What Changes When a Model Can Look Things Up
We gave Claude, Gemini, and GPT the same brief for a simulated $1.2M luxury watch marketing launch — once on their own, once with KINETK's multimodal GraphRAG API connected. Same prompts, same models. With the API, the answers came back with citations instead of names and figures assembled from training data.
Adding KINETK lifted Authority, Precision, and Conviction by 25% to 45% over the same models running alone
The Results
How We Ran the Test
One brief, three frontier models, run twice each — with and without KINETK connected.
The Scenario
A marketing brief for a heritage watch brand launching a limited-edition "World Cup Tournament Chronograph" (Campaign: The Legacy of the Game).
The Task
Each model produced the brief's deliverables: trend strategies clear of existing IP, visual mood boards, and named influencer shortlists grounded in current market data.
The Control
Identical prompts against the enterprise tiers of Claude, OpenAI, and Gemini — first standalone, then with KINETK's RAG architecture connected.
Where the Standalone Models Broke
Three patterns showed up in the standalone runs:
It Can't Tell Recall From Guess
With only training weights to work from, the model delivers what it remembers and what it invented in the same confident register.
Generic Instead of Specific
Modern trends are carried in video and imagery. A text-only model can't see them, so it describes a generic version of the audience instead of the real one.
No Sources, No Recency
The model works from a frozen snapshot of the web, and can't point to where any of it came from.
What Changed With the MCP Connected
The same models, with the KINETK MCP connected, changed in three ways:
Answers With Sources
Claims came back retrieved and cited, so a strategist could check where each one came from.
Specific Instead of Generic
With real market data in context, the models named specific communities and creators instead of describing a target audience in the abstract.
Current, Not Cached
The campaign hinged on what was moving that week. KINETK's Sentinel network feeds live data in, so the outputs tracked current trends rather than training-set ones.
The gap is architectural, not a prompting problem
Multimodal From the Start
The most popular content on the internet is shot, edited, and posted as a clip. KINETK reads the video and the imagery — not the caption standing in for them, which is all most infrastructure ever sees.
Built on Relationships
Raw posts become structured, governed records with the edges between them intact. Agents can follow who influenced whom, and map a trend back to the specific IP behind it.
The Corpus Keeps Growing
A decentralized Sentinel network captures data continuously, worldwide. Those signals reach the model at query time, so it isn't limited to what it learned in training.
Technical Documentation
The scoring math and the exact prompts we used:
SDI Methodology Cheat Sheet (Detailed breakdown of the Authority, Precision, and Conviction formulas)
The Global Heritage Campaign Brief (The exact enterprise inputs and constraints used in the benchmark)