VectorMethods
Media analysis visualization

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.

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.

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
13
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
92
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
30
Team Id
team_white
Teams
[0]
Visible Player Count
4
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
3
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
13
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
92
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.

Segmented video analysis timeline

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.

Multimodal media search visualization

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 interface

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.

Automated media processing workflow

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.

Media intelligence workflow diagram

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.

01

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.

02

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.

03

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.

04

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.

05

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.

$0

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

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

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

Enterprise

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

Contact us

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

Solution paths

Find the right VideoVector workflow

VideoVector supports core platform capabilities, industry-specific workflows, and production playbooks for teams turning large media libraries into structured intelligence, agentic MediaRAG, and grounded retrieval applications.

Browse all solutions

Solution categories

Questions teams ask before rollout

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