparency, integration breadth, supervisor tooling, AI capabilities, exit, SLA.
Read the articleReal-Time Event Webhooks for Insurance Agencies: Use Cases an Operator Should Care About
What an agency operator should expect a modern dialer/CRM to push out as real-time events — call started, sale dispositioned, contact created — and the workflows it unlocks.
Read the articleClick-to-Call from Your CRM: How Insurance Agencies Wire AgentTech to Salesforce, HubSpot, and More
Integration patterns for click-to-call from external CRMs — what the agent experience looks like, what gets logged where, and what agency IT needs to know.
Read the articleIntegrating Your Lead Vendor with AgentTech: A Developer's Guide
How to use the queue availability API and SIP routing to connect external lead sources like Retreaver, Ringba, and affiliate networks.
Read the articleSpeech Analytics for Insurance Call Centers: Turning Conversations into Data
How speech analytics extracts trends, keywords, and compliance patterns from thousands of recorded calls.
Read the articleThe Queue Availability API: Let External Partners Check Your Capacity in Real Time
How publishers and affiliate networks use the public queue availability API to send calls only when agents are available.
Read the articleSentiment Analysis on Insurance Calls: Reading the Room in Real Time
How real-time sentiment analysis detects beneficiary confusion, frustration, or buying signals.
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