Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Implement real-time sentiment analysis that detects frustrated or angry customers during support interactions and triggers automatic escalation to senior agents before the situation deteriorates.
Design and implement multi-step data extraction pipelines that transform unstructured text into clean structured data using LLMs. Covers entity extraction, relation extraction, and pipeline orchestration.
Build comprehensive observability for your AI prompts. Learn structured prompt logging, performance tracking dashboards, failure analysis workflows, and data-driven optimization techniques.
Architect a single chat agent that operates across web widgets, WhatsApp, Slack, and SMS by building a channel abstraction layer, message normalization pipeline, and channel-specific rendering adapters.
Learn battle-tested patterns for building production-grade tool-calling AI agents, including error handling, retry strategies, validation, and reliability engineering.
Learn how to build a multi-index RAG system that routes queries to appropriate collections, merges results, and normalizes relevance scores across heterogeneous document stores.
Design a user onboarding flow that takes customers from sign-up to a working AI agent in under five minutes, including template selection, guided prompt configuration, and first-conversation testing.
Build multi-layer validation systems using input guardrails, output guardrails, and tool-level guardrails in the OpenAI Agents SDK with composition, priority ordering, and custom tripwire behavior.
Synthetic data generation has become a core methodology for training competitive AI models. Learn how leading labs create synthetic training data, maintain quality controls, and avoid model collapse.
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