Multimodal AI Engineering

Build production multimodal AI pipelines for image-text understanding. Covers VLM architecture, OCR, document parsing, and structured extraction with code.

A production-grade guide to advanced Multimodal RAG systems. Covers cross-modal embedding alignment (CLIP, SigLIP, ColPali), hybrid image-text retrieval pipelines, late-interaction architectures, re-ranking strategies, and end-to-end Python/TypeScript implementations with benchmark comparisons.

A production engineering guide to AI video generation APIs in 2026. Covers Google Veo 3, Kuaishou Kling 2.0, Runway Gen-4, and Pika 2.0 API integration with quality evaluation frameworks, cost optimization, prompt engineering for video, and automated pipeline design.

A production engineering guide to real-time voice AI agents. Covers streaming ASR, turn detection, low-latency LLM orchestration, TTS streaming, barge-in handling, WebRTC architecture, observability, and Python/TypeScript implementation patterns.

A practical architecture comparison of native multimodal models and modular pipeline systems. Covers GPT-4o/Gemini-style unified models, OCR + ASR + VLM pipelines, latency, cost, observability, reliability, compliance, and migration patterns for production AI systems.

A production guide to AI image understanding pipelines. Covers OCR, layout analysis, document parsing, visual question answering, structured extraction, confidence scoring, human review loops, and Python/TypeScript implementation patterns.

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.