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Use this path when you want to show that NeuronSearchLab is more than a recommendation API. The demo should prove that a team can define a surface, tune ranking behavior, apply business logic, and explain the result. The strongest version is a vertical-specific walkthrough, such as news/media, commerce, sport, or video. Keep the story narrow: one surface, one goal, one ranking change, one explanation.

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Demo story

1

Start with a surface

Open or create a context for a recognizable surface, such as Homepage Feed, Related Articles, Product Detail Rail, or Video Continue Watching.
2

State the business goal

Explain what the surface is meant to improve: discovery, CTR, conversion, retention, long-tail coverage, freshness, or editorial control.
3

Show the recipe

Open the context’s Recipe tab and show how candidate sources, signals, discovery settings, and guardrails are attached to that surface.
4

Show the pipeline

Open the Pipeline tab and show the runtime stages: candidate generation, enrichment, scoring, rules, ranking, and post-processing.
5

Apply one business control

Use a simple rule, such as boosting fresh content, filtering unavailable items, capping sponsored items, or pinning a launch item.
6

Explain the result

Use Explainability to show retrieval score, rule impact, pipeline stage status, and feature contributions for one user-item pair.

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What the buyer should understand

By the end, the buyer should believe:
  • NSL can separate ranking behavior by product surface.
  • Operators can adjust ranking without waiting for application releases.
  • Business rules sit after model scoring, so control does not replace relevance.
  • Pipelines expose the runtime path instead of hiding everything in a black box.
  • Explainability makes recommendations inspectable enough for debugging, demos, and rollout confidence.

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Suggested proof scenario

For a media/news demo:
  1. Create or open a Homepage Feed context.
  2. Attach a discovery-oriented recipe.
  3. Show candidate generation and rules stages in the pipeline.
  4. Add a rule that boosts recent editorial priority content but keeps relevance in the ranking.
  5. Request recommendations for a sample user.
  6. Explain one recommended item and point to the score, matched rules, and pipeline stages.
The message is simple: NSL gives teams inspectable control over recommendation behavior, not just a black-box ranked list.