KINETK
Teardown

Replace Flat RAG With a Multimodal Knowledge Graph

The ProblemThe BlueprintThe Payload
The Problem

What Flat RAG Can't Tell You

If your agent's context is a handful of text snippets pulled from a vector database, three things are already missing before the model starts reasoning:

  • Disconnected Snippets

    A vector database returns the top-k passages and nothing about how they connect. Treat social data as independent documents and the agent never sees which accounts overlap, which communities formed, or what happened between the posts.

  • No Interaction Data

    A model can read the post. It can't see who shared it, who replied, or where it went next. Without that, your agent can tell you what a narrative says but not how it's spreading or who is carrying it.

  • Answers From Frozen Weights

    Ask about something that happened last week and the model answers from weights frozen before it. It answers anyway, confidently, with the nearest pattern it picked up in training.

The Blueprint

The Multimodal Graph Layer

KINETK maps content, interactions, entities, and relationships into one multimodal knowledge graph. Call the Graph Service API from your agent loop and the model gets the edges along with the text. Here's how to wire it up:

  1. 01

    Traverse Entities, Not Documents

    Query for an entity or concept instead of keywords. The API returns the matching posts and the edges between them, so your agent can follow a TikTok video to the account that shared it, to the visual remix that appeared on Reddit, to the cluster that formed around it.

  2. 02

    Put the Graph in the Context Window

    Feed the graph payload into the prompt as-is. The question changes shape: not “summarize this topic” but “trace how this moved and who moved it.” The agent reasons over the path a narrative actually took, not just its wording.

  3. 03

    Questions Your Agent Can Now Answer

    With the edges in context, multi-step questions become answerable:

    • Brand Intelligence

      "Which distinct sub-communities are visually interacting with this product launch, and who are the central bridge nodes connecting them?"

    • Threat Intelligence

      "Map the origin of this viral visual narrative. Is it organic community formation or coordinated bot amplification?"

The Payload

What Comes Back

Nodes and interaction edges as structured JSON — parseable by the model without a preprocessing step.

JSON:
1{
2  "entity_id": "concept_luxury_minimalism",
3  "knowledge_graph_telemetry": {
4    "primary_nodes": 412,
5    "interaction_edges": 18500,
6    "cross_platform_velocity": "ACCELERATING"
7  },
8  "community_clusters": [
9    {
10      "cluster_id": "C-992",
11      "dominant_modality": "video_frames",
12      "network_density": 0.88,
13      "interaction_behavior": "high_share_low_comment",
14      "bridge_nodes_to_mainstream": [
15        "user_883X",
16        "forum_thread_A"
17      ]
18    }
19  ],
20  "influence_flow": {
21    "origin_platform": "TikTok",
22    "current_containment_status": "BREAKING_TO_REDDIT",
23    "narrative_cohesion_score": 0.94
24  }
25}