Engineering practices for production multimodal AI systems, covering image-text understanding, voice agents, video generation, cross-modal retrieval, multimodal RAG, native multimodal models versus pipelines, latency optimization, WebRTC, observability, and safety review.
Build a production multimodal document pipeline with immutable inputs, layout-aware parsing, OCR or VLM routing, evidence-linked fields, reproducible evaluation, bounded retries, secure processing, and measurable cost per accepted page.
Design and evaluate production Multimodal RAG retrieval without mixing incompatible scores or repeating unsupported winner claims. This guide separates image-text embeddings, visual document late interaction, lexical and layout retrieval, explains reciprocal rank fusion in Go, and defines provenance, access control, labeled slices, retrieval metrics, answer grounding, drift monitoring, and rollout gates.
Build reliable AI video APIs with durable jobs, signed webhooks, polling reconciliation, idempotent retries, safe asset ingestion, cost controls, and SLOs.
Engineer low-latency voice agents with explicit turn commits, playback-aware barge-in, reliable timing, WebRTC traces, safety controls, and task-level evaluation.
Choose between native multimodal APIs, modular perception pipelines, and hybrid systems using task contracts, evidence needs, failure isolation, serving topology, privacy, latency, and cost per accepted outcome instead of model labels or generic benchmarks.
Build an auditable AI image understanding pipeline for OCR, document parsing, structured extraction, and visual question answering, with evidence contracts, calibrated confidence, security controls, evaluation gates, and runnable Go validation code.
A production-oriented deep dive into 3D generation and world models. Covers NeRF, Gaussian Splatting, text-to-3D, video world models, Sora-style simulators, World Labs spatial intelligence, evaluation metrics, and engineering patterns for spatial AI systems.