Real ChatGPT answer · index seedXyKojZEQ9m8completeMarketing and advertising · AI search and generative engine optimizationcommercial
Can a small marketing agency with a limited tool budget successfully implement multimodal search optimization using open-source models
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Can a small marketing agency with a limited tool budget successfully implement multimodal search optimization using open-source models
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
open source multimodal embedding models image text video search SigLIP2 Qwen3-VL BGE-M3 documentation 2026 Google ↗
Ollama open source local models embeddings documentation Chroma vector database open source Google ↗
- Documentation · qdrant.tech
- Documentation · huggingface.co
- GitHub repository · github.com
- Documentation · docs.n8n.io
- SigLIP2 · docs.vllm.ai
- Supported Models · docs.openedgeplatform.intel.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
open source multimodal embedding models image text video search SigLIP2 Qwen3-VL BGE-M3 documentation 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 | |
Ollama open source local models embeddings documentation Chroma vector database open source OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |