What kinds of media can VideoVector analyze?

VideoVector is built for video, audio, and image libraries, including long-form broadcasts, surveillance footage, streaming catalogs, podcasts, and mixed media archives.

Can teams define their own extraction schema?

Yes. Teams can model structured outputs around their own taxonomy, compliance fields, editorial tags, or incident review requirements instead of relying on a fixed metadata format.

How do downstream systems receive results?

Teams can work in the UI, query results via API and SDK, or push processing outputs into their own workflows through automation, exports, and webhooks.

Is there both a self-serve and enterprise path?

Yes. Teams can start in the playground for fast trials, then move to a contact-led enterprise deployment for larger archives, integrations, and rollout planning.

VideoVector

AI media intelligence for video, audio, and image libraries

VideoVector turns large video, audio, and image libraries into structured metadata, timestamped evidence, multimodal embeddings, and grounded MediaRAG workflows teams can search, review, and use in production.

Try now Contact sales View documentation

Transform raw media into structured intelligence for search and discovery

Extract time-stamped metadata and asset-level analysis for downstream pipelines, user-facing search, discovery surfaces, recommendation engines, and automated review systems.

Custom media intelligence for domain-specific workflows

Align extraction fields, schema, and outputs to your media type, business rules, and downstream systems.

Contact sales Try now

Sports and broadcasting

Broadcast moment indexing

Detailed analysis schema

0:000:180:230:341:01

Your custom schema

Define the exact fields, taxonomy, and JSON structure your use case needs. Try now

Sequence Summary

Players from both teams skate and pass the puck around the neutral and defensive zones during a practice session.

Scene Type

normal_flow

Sponsor Or Brand Text

Uncertainty Notes

Exact purpose of the drill or scrimmage is unclear.

Visible Players

[0]

Role Or Action

skating

Name If Visible

АЛЕКСЕЕВ

Rink Location

neutral zone

Player Id

player_white_13

Puck Relationship

away_from_puck

Evidence Notes

Name and number clearly visible on the back of the white jersey.

Jersey Number

Team Id

team_white

[1]

Role Or Action

skating

Name If Visible

ЧОП

Rink Location

neutral zone

Player Id

player_blue_92

Puck Relationship

near_puck

Evidence Notes

Name and number visible on the back of the blue jersey.

Jersey Number

Team Id

team_blue

[2]

Role Or Action

defending net

Name If Visible

Rink Location

goal crease

Player Id

goalie_white_30

Puck Relationship

goaltender

Evidence Notes

Goalie in white jersey with number 30 visible on the sleeve.

Jersey Number

Team Id

team_white

Teams

[0]

Visible Player Count

Role In Sequence

unclear

Bench Or Ice Presence

ice

Evidence Notes

Players in white jerseys skating and passing.

Jersey Colors

white, blue, red

Team Id

team_white

[1]

Visible Player Count

Role In Sequence

unclear

Bench Or Ice Presence

ice

Evidence Notes

Players in blue jerseys skating.

Jersey Colors

blue, red, white

Team Id

team_blue

Event Details

Reaction

none

Event Outcome

play continues

Evidence Notes

General skating and passing during what appears to be a practice or warm-up.

Event Label

skating_and_passing

Scoring Or Chance Quality

not_applicable

Camera View

ice_level

Rink Zone

neutral_zone

On Screen Text

[0]

Text Type

jersey

Text

АЛЕКСЕЕВ

Associated Entity

White Team Player 13

Text Id

text_jersey_name_alekseev

Screen Location

player back

[1]

Text Type

jersey

Text

Associated Entity

White Team Player 13

Text Id

text_jersey_number_13

Screen Location

player back

[2]

Text Type

jersey

Text

ЧОП

Associated Entity

Blue Team Player 92

Text Id

text_jersey_name_chop

Screen Location

player back

[3]

Text Type

jersey

Text

Associated Entity

Blue Team Player 92

Text Id

text_jersey_number_92

Screen Location

player back

[4]

Text Type

jersey

Text

КРАСНАЯ ЗВЕЗДА

Associated Entity

White Team Goalie

Text Id

text_jersey_crest_krasnaya_zvezda

Screen Location

player chest

Highlight Value

Clip Start Cue

Reason

Routine practice play with no notable events.

Clip End Cue

Rating

none

Puck State

Location

neutral zone

Holder Player Id

unknown

Evidence Notes

Puck is passed between players but not always clearly visible due to camera angle.

Visibility

intermittent

Movement

passing

Segmentation

Segment-driven video analysis

Break long-form media into reliable review units. Each segment can carry structured JSON, searchable metadata text, embeddings, source timestamps, and a video-level rollup for the full asset.

Fixed

Uniform length

Split continuous footage into consistent intervals for surveillance, timelapse, manufacturing, infrastructure, and other streams where predictable chunks simplify review.

interval: 30s, 60s, 5m

Smart

Scene detection

Detect camera cuts, visual transitions, and meaningful boundaries using computer-vision signals such as gradient and histogram changes.

threshold: 0.3 - 0.9

Contextual

Story-aware analysis

Group footage by event progression, topic shifts, speaker context, and narrative structure for media workflows that need content-aware boundaries.

modes: fast, balanced, thorough

Search and workflow surfaces

Search every layer of the media record

VideoVector combines structured extraction, multimodal embeddings, analyst-friendly filtering, and agentic MediaRAG retrieval so teams can move from raw media to grounded answers faster.

Retrieval

Search by meaning, fields, evidence, and context

Combine semantic similarity with precise metadata filters, SQL-style analysis, and multi-step VideoRAG, AudioRAG, and ImageRAG search. Results stay connected to the source segment, extracted fields, and review window.

Vector search

Find conceptually similar moments across transcripts, visual descriptions, tags, and extracted fields.

query: "tense confrontation"

Multimodal search

Search with text, images, or reference frames when visual similarity matters as much as language.

input: reference_frame.jpg

Conditional search

Filter by exact structured fields such as severity, speaker, object class, rights status, or campaign label.

where: severity = "high"

SQL search

Give analysts direct query access to scoped media evidence, extracted fields, and prompt-run results.

sql: SELECT * FROM search WHERE severity = "high"

Agentic search

Run multi-step MediaRAG retrieval that can compare results, expand queries, and consolidate evidence across indexes.

agent: find_similar_incidents

Schema playground

Prototype extraction logic before production

Build, test, and refine media schemas against real assets. Teams can validate nested fields, prompt versions, indexed metadata, and model behavior before production rollout.

Nested objects

Model deep media structures without flattening key context.

Field controls

Mark fields required, optional, indexed, or used for summarization.

Embedding selection

Choose which metadata fields become semantic search surfaces.

Model modes

Tune runs for speed, quality, cost, or deeper reasoning.

Prompt versions

Track schema iterations as media operations evolve.

Contextual indexing

Preserve relationships between people, places, scenes, and events.

Automation

Run media intelligence as an operating workflow

Connect storage, schedule processing, trigger prompt runs, publish results, and keep external systems updated through APIs, SDKs, webhooks, and governed workspaces.

Cloud storage

Connect S3, GCS, Azure, R2, and existing media buckets.

Job scheduling

Run batch jobs, scheduled processing, and trigger-based imports.

Webhooks

Send completion events and structured outputs to downstream systems.

Enterprise auth

Support SSO, RBAC, audit trails, and governed workspace access.

REST API

Create indexes, prompts, runs, searches, and exports programmatically.

Python SDK

Integrate media intelligence into notebooks, data pipelines, and services.

How it works

From prompt design to searchable media operations

VideoVector connects prompt definitions, media indexes, automated analysis, structured outputs, webhooks, cloud exports, and search in one production media intelligence pipeline.

Define

Set the extraction logic, output shape, and search surfaces before processing begins.

Prompt definition

Describe the signals, events, entities, and summaries each run should produce.

Segment output schema

Capture structured fields for each reviewable segment.

Video-level output

Roll segment findings into a complete asset-level result.

Semantic search fields

Choose which metadata fields become vector-searchable.

Ingest

Bring source media into governed indexes from manual uploads or existing storage systems.

Index creation

Organize media collections, prompt runs, search scopes, and permissions.

Manual upload

Add files directly when teams need controlled review or one-off analysis.

Cloud connector

Import from GCS, S3, Azure, R2, and production media storage.

Analyze

Run extraction across selected media, complete indexes, or automated intake pipelines.

Prompt run

Execute selected prompts against an index, folder, asset, or batch.

Segment analysis

Generate structured evidence for each scene, interval, or contextual unit.

Video synthesis

Create consolidated summaries, classifications, tags, and report-ready fields.

Automation

Watch storage locations, import new assets, and trigger analysis workflows.

Deliver

Send structured media intelligence to the people and systems that need to act on it.

Segment results

Review timestamped JSON, tags, confidence, and evidence ranges.

Asset results

Use the video-level output as the canonical record for the full media asset.

Webhook notification

Notify downstream systems when processing completes or errors require attention.

Cloud export

Write results back to storage, analytics workflows, catalogs, and partner systems.

Search

Query across indexes, extracted fields, media segments, embeddings, and prompt results.

Scope selector

Search one index, multiple indexes, or a curated media set.

Agentic search

Use multi-step retrieval to compare evidence and consolidate answers.

SQL search

Run structured analysis over scoped results and extracted fields.

Vector and condition search

Combine semantic similarity with exact filters for high-confidence retrieval.

Pricing

Start free, upgrade through Stripe checkout, or scope an enterprise deployment with custom controls.

Free

A workspace for trying core search, prompt runs, and early schema design.

No checkout required

10 video processing credits included

Entry-level API and processing throughput

Core search and processing workflows
Prompt runs for validation projects
Community support

Try Free

Starter

For teams moving from evaluation into repeatable processing and API work.

Popular

$49/mo

Stripe monthly subscription

500 video processing credits included

Higher API and processing throughput

Connector, webhook, automation, and API key access
Email support
On-demand credit purchases

Choose Starter

Pro

For production teams that need larger self-serve credit allocation and priority help.

$149/mo

Stripe monthly subscription

2,000 video processing credits included

Highest self-serve API and processing throughput

Priority support
Bulk review workflows for larger libraries
Larger recurring credit allocation

Choose Pro

Enterprise

For governed deployments with custom rollout, infrastructure, and workflow requirements.

Custom commercial terms

Custom video processing credit allocation

Enterprise API, search, and processing limits

Bulk processing and archive-scale onboarding
Upstream and downstream workflow customization
Dedicated instances, SLAs, and support alignment

Contact sales

Solution paths

Find the right VideoVector workflow

Technical solution

Video Vector Embeddings

Create multimodal video embeddings from transcripts, visual context, metadata_text, and structured outputs for search-ready libraries.

Technical solution

Video Vector Embedding Search

Hybrid media retrieval combines vector search, metadata filters, SQL media search, and agentic discovery across large libraries.

Questions teams ask before rollout

These are the most common evaluation questions we hear from teams planning archive, security, and workflow deployments.