Track narrative shifts

Goal: catch the narrative around a name or debate shifting — which arguments are gaining ground, which are being abandoned, and who moved.

Every insight is stamped with the moment it was said (published_at — the episode’s air date), so the corpus supports point-in-time comparison. Run the same Search over two windows:

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{
2
  "query": "NVIDIA competitive exposure to hyperscaler custom ASICs",
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  "tickers": ["NVDA"],
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  "date_from": "2026-03-01",
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  "date_to": "2026-05-31"
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}

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{
2
  "query": "NVIDIA competitive exposure to hyperscaler custom ASICs",
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  "tickers": ["NVDA"],
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  "date_from": "2026-06-01"
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}

Or let Ask do the comparison — it reads a time window from the question:

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{"query": "How has the investor narrative on NVIDIA's ASIC exposure shifted since June?", "answer": true}

Look for: arguments that appear in the recent window but not the earlier one, arguments that quietly disappear, sentiment flipping on the same mention, and new speakers entering the debate — each is a narrative shift the price may not have digested yet.

A pattern across all recipes: use Ask to open an investigation, then Search to pull the specific evidence sets you want to work with. The insight objects that come back are the same either way — see The Data Model.