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Normalizes caller-supplied blink deviation, lip-sync error, spectral discontinuity, compression inconsistency and metadata anomaly using declared fixed weights. It does not inspect media, establish authenticity, identify a generator or replace human forensic review; the result is only as reliable as

Normalizes whitespace, inserts a caller-supplied visible marker after every selected number of words and at the end of a final partial interval, then reports source and output digests. This deliberately visible text annotation is not a robust image, audio or cryptographic watermark and can be remove

Requires shares totaling exactly 10,000 basis points, floors each proportional minor-unit allocation, and distributes leftover units by descending fractional remainder with a stable name tie-break. It performs arithmetic only and does not transfer funds, interpret contracts, withhold tax or resolve

Tokenizes caller-supplied campaign and trend keywords, calculates overlap relative to the trend vocabulary, and flags any supplied blocked keyword. It neither discovers live trends nor recommends deception, impersonation or spam; its scoped purpose is a transparent relevance and brand-safety screen.

Converts caller-supplied MIDI-note sequences to adjacent pitch intervals, forms fixed-length interval n-gram sets and reports Jaccard overlap. It does not analyze rhythm, harmony, timbre, recordings, access to prior work or legal substantial similarity, and cannot determine plagiarism.

Validates frame/channel dimensions, clamps weights to 0–1 and applies exponential smoothing over time in each named channel. It does not infer a face, build a rig, retarget skeletons, process video or replace animator review.

Sorts caller-supplied placement opportunities by end time and solves the weighted non-overlapping interval problem exactly with dynamic programming. It does not buy inventory, predict conversion, enforce platform policy, model audience harm or guarantee campaign performance.

Weights likes, comments, shares, saves and clicks, divides by supplied reach, and reports cohort and aggregate points per thousand reached accounts. It does not access a platform, distinguish authentic from manipulated activity, measure sentiment or predict revenue.

Updates one shared beta prior with each variant’s supplied clicks and non-clicks, reports posterior means and bounded normal-approximation 95% intervals, and ranks by posterior mean. It does not run an experiment, correct assignment bias, prove significance or predict future performance under distri

Calculates cosine similarity between caller-supplied numeric embeddings and combines it with supplied spectral distance, pitch-contour correlation and timing distance using fixed weights. It processes no audio and cannot establish speaker identity, consent, cloning, authenticity or fraud.