VectorMethods
Media analysis visualization

VideoVector

AI media intelligence for video, audio, and image libraries

VideoVector transforms large video, audio, and image libraries into structured metadata, timestamped contextual intelligence, multimodal embeddings, and grounded MediaRAG workflows for search and discovery.

Transform raw media into structured intelligence

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

Find the exact moment that matters

Combine vector search, semantic search, natural language retrieval, SQL analysis, conditional search, multimodal embeddings, visual references, and multi-step VideoRAG, AudioRAG, and ImageRAG search. Every result stays traceable, explainable, and grounded in the source segment, extracted fields, and timestamped evidence.

Text query

Officers arresting a tall middle-aged man
6 segments
Refine
Blue number 88 scores through the Short-side gap; goalie looks disappointed at defenders
Moment found inside game archive

Segment by story, signal, or time

Choose segmentation by narrative reasoning, adaptive signal detection, or fixed intervals, then turn continuous video into precise, timestamped structure for search, enrichment, and downstream media workflows.

Narrative-aware splits

Create boundaries from story, context, and instructions

The model understands the full asset, its context, and its nuances, then follows segmentation instructions to choose boundaries from narrative shifts, scene changes, plot progression, character arcs, topic changes, or custom criteria. Optimized for videos up to two hours.

Ride out with John
Reach the train route
Intercept the train
Board and search the train
Survive the response
Regroup after the robbery
0:002:405:108:0010:1212:3014:51.4
Establish the train robbery plan while the gang moves toward the intercept point.
Travel and environmental transitions move the player from setup into the train interception area.
Nighttime visuals and audio shift into the train sequence as the robbery target enters the scene.
The mission changes from travel into active robbery as the player boards, searches, and repositions through the train.
The pace escalates into sustained combat and escape activity as the robbery draws resistance.
The mission winds down through final movement, late action beats, and transition away from the train sequence.

Connect your cloud storage. Automate your media pipeline.

VideoVector supports GCP, AWS, and Azure cloud storage out of the box. Connect your storage bucket once, and every new media file is automatically processed, enriched with structured metadata, and delivered to your downstream systems through customizable webhooks.

Your cloud storage

connected media buckets

gs://media-ops/scheduled_feed_exports/

cam-07_warehouse-aisle.mp4

418 MB mp4

cam-12_loading-dock.mp4

692 MB mp4

cam-03_marina-pier.mp4

531 MB mp4

cam-18_canal-lot.mp4

604 MB mp4

S3://media-ops/results_json_exports/

cam-07_warehouse-aisle.json

application/json

cam-12_loading-dock.json

application/json

cam-03_marina-pier.json

application/json

cam-18_canal-lot.json

application/json

Your downstream server

webhook receiver / 10.24.8.42

14:00:02.418200

POST /webhooks/media import.received

4 source media files received

files=[cam-07_warehouse-aisle.mp4, cam-12_loading-dock.mp4, cam-03_marina-pier.mp4, cam-18_canal-lot.mp4]

14:00:03.108200

POST /webhooks/media processing.started

4 media processing runs started

files=[cam-07_warehouse-aisle.mp4, cam-12_loading-dock.mp4, cam-03_marina-pier.mp4, cam-18_canal-lot.mp4]

14:00:06.902200

POST /webhooks/media processing.completed

4 media processing runs completed

files=[cam-07_warehouse-aisle.mp4, cam-12_loading-dock.mp4, cam-03_marina-pier.mp4, cam-18_canal-lot.mp4]

14:00:08.621200

POST /webhooks/media export.completed

4 JSON result files exported

files=[cam-07_warehouse-aisle.json, cam-12_loading-dock.json, cam-03_marina-pier.json, cam-18_canal-lot.json]

VideoVector media index

integration control panel

0/4

Cloud integration

Live

Extraction engine

CCTV event detection

Output format

JSON cloud export

Batch processing0%

Warehouse aisle

cam-07_warehouse-aisle.mp4

syncing
00:28:120%

Loading dock

cam-12_loading-dock.mp4

syncing
00:31:440%

Marina pier

cam-03_marina-pier.mp4

syncing
00:24:550%

Canal lot

cam-18_canal-lot.mp4

syncing
00:29:380%
Schema playground interface

Schema playground

Prototype extraction logic before production

Build, test, and refine media schemas against real assets. Teams can validate nested fields, extraction engine 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.

Extraction engine versions

Track schema iterations as media operations evolve.

Contextual indexing

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

Media intelligence integration workflow

Integration

Connect media intelligence to production systems

Connect storage, schedule processing, trigger extraction executions, 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, extraction engines, extraction executions, searches, and exports programmatically.

Python SDK

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

Media intelligence workflow diagram

How it works

From extraction engine design to searchable media intelligence

VideoVector connects extraction engine definitions, media indexes, extraction execution, 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.

Extraction engine definition

Describe the signals, events, entities, and summaries each extraction execution 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, extraction executions, 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.

Extraction execution

Execute selected extraction engines 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.

Integration trigger

Watch storage locations, import new assets, and trigger extraction execution.

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 Extraction Engine 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.

See the media intelligence workflow in motion

See how VideoVector transforms media libraries into structured intelligence, searchable context, and production-ready workflows for organizations that rely on video, audio, and image data.

VideoVector vs Alternative

For teams evaluating TwelveLabs and other alternatives for video intelligence workflows.

Capability
VectorMethods
TwelveLabs
Model strategy
Selectable GCP, AWS, Azure, and open-source model providers
In-house Marengo and Pegasus models
Cost
Tunable by extraction complexity, provider choice, and model size
Anchored to Marengo and Pegasus pricing
Segmentation strategy
Contextual and instruction-based, computer-vision and shot analysis, and fixed-window modes
Model-based segmentation
Output control
API, SDK, MCP, Pydantic schemas, full indexed embeddings and artifact exports
API, SDK, MCP
Deployment
Cloud, private cloud, dedicated instance, and on-prem deployment
Cloud, private cloud, and on-prem options

Pricing

Start free, choose the plan that fits your workload, or scope an enterprise deployment with custom controls.

Free

A workspace for trying core search, extraction execution, and early schema design.

$0

No checkout required

10 video processing credits included
Entry-level API and processing throughput
  • Core search and processing workflows
  • Extraction executions for validation projects
  • Community support

Starter

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

Popular

$49/mo

Stripe monthly subscription

500 video processing credits included
Higher API and processing throughput
  • Connector, webhook, export, 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 processing workflows for larger libraries
  • Larger recurring credit allocation

Enterprise

For governed deployments with custom rollout, infrastructure, and integration 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 integration 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.