AI System Architectures

Architectures where the primary interface is natural language dialogue — the system must maintain context, manage turns, and produce coherent responses across multi-turn exchanges

AI embedded directly in professional tools at the point of work — surfacing suggestions, generating content, and automating repetitive steps without requiring the user to leave their primary application

AI systems that pursue goals independently — planning actions, using tools, and executing multi-step workflows with minimal human intervention. Human provides the goal; agent determines and executes the path

Batch and stream processing architectures that apply AI transformations to data at scale — ingestion, enrichment, generation, and media analysis

Proven architectural patterns for composing AI capabilities into search and discovery experiences — from semantic retrieval to proactive recommendation and structured graph exploration

AI systems that continuously observe data streams, flag anomalies, and surface actionable alerts. Includes anomaly detection, fraud, content moderation, visual inspection, and security threat detection

Architectural patterns for systems that produce forecasts, scores, allocations, or intervention recommendations — turning historical and real-time signals into actionable numeric or probabilistic outputs