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
Implement cost allocation for enterprise AI agents with per-department chargebacks, showback reporting, budget management, and automated alerts. Learn how to track LLM token costs, infrastructure expenses, and generate financial reports.
Break down the true cost of running AI agents in production, from token costs and tool invocations to infrastructure and storage. Learn to identify the biggest cost drivers and build a cost model for your agent systems.
Build an AI course creation agent that generates curriculum outlines mapped to learning objectives, drafts lesson content, and designs aligned assessments — all from a topic description.
Build an AI agent that performs initial crisis assessments, matches callers with appropriate resources, and executes warm handoffs to trained counselors — with safety-first design principles.
Build an AI onboarding agent that guides new customers through product setup, tracks their progress, offers contextual help, and optimizes for activation metrics.
Build an AI agent that monitors PostgreSQL performance, detects slow queries, recommends optimal indexes, schedules vacuum operations, and plans for capacity growth.
Build an AI agent that automates dental insurance verification by integrating with payer APIs, parsing complex plan structures, and explaining coverage details to patients in plain language.
Build an AI agent that generates professional documents like contracts, proposals, and reports by combining template engines, dynamic data injection, and PDF rendering with version tracking.
Build an AI agent that helps electrical contractors assess job scope, track permit applications, verify code compliance, and manage crew scheduling across multiple active projects.
