Guides
Vendor-neutral, engineer-written guides to the concepts behind multimodal AI: perception, retrieval, embeddings, and the infrastructure agents use to see, hear, and search unstructured data. Learn the idea first; then see how Mixpeek applies it.
107 guides across 18 topics
C2PA Content Credentials: How Media Provenance Actually Works
How C2PA Content Credentials record where a file came from and what was done to it, what hard and soft bindings are, why credentials disappear when a platform re-encodes an image, and what you can recover once they are gone.
How to Check if a Video Is Copyrighted
Five ways to check whether a video is copyrighted before you publish it, compared on what each one catches, what it costs and where it fails: YouTube Content ID, manual rights clearance, frame-by-frame reverse image search, audio fingerprinting, and automated multimodal scanning.
How Do You Detect and Redact PII in Images, Video, and Audio?
Text redaction is a solved problem and media redaction is not. A face is PII, a voice is PII, and so is the licence plate reflected in a shop window behind your subject. This works through what counts as identifying in each modality, why detect-then-blur leaves the original recoverable, how redaction interacts with an embedding you already computed, the irreversibility test that separates real redaction from a visual effect, and how to verify a redaction rather than trust the pipeline that claims it.
How Do You Delete Data From a Vector Index? Embeddings, Tombstones, and the Right to Be Forgotten
Deleting the source file does not delete the data. An embedding is derived data that outlives its origin, and most vector indexes mark a deletion rather than removing it. This works through the four places a deleted item still lives, what an ANN index actually does when you call delete, why a soft-deleted record can be invisible to reads and still block a re-create, whether an embedding can be inverted back into the thing it described, and how to verify an erasure rather than trust the API's 200.
Do You Need a Vector Database? When Brute Force, pgvector, and a Dedicated Store Each Win
Most teams reach for a vector database before the maths says they need one. This works out the actual thresholds: what brute-force NumPy costs at 10k, 100k and 1M vectors, where pgvector stops being free, what an ANN index buys and what it costs you in recall, and the four properties that genuinely force a dedicated store. Covers the memory arithmetic, why recall@k is the number that decides it, filtered search as the usual breaking point, and what changes when the vectors describe video or images rather than text.
How to Build a Taxonomy From Unlabeled Data: Clustering, Labeling, and Promotion
You have a million files and no categories. This walks the whole path: embedding the content, clustering it without guessing how many groups exist, naming what comes out, and the step almost everyone skips, which is deciding which clusters actually deserve to become categories. Covers HDBSCAN against k-means, why silhouette scores mislead on embeddings, exemplar-based labeling, promotion criteria, drift once new content arrives, and how the picture changes when the corpus is video or audio rather than text.
What Is Hybrid Search? BM25, Vector Retrieval, and How to Fuse Their Rankings
Why keyword and vector retrieval fail on opposite queries, what BM25 actually computes, and how Reciprocal Rank Fusion combines two rankings whose scores are not on the same scale. Covers the term-frequency saturation and length normalization inside BM25, why raw score addition breaks, RRF versus min-max and convex combination, how to pick the weighting, when hybrid is worse than either half, and what changes when one side of the index is video or images.
What Is MUVERA? Turning Multi-Vector Retrieval Into a Single-Vector Search
How MUVERA encodes a whole set of ColBERT-style token embeddings into one Fixed Dimensional Encoding whose inner product approximates the MaxSim score, so multi-vector retrieval can run on ordinary MIPS indexes. Covers the SimHash partitioning, the query-sum versus document-centroid asymmetry that makes the approximation hold, empty-cluster filling, how repetitions set the final dimension, and why the FDE is a candidate generator that still needs exact rescoring.
How Do You Run Your Own Model Inside a Managed Search Pipeline?
The four places a managed retrieval platform can let you run your own code (ingest-time extraction, query-time inference, reranking, and enrichment), what each one demands of the platform, and why the ingest and query sides must load the identical model or your vectors and your queries end up in different spaces. Covers the packaging contract, the cold-start and GPU-allocation problems that decide whether query-time custom inference is usable, versioning against an already-indexed corpus, and how to tell a real extension point from a webhook with good marketing.
How Do You Isolate Tenants in a Vector Index?
The three architectures for multi-tenant vector search (index per tenant, shared index with a tenant filter, and namespaces or partitions), what each costs, and the failure modes specific to each. Covers why a tenant filter is a correctness boundary rather than a performance knob, why post-filtering silently returns fewer results than you asked for, and why an unindexed tenant field can turn a working filter into an empty result set.
What Does It Cost to Make a Video Library Searchable?
A vendor-neutral cost model for turning raw video, images, and documents into a searchable index: the four cost centers (extraction, vector storage, serving, re-extraction), why per-feature extraction pricing stacks into the real bill, the arithmetic behind vector storage, and the re-extraction multiplier that decides whether you can ever upgrade your embedding model. With a worked 1,000-hour example using published July 2026 prices.
How to Search, Deduplicate, and Moderate AI-Generated Video and Images (FLUX 3, Kling, Veo, Runway)
AI video and image generators like FLUX 3 create content but do not search, deduplicate, or moderate the library you produce. A vendor-neutral guide to the retrieval layer that does: extraction (scenes, transcripts, faces, embeddings, fingerprints), semantic search, near-duplicate detection, pre-publication moderation, and clustering over your generated media in object storage.
From concept to production
These guides explain how multimodal perception and retrieval actually work. Mixpeek is the platform that runs them: point it at your storage and get back relevant, timestamped results.