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Evaluates logo usage, color accuracy, font adherence and layout rules against your brand style guide. Returns compliance_score, violation_list, recommended_action.

Checks content against ESRB, PEGI, CERO frameworks and territory-specific rating requirements. Flags content that would require a rating change or breach a distribution agreement.

Validates that character appearances align with official model sheets, flags color palette deviations beyond defined tolerance, and catches scenes where a character appears in a context that conflicts with their canonical personality profile.

Cross-references asset metadata against the active licensing agreement database. Flags any asset containing elements not cleared for the targeted distribution channel.

Flags third-party IP, PII risks (faces without releases, visible license plates), missing legal lines and missing disclosures. Reduces attorney scanning to reviewing only the flagged items.

Reviews caption and dubbed audio tracks for brand-tone compliance and territory-specific restrictions. Works directly against caption and audio track data already managed in Ceivo.

Applies a separate evaluation standard for competitive content: broadcast graphics, tournament branding, player imagery rights and sponsor placement rules specific to the competitive ecosystem.

Pre-checks commercial advertising clearance submissions against broadcaster rules. Validates continuity, ratings, product claims and required disclosures before a submission reaches human reviewers.

Lets users search the library with queries like "practice sessions" or "qualifying highlights". The agent translates intent into the right underlying searches and returns ranked results with timecodes.

Takes descriptive prompts ("two people at a bar with dramatic lighting," "fight scene with rain") and returns ranked scene matches across the entire archive. Built on TwelveLabs Marengo embeddings.

Given a channel concept like "I'm launching a new sports channel," interrogates the library, categorizes content by type, and generates suggested slogans, channel descriptions and a content slate.

Compiles a catalog of every person referenced across the library, organized by show, sponsor and mentions. Proven at scale: 130+ people extracted from a mid-sized sports library in a single run.

Given a reference scene or file, returns "more like this" based on visual similarity, semantic similarity or mood match. Excludes same-video matches by default.

Scores scenes and thumbnails for fitness for specific downstream uses (social promo, trailer, broadcast). Surfaces visual contrast, motion, composition and rating signals.

Existing Ceivo promo-orchestrator pattern; proven on holiday TikTok and F1 compilation workflows. Takes a brief like "make me a 22-second TikTok promo for 3 movies coming in February" and returns a ranked shortlist of candidate scenes plus suggested motion prompts, aspect ratios and pacing.

Detects peak moments (goals, crashes, finishes, key plays) and assembles social-ready clips with platform-specific aspect ratios. Reference pattern: F1 checkered-flag compilation for TikTok.

Narrative-aware agent that pulls emotional beats and key story moments into a trailer arc. Different scoring criteria than promo (sustained tension vs. instant punch).

Surfaces atmospheric content with consistent tone: ambient B-roll for documentaries, lifestyle content or brand videos.

Interview shots, establishing shots and archival B-roll selected against a documentary outline.

Converts any agent's clip selection into a timeline-ready EDL for Adobe Premiere, DaVinci Resolve or Avid.

Answers queries like "what content is available for a specific territory from March to June 2026?" by querying Ceivo's library against your Rights Tracker. Produces sales-ready avail lists with reasons for unavailability.

Federated search across multiple operating-company libraries so a parent organization with 50+ opcos can see what exists where without centralizing storage.

Finds where the same content exists multiple times across the organization (a common pattern in multi-opco groups where the same file can sit in 50+ places). Recommends canonicalization.

When marketing grabs clips from a primary MAM and creates derivatives, this agent maintains the parent-child relationship back to the source material and its contracts, which is critical for post-hoc rights queries.

Cross-references people, brands and product placements detected in content against the active agreements database and flags clearance gaps before distribution.

Existing Ceivo agent framework pattern. Triggered when new files land in S3. Runs frame-by-frame technical QC (black frames, audio dropouts, resolution mismatches, codec compliance) and reports a pass/fail verdict and detailed issue list back to Ceivo.

Identifies and timestamps all faces in a video against a roster you provide. Example: "23 instances of target subject detected." Powers talent cataloging, releases verification and people-based search.

Segments video into logical scenes, classifies each by type (interview, action, establishing, dialogue), and surfaces results as structured metadata. Runs on ingest for every video.

Auto-generates structured metadata (topics, entities, settings, sentiment, content rating) on ingest. Feeds downstream search, compliance and recommendation agents.

Validates caption files against the audio track, flags misalignments, timing drift and missing speaker labels.

Validates integrity, metadata completeness and duplicate detection during bulk migrations off legacy platforms (Curio, Ateliere, MediaValet, Iconik).

Full-text extraction plus OCR for scanned PDFs, layout-aware parsing for columns and tables, and semantic indexing so PDFs participate in natural-language search alongside video.

Validates print-ready PDFs (packaging, collateral, TCG card proofs, brochures) against the same brand style guide used for video: color accuracy, logo usage, typography, bleed and trim, required legal lines.

Ingests script PDFs, extracts scenes, characters, dialogue and locations as structured data, then cross-references against produced video. Answers "did we shoot every scene in the script" and "which version of this line made the cut?"

Parses rights contracts and licensing agreements, extracts structured terms (parties, territories, windows, holdbacks, restrictions), and feeds them into your Rights Tracker or directly into the Avails Agent.

For regulated broadcast workflows, parses clearance submissions and regulatory PDFs, validates required fields are present, and ties each document to the video asset it governs.

Visual embeddings and OCR on still images (JPG, PNG, TIFF, WebP), making stock photos, key art, production stills and social assets searchable with the same natural-language interface as video.

Orchestrates agents across formats. Example: parse a sponsorship contract PDF, pull the name list, run face and transcript detection across the video library, return matched clips with timecodes.

For podcast libraries, radio archives and audio-only masters: transcription, speaker diarization, topic extraction, music vs. speech classification and ad-break detection.

Scans a list of off-platform URLs you supply, uploads and partner-provided feeds for unauthorized use of your IP, and matches findings back to Ceivo-managed source material.

Takes hits from OSINT tooling or approved content-ID partners, matches them against the content relationship graph to identify the source file, and routes a ticket to the brand-protection team.

Pulls competitor content from public sources, indexes it in a separate governed library, and surfaces emerging creative trends without bringing unowned content into the main archive.

Existing Ceivo LLM-compare tool pattern. Runs the same prompt across multiple models (Anthropic, OpenAI, Bedrock, TwelveLabs Pegasus) against a slice of the library, captures cost/tokens/latency, and surfaces a human-in-the-loop rating interface.

Scans the MCP Policy Engine audit log for unexpected model calls, cost anomalies or attempted policy violations. Generates a weekly governance report.

Caps agent runs against a budget envelope, routes expensive workloads to cheaper models when appropriate, and alerts on unexpected consumption spikes.