Applied social intelligence
Consumer behavior movement at your fingertips
KINETK turns the messy, multimodal social web into structured intelligence, so agents, brands and enterprise teams can see the patterns shaping consumer behavior.
Live
- data points
- 200M+data points
- content + creators analyzed daily
- 10M+content + creators analyzed daily
- narratives + patterns generated
- 10M+narratives + patterns generated
- vector embeddings
- 15B+vector embeddings
From behavior to intelligence.
Consumer behavior goes in as video, image and text, and comes out as narratives, trajectories and signals your team can act on.
One question
What is forming in my category right now?
The material itself
Ranked posts and videos from a shared space over video, image and text — matched on what they look like, not on what the caption happens to say.
What comes back
- platform
- tags
- engagement
- similarity
- theme
- tone
- novelty
Good for
Reading, citing and sanity-checking the actual source material.
Use cases
How the graph is currently used to show consumer behavior movement.
Narratives form on video long before anyone names them
A narrative rarely starts with a hashtag. It shows up as a clip, a reshoot, a screenshot, a reaction; different content carrying the same underlying idea.
- Connect the momentCluster related clips, reshoots, screenshots, reactions, and other multimodal content.
- Validate the signalTest patterns across volume, creator diversity, platform spread, and recency.
- Track the trajectorySeparate narratives already at scale from the ones just starting to break.
Evidence
Three findings, already published.
Each one went out as a card on a different question. The line under each is the line it shipped with.
01 · Look-alike, no hashtag
Restocking videos grouped by what they look like. Not one of them shared a hashtag. 02 · Ranked on attention
One motorsport topic, top 20 by engagement. Three of them have under 15,000 followers. 03 · Same trend, new posts
The same trend, twelve days apart. The posts turn over; the trend stays.
Architecture
One pipeline, from raw signal to agent-ready context.
Implicit signal capture
15B+ vectors · 1408d multimodalA product, aesthetic, or consumer behavior is rarely named in captions. KINETK identifies visual and behavioral motifs across 15B+ vectors using high-dimensional vector search that traditional text RAG can never index.
Cross-modal retrieval
500M+ nodes · 2.3B+ typed edgesQuery via text, an image snapshot, or a video clip within the same coordinate system. Trace emerging trends seamlessly across platforms, even when captions, hashtags, or usernames completely shift.
Continuous real-time ingestion
Sentinel network · proof of humanInstead of indexing static text or relying on stale training data, KINETK continuously ingests real-world behavior to detect the underlying visual motifs, gestures, and cultural patterns shaping consumer decisions across platforms in real time.
Agent-ready infrastructure
REST API · MCP · sub-secondTransform fragmented posts into token-efficient entity graphs via MCP, giving AI agents the intent-driven interface needed to conduct market research, risk assessment, and campaign discovery autonomously.
Connect your agent via MCPReady to see your market clearly?
Access the graph with our API or our MCP for AI agents.