Context engine for analyst workflows

The real product being described is not search, it is machine judgment about what adjacent facts could move an analysis. In the Nike example, the useful step is spotting that Adidas momentum can change shelf space, pricing pressure, and investor expectations before that connection is explicitly asked for. That turns a research agent from a filing retriever into something closer to an associate that can widen the frame of a report on its own.

• The workflow only matters if the extra context is economically relevant. Adidas reported record 2025 revenue, 13% currency neutral brand growth, and double digit growth across channels, with North America up 10% for the full year. That is exactly the kind of competitor signal that could change a Nike forecast.
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• This is especially valuable in categories like athletic footwear and apparel, where share shifts show up through very concrete levers, retailer shelf space, full price sell through, marketing partnerships, and wholesale orders. Adidas says strong sell through supported more shelf space allocations, which is the kind of second order fact an analyst would want surfaced automatically.
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• The interview makes clear that deep research tools are moving beyond answering the stated question. The higher value behavior is suggesting missing sections, surfacing outside evidence such as regulations or competitor news, and using that to improve a company model. That is a broader product ambition than a standard search API returning links and snippets.
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This points toward research infrastructure that acts less like a browser and more like a context engine for analysts. The winners will be the products that can reliably pull in the right outside facts, competitor moves, filings, and domain sources at the moment they become material, and do it early enough to change the draft before the analyst knows what is missing.