A 3072-dimensional embedding encodes everything about a video and distinguishes nothing. Decomposing content into named, measurable features, then placing them in a queryable hierarchy, is how multimodal search actually works at scale.
Text-only RAG pipelines miss 80%% of what is in your content. A video contains faces, dialogue, on-screen text, background music, and brand logos. No single embedding captures all of that. The solution is multi-stage retrieval.
Traditional taxonomies classify one content type at a time. Multimodal taxonomies unify classification across every format using embedding similarity the missing layer between raw AI features and structured, searchable metadata.
We compared 21 S3-compatible object storage providers across pricing, egress, features, and fine print. AWS S3 costs 15x more than the cheapest alternative for the same workload. Here's everything we found.
How we built an autonomous Kalshi trading bot using the Kalshi API and Mixpeek's video transcription, semantic search, and LLM data extraction no external tools required.
We are drowning in unstructured data — video, audio, images, documents, IoT — but our infrastructure still assumes everything is a row or a vector. The multimodal data warehouse is the missing layer: object decomposition, tiered storage, and multi-stage retrieval pipelines for the AI era.
We benchmarked every viable approach to multimodal document retrieval on financial tables (ViDoRe/TabFQuAD) and found a combination that hasn&
Every major IP enforcement tool finds violations after they're live. We built one that catches them before publication. Here's the architecture, the models, and what we learned.
We tested Gemini, Twelve Labs Marengo, X-CLIP, SigLIP 2, and InternVideo2 on text-to-video retrieval with graded relevance. The results surprised us.
Google's Gemini Embedding 2 embeds images, PDFs, and text together in a single API call. Here's how we integrated it into Mixpeek's feature extractor pipeline, the production numbers, and where multi-file embedding beats single-chunk approaches.
How we built query preprocessing into Mixpeek's feature_search stage — decompose a 500MB video into chunks, embed in parallel, fuse results. Zero API surface change for callers.
Sports broadcasters cut 4-8 hour editing sessions to 15 minutes using AI video analysis. Learn how to build automated highlight detection, archive search, and performance analytics pipelines for any sport.
We run 20+ ML models in parallel across video, image, and document pipelines. Here's the Ray architecture behind it -- custom resource isolation, flexible actor pools, distributed Qdrant writes, and the lessons we learned the hard way.
6,000+ ZIP codes straddle congressional district lines. At ZIP+4 precision, federal, state, and local disclaimer requirements can all apply simultaneously. Here's how multimodal AI solves what static rules engines can't.
Your Weekly Multimodal AI Roundup (Feb 9 - Feb 16)
Classify text, images, and video into 700+ IAB Content Taxonomy categories using multimodal AI. Learn how it works under the hood and how to extend it for your contextual targeting needs.
Instead of polling with an LLM on a cron schedule, Retriever Alerts evaluate semantic conditions at ingestion time. Vector math instead of inference calls. Event-driven instead of scheduled. Three API calls to set up.
Nurses spend 40% of their time on documentation. MDS coordinators abstract charts for 3-4 hours per assessment. PDPM revenue goes uncaptured. The root cause is that clinical documentation is inherently multimodal — and most tools only handle text.
How semantic chunking improves RAG quality by splitting content at natural boundaries rather than fixed token counts. Covers text, documents, video, and audio.
How agentic retrieval goes beyond traditional RAG by letting AI agents dynamically plan and execute multi-step search strategies with tool calling.
How AI-powered video intelligence extracts structured, searchable information from raw footage — covering scene detection, transcription, face recognition, and temporal indexing.
A clear comparison of keyword, semantic, and hybrid search with practical guidance on when to use each approach in production systems.
A practical guide to building search that works across text, images, video, and audio using shared embedding spaces and retrieval pipelines.
Feb 2 - 9: GPT-5.3-Codex and Claude Opus 4.6 handle full software lifecycles from debug to deploy, MiniCPM-o 4.5 beats GPT-4o on vision tasks at 9B parameters running on-device, HUSKY skateboards using physics-based control, and TinyLoRA fine-tunes models with a single parameter.
Deep dive into multimodal AI, data processing, and best practices from our engineering team.
