Post-Call Sentiment + Lead Scoring: CallSphere vs Vapi Analytics Gap
CallSphere auto-scores every call: sentiment -1.0 to 1.0, lead 0-100, intent, satisfaction, escalation. Vapi gives you raw recordings. Here is the analytics pipeline.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
CallSphere auto-scores every call: sentiment -1.0 to 1.0, lead 0-100, intent, satisfaction, escalation. Vapi gives you raw recordings. Here is the analytics pipeline.
For a 5-person clinic, CallSphere's flat tier breaks even on day 14 vs Vapi's all-in. Here is the ROI math and migration timeline.
An agentic-AI perspective on Claude Code 2.1 background agents, covering orchestration patterns, tool use, and how asynchronous AI fits production agent stacks.
Infrastructure-level look at Claude Sonnet 4.6 Vertex, including GCP Anthropic, deployment topology, region availability, and cost considerations.
A practical engineering deep dive into MCP 1.0 vs OpenAI, covering architecture, tradeoffs, and what production teams need to know about tool protocol comparison.
A practical engineering deep dive into Claude Sonnet 4.6, covering architecture, tradeoffs, and what production teams need to know about production AI agents.
LoCoMo is the closest thing the field has to a memory benchmark right now. How to use it to evaluate Mem0, Zep, and a custom store with reproducible methodology.
AGUI is the emerging protocol for streaming agent state to UIs without bespoke glue. The spec, the Vercel/CopilotKit implementations, and the adoption signals to watch.
Magentic-One lifts the orchestrator-worker pattern into AutoGen 0.5. How to design teams that escalate to specialists without infinite loops or runaway cost.