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
Build an AI agent that tracks feature usage, identifies underutilized capabilities for each user, and delivers contextual tips to drive adoption and reduce churn risk.
Build an AI agent that extracts key concepts from study material, generates effective flashcards, and schedules reviews using the SM-2 algorithm with performance analytics.
Build an AI agent that identifies overdue patients, runs multi-step communication sequences to bring them back, and tracks reactivation success rates with real Python implementation code.
Build an AI voice agent that takes restaurant food orders over the phone, handles menu customizations, confirms orders accurately, and integrates with POS systems for seamless fulfillment.
Build intelligent form auto-completion that predicts field values from context, validates entries in real time, and lets users override every suggestion with a single keystroke.
Build a hybrid lead scoring system that combines traditional ML feature engineering with LLM-based qualitative analysis for more accurate prospect ranking and CRM integration.
Build an AI notification system that scores alerts by importance, selects the right delivery channel, bundles related notifications, and learns from user engagement patterns.
Build an AI onboarding agent that adapts to each user's role, experience level, and goals to guide them through your SaaS product with personalized walkthroughs and recommendations.
Build semantic search for your SaaS product using vector embeddings, enabling users to find records by meaning rather than exact keyword matches.
