AI agents that can actually do the work — under your governance.
Every agent on CEIVO has a name, a job, a defined set of granted skills and a logged trail. Bring your own framework. Keep your enterprise standards. We participate as the governance layer.
Agents you can trust to act.
CEIVO doesn't replace your agent stack — it gives every agent a governed surface to work against. Triggers kick off real work. MCP makes every call traceable. A2A keys and granted skills mean an agent only ever sees what you've authorized. The output flows back through the API and surfaces in your library.
An agent execution layer with the governance built in.
Four properties make every agent on CEIVO defensible, predictable and ready to extend — whichever runtime it lives in.
MCP-governed access
Every tool call an agent makes flows through the MCP Policy Engine. Tenant-scoped, model-aware, fully logged and queryable. If it isn't logged, it didn't happen — and on CEIVO, everything is logged.
Granular skill grants
Each agent gets an A2A key bound to a named role. The role declares exactly which Agent API skills the agent can call. No implicit access. No ambient credentials. No "the agent could read everything because it had the user's token."
Framework agnostic
Run agents in Databricks, Foundry, Bedrock AgentCore, CrewAI, n8n, Windmill, LangChain — or your in-house runtime. CEIVO is the governance and data layer underneath; the runtime stays your call.
Triggers that fit your workflow
User-invoked from the CEIVO UI. System events on ingest, transcoding or metadata change. Review-and-approve gates that wait for a human signal. Whatever fires the work, the trail starts the same way.
Agency without governance is a security incident waiting to happen.
Every enterprise agent program eventually meets the same three questions from security, legal and infra. CEIVO answers them up front.
From trigger to update — in six governed steps.
Same shape every time. Whether the agent is built on Bedrock AgentCore or n8n or your own Python service, the surface CEIVO sees is identical — which is what makes the whole thing auditable.
A user invokes an agent from the CEIVO UI, a system event fires (ingest, transcode, metadata change), or a review-and-approve gate completes.
CEIVO calls the agent's webhook endpoint with the trigger context, the requesting identity and a one-time correlation ID for the run.
The agent opens an MCP session back to CEIVO using its A2A key. The session is bound to the agent's role — every call is attributed and logged.
The agent can only see and call the Agent API skills its role grants — search, read, tag, create, trigger workflows. Anything outside scope is denied at the policy layer.
The agent reasons, composes calls and produces a result — using your framework of choice. CEIVO doesn't dictate the runtime; it governs the surface.
The agent writes back through the Agent API — tags applied, documents created, workflows started, status returned. Activity surfaces in the CEIVO UI and the audit log.
We integrate with your enterprise standard. We don't replace it.
We've shipped agents on every major framework. Customers retain the freedom to define corporate-wide agent standards — CEIVO participates as the governance and data layer underneath whichever runtime your enterprise has chosen.
A starting library — extend or replace any of them with your own.
Every agent below is shipped as a structured, plain-language instruction set: versioned, inspectable, owned by you. Clone one and modify it, swap its runtime, or stand up a brand-new agent against the same MCP and Agent API surface. Nothing here is opaque.
🛡️ Compliance & Brand Governance
Most platforms make brand compliance a service you subscribe to. CEIVO makes it a capability you own — when a new franchise launches or a new rule appears, you update the agent the same day.
Verifies brand work is right by design — the logo IS in the safe area, the colors ARE within ΔE tolerance of the approved palette, the character IS on-model, and forbidden elements are absent. Combines entity search, color-distance checks, logo-zone validation and model-sheet proportion analysis to turn manual episode-by-episode reviews into structured findings reviewers act on in minutes.
Cross-checks the rating logo or title card actually shown in a video against the published rating from TMDB across every certification system (MPAA, BBFC, FSK, ACB, ESRB, PEGI, CERO). Surfaces three classes of finding — wrong rating displayed, missing rating card, or unexpected rating card on unrated content — with parallel scene-thumbnail analysis and head/tail text search.
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.
Static-image companion to Brand Visual Compliance. A poster, key-art and partner-creative gate that runs ten checks against any supplied still: logo presence and variant, safe-area placement, brand-color ΔE conformance, approved typography, on-model character likeness, AI-generation tells, flipped-image detection, copy and claims accuracy, delivery-spec format, and manipulation artifacts. Findings land as a linked JSON sidecar plus tags so QC teams know which partner-supplied creative to send back before it reaches the broadcast pipeline.
Consolidates three manual review tasks into one automated pass: character faces aren't cropped, the correct rating logo is present and matches the published rating, and no unauthorized stock footage or unlicensed watermarks are embedded in the deliverable. Rating-logo verification — currently a manual frame-by-frame check — becomes instant.
Verifies that the spoken language and on-screen text in a delivery match the expected language for that version. For a 30-language batch, processes every version in minutes and flags only the mismatches that need human attention.
Surfaces potential defamation moments — false statements of fact about identifiable real people or organizations, presented as fact, that could harm reputation. Each finding carries a 0–10 score, the verbatim quote, the named subject, the claim type and the rule reference, so a human reviewer can route to legal counsel.
Flags identifiable people captured without consent, minors in frame, readable license plates, addresses, sensitive on-screen documents, medical info and locations with elevated privacy expectations like homes, hospitals, schools and locker rooms. Each candidate is surfaced with severity and a specific subcategory so the reviewer can route to compliance or legal as needed.
Flags culturally insensitive content — slurs, stereotypes, sacred-imagery misuse, regional offense, problematic identity portrayal — with a 0–10 score, verbatim quote or scene reference, and offense category, so the reviewer can route to a regional or cultural advisor. Per-market sub-policies are forward-compatible.
Catches the regulated-content categories the broadcast frameworks miss — medical and health claims, financial and investment claims, legal-advice claims, age-gated content disclosures, and missing-required-disclaimer detection. Sits alongside GARM/FCC/YouTube/MPAA/TikTok/OFCOM as the claim-based regulation layer, surfacing every candidate so legal can confirm substantiation or required disclaimers.
Reads every compliance and legal-risk marker on a file, groups them by category, prioritizes by severity, and renders a polished per-file PDF with a Broadcast Compliance section and a separate Legal Risk section that surfaces score, verbatim quote, subject, claim type and recommended action. The PDF lands as a linked sidecar so reviewers see one human-readable rollup instead of scrubbing the timeline marker by marker.
🔍 Discovery & Intelligence
Find anything, in any format, in plain language — backed by best-in-class video understanding and CEIVO's unified metadata layer.
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.
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.
Action-oriented sibling to the Talent Catalog. Finds every appearance of a named person, character or entity across the library by combining IMDB/TMDB lookup (actor name → character name), transcript NER, scene-description matching and entity search against reference headshots. Results write back as markers and a Ceivo playlist that loads directly into Premiere.
Finds story-level transitions, act breaks, cold opens, credits and natural ad-insertion points by fusing visual cuts, audio silences and semantic chapter analysis. Selectable modes for ad-break placement, news-story segmentation, or structural detection, with EDL/XML/JSON export to NLEs and playout systems.
Scans incoming reality and unscripted dailies for sit-down interviews — single person seated, direct-to-camera framing, consistent lighting and background — then identifies the cast member and builds a per-talent playlist that drops straight into the Adobe Premiere panel. Replaces the manual "someone watches everything and pulls selects" step that always runs behind the shoot schedule.
Discovers every piece of on-screen text — titles, lower thirds, signage, UI, embedded text — using a two-stage scene scan plus dedicated OCR, then exports a structured inventory with bounding boxes, frame thumbnails and detected languages for translation teams.
🎬 Creative & Production
Promo, trailer, highlight and mood-piece workflows that take a creative brief and return a ranked, timecoded shortlist — or an EDL ready for the editor.
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.
Pulls any subset of a file's accumulated markers — by category, by severity, or all of them — and emits a CMX 3600 EDL ready to import into Premiere, Resolve or Avid. Editors load only the slice they need ("just the TikTok findings", "just HIGH-severity legal flags") and act on timecoded notes inside the NLE instead of jumping between tools.
📜 Rights, Avails & Lineage
The hardest "Do we have the right to use this?" questions, answered automatically — against your own contract system.
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 — 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.
Builds a complete music map of any file — every identified song, artist, ISRC and exact in/out timecode — by chunking audio and submitting fingerprints to a recognizer (AudD in v1, ACRCloud / Gracenote / Audible Magic via the pluggable backend in v2). Detections write back to a dedicated Music_ID timeline track in Ceivo so rights and clearance teams retire manual Shazam passes and cue-sheet cross-referencing.
Aligns a duration-only PRO cue sheet (RapidCue and similar) to the actual program audio so each cue lands on the correct timecode in Ceivo — fingerprinting recurring stems (main-title bed, opening theme, transition stingers) as anchors, then aligning the remaining cues against detected music regions. Bridges the gap between PDF cue sheets and the timecoded markers downstream rights and music-supervision teams need.
Generates a MovieLabs OMC v2.8 JSON sidecar from Ceivo's AI analysis — turning scenes, transcripts, tags and technical metadata into the industry-standard ontology used for the 2030 Vision supply chain. Produces structured CreativeWork, Asset, Participant and MediaCreationContext entities ready to feed a 2030-compliant MAM, distribution pipeline or downstream studio system.
🔧 Ingest & QC
Automated technical and content QC the moment a new file lands — with structured outputs that downstream agents can act on.
Existing CEIVO agent framework pattern. Detects the QC failure patterns that make 50–100-version delivery batches impossible to review manually — black and frozen frames, rendering artifacts, aspect-ratio anomalies, missing head/tail content. Flags each issue as a timecoded marker in Ceivo and the Adobe Premiere panel so editors jump straight to the problem instead of scrubbing through hours of footage.
Identifies and timestamps all faces in a video against a customer-provided roster. 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).
Watches every ingest source — watch folders, SharePoint, Frame.io, direct upload — and automatically files new assets into the right project folder, applies standardized project/language/version metadata, and adds them to the appropriate review playlist. Replaces the manual "someone moves and renames the files" step with configurable per-project routing rules.
Runs an AI first pass on every new asset to catch the obvious failures — black frames, missing audio, language mismatch, missing end cards or rating logos — before a human reviewer ever opens the file. Passes route to "Ready for Review", fails bounce back to production with timecoded markers, and brand and legal reviewers only see assets that have already cleared a basic checklist. Cuts sequential review queues dramatically.
Catches audio defects on ingest — muted tracks, mono-when-stereo deliveries, clipping, silence gaps and audio/video sync drift. Configurable thresholds let you tune the QC bar per project (broadcast vs. social), and findings land as timecoded markers and tags directly on the file so post catches issues before they cause re-deliveries.
Submits a Ceivo file to Interra Systems Baton against a customer-specified Test Plan, polls for completion, then maps every loudness violation, freeze frame, codec issue and PSE flag back into Ceivo as markers on a dedicated Baton QC track. The raw Baton JSON report is uploaded as a linked sidecar so editors get file-based QC findings exactly where they're already working.
A generic, config-driven engine that validates a mezzanine against a customer's house specification — codec, container, resolution, frame rate, audio channel layout, timecode start, head/tail handles. Loads a named YAML spec, tags the file housespec_ok or housespec_fail, and returns the closest-matching variant plus a human-readable list of which checks failed and how.
Pre-screens animation deliverables for rendering errors, incomplete compositing, duplicate-frame drops, color banding, transparency artifacts and missing elements — including compositing-boundary quality on mixed live-action/animation content. Replaces the email-thread review process with structured timecoded findings so reviewers focus only on flagged work.
Identifies visible PII on location footage — non-consenting faces (cross-checked against the cast list), readable license plates, on-screen documents, phone numbers on whiteboards — and writes precise timecoded "needs blurring" markers plus a PII Review playlist for the post team. Legal can verify PII handling without watching everything.
📄 Document & Multi-Format
When a workflow needs to reach beyond video — scripts, contracts, brand guidelines, print proofs — CEIVO can bring documents and reference images alongside the video so an agent can use them as context.
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.
Compares proof rounds against each other, against spec sheets and against vendor-supplied contract proofs — pixel diff, layout-shift detection, text changes, color drift, bleed and safety zones, required legal text placement, barcode/UPC presence. Estimated to cut proofing time in half on packaging products with hundreds of components across multiple rounds.
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.
📡 Monitoring & Brand Protection
Keep watch for unauthorized use of your IP across sources you can legitimately monitor — and tie findings back to the source material in your library.
Scans a customer-supplied list of off-platform URLs, 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.
🧪 Evaluation & Governance
Pick the right model per use case, audit every model call, and cap consumption against an explicit budget envelope.
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.
A lightweight, password-protected, read-only browser for every agent's deployed prompt and rule files. Pick an agent, pick a module, read the rule. Reflects the live state of the agents repo so prospects, partners and compliance reviewers can see exactly what each agent "knows" without touching the codebase.
💬 Interfaces & Notifications
Chat interfaces and real-time surfaces that sit on top of the agent layer — so the people who consume agent output get it in the tools they already use.
A Claude-powered Slack bot that posts run cards to DMs and subscribed channels when agents finish, then opens a thread where users can ask "why did this fail," "what mezzanine spec was expected," or "what files are ingesting right now" — backed by the Ceivo MCP, the ceivo-api skill and an agent-run lookup tool. Org-scoped memory carries preferences and conventions across conversations.
Architect questions, answered up front.
The questions security, infra and AI/ML teams ask before they sign off on an agent program. If yours isn't here, send it via the contact form and we'll come back with a written answer.
Ready to put agents to work — under governance you can defend?
We'll walk through the trigger model, the MCP surface, your framework choice, and what it would take to stand up your first three agents.
Talk to us about agents