> For the complete documentation index, see [llms.txt](https://docs.tickertrends.io/tickertrends/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tickertrends.io/tickertrends/documentation-v2/4.-features-and-workflows/ai-search.md).

# AI Search

### Overview

**AI Search** lets users search TickerTrends in plain English.

Instead of starting from a ticker or manually setting filters, users can type a natural-language question and let TickerTrends route them to the most relevant page, screener, or dataset.

This is useful for both discovery and speed. Users can go from a broad idea to a filtered workflow in one step.

### How it works

The search bar accepts:

* ticker and company searches
* keyword and term searches
* natural-language questions

The router interprets the request, understands the intent, and sends the user to the most relevant destination. This is not meant to behave like a chatbot conversation. It is designed to behave like a smart search and routing layer across the platform.

### Example queries

Users can ask questions such as:

* `what fashion brands are trending on TikTok?`
* `viral food`
* `what's exploding on TikTok right now?`
* `find private AI companies with accelerating consumer interest`
* `how is Nvidia trending on social media`

These prompts help users discover:

* trending categories
* relevant companies
* fast-moving keywords
* the right screener tab for deeper work

### What the router does

Depending on the query, TickerTrends may route users to:

* **Exploding Trends** for raw breakout keyword discovery
* **Trackers** for ranked company momentum
* **Forecasts** for KPI-driven idea generation
* company pages when the query is ticker-specific
* private company pages when the search suggests a private-market workflow

This saves time and reduces the need to manually decide where to begin.

<figure><img src="/files/yRFGBpfZysuHyx9X39Ps" alt=""><figcaption></figcaption></figure>

### Search suggestions and recent results

When users click into the search bar, TickerTrends can surface:

* recent tickers
* recent private companies
* direct company matches
* natural-language guidance for what users can ask

This makes the search bar useful both as a navigation tool and as a discovery surface.

### Best practices

AI Search works best when the prompt is phrased like a search goal.

Good examples:

* `consumer companies with accelerating awareness`
* `what fashion brands are trending on TikTok?`
* `viral food`

Less useful prompts are overly long or conversational. Short, direct intent works best.

### Why it matters

AI Search makes TickerTrends easier to use when users do not yet know:

* which ticker to start with
* which dataset to search
* which screener tab fits the question

It turns the platform into a faster discovery engine and helps users move from idea to workflow with much less friction.