VideoDB's answer

VideoDB is designed for developers and teams who need real-world perception in their systems:

• AI engineers building agent workflows.

• Founders building agent-native products.

• ML teams working with video, audio, and multimodal data.

• Infrastructure engineers scaling real-time media systems.

VideoDB's answer

VideoDB was founded by Ashutosh Trivedi on a simple belief: Files, compression formats, and players are optimized for playback. But agents don’t watch video — they compute on it. VideoDB introduces a new media layer for AI — turning video into real-time, queryable context that agents can reason over and act on. This unlocks a shift that creates a perception layer for the agentic world:

from files → streams

from media → context

from batch processing → real time

from insights → actions

VideoDB's answer

VideoDB is built as a streaming-first, agent-native system:

• Streaming architecture for real-time context (~seconds latency).

• Multimodal AI orchestration (LLMs + vision models).

• Indexes-as-code abstraction for programmable understanding.

• Search with playable outputs, not just metadata.

• Event-driven system for real-time triggers and automation.

VideoDB's answer

Most systems treat video as storage. VideoDB treats video as live, queryable context.

Key differentiators:

• Real-time perception layer for agents.

• Indexes-as-code instead of fixed pipelines.

• Search returns playable evidence.

• Unified system across files, streams, and desktop.

• Built for agent loops, not dashboards.

• Real-time programmable editing layer to extract and edit clips.

VideoDB's answer

Developers use VideoDB to move beyond fragmented tooling:

• No more stitching together FFmpeg, transcription, and vector DBs.

• One API for ingest, understanding, search, and action.

• Instant search over video without pre-processing pipelines.

• Native fit for agent frameworks and real-time systems.

It replaces an entire stack with a single, programmable media layer.

VideoDB's answer

VideoDB is used by teams building at the edge of AI across four major sectors:

• AI & Agent Builders: Developing screen-aware agents, coding assistants, and autonomous workflows.

• Media & Content Platforms: Powering archive search across thousands of hours of video, AI-assisted editing, and content generation.

• Security & Monitoring Systems: Implementing real-time camera intelligence, automated alerts, and compliance tracking.

• Data & Model Companies: Managing large-scale video dataset creation and training pipelines for vision and multimodal models.