> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pureframe.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use Cases

> Common ways teams use Pureframe AI to build video-aware applications.

## Internal footage retrieval

**Problem:** Teams sit on hours of raw recordings — sales calls, user interviews, training sessions — and finding a specific moment means scrubbing through video manually.

**How Pureframe AI helps:** Upload recordings to a collection and search with natural language. `"customer mentions pricing"` returns the exact timestamp across hundreds of hours of footage in under a second.

```bash theme={null}
curl -X POST https://api.pureframe.ai/v1/search \
  -H "Authorization: Bearer pf_..." \
  -F "query=customer mentions pricing" \
  -F "collection_id=col_sales_calls"
```

***

## AI agent with video context

**Problem:** AI assistants can't see video. When users ask questions about recorded content, the agent has no way to look it up.

**How Pureframe AI helps:** Connect the Pureframe AI MCP server to Claude, GPT-4o, or any LLM with function calling. The agent can search footage, retrieve frame images, and reason about visual content in a single conversation turn — with every claim traceable back to a timestamp, see [Source-backed Results](/agents/source-backed-results).

**Example agent prompt:** `"Find the part of last week's demo where we showed the pricing page, and tell me what the presenter said about the annual plan."`

***

## Video search for end users

**Problem:** You're building a product that includes video content and users need to find specific moments without scrubbing.

**How Pureframe AI helps:** Use the search API to power a search bar in your app. Users type what they're looking for, and your UI displays the matched clips with thumbnails and timestamps. Results include presigned playback URLs ready for your video player.

***

## Content moderation at scale

**Problem:** You have a library of user-generated video and need to identify specific types of content (e.g., sensitive scenes, brand mentions, competitor references).

**How Pureframe AI helps:** Run recurring searches across your library using the `modes` parameter (`video`, `transcript`, `scene`) to surface content that matches your criteria. Automate flagging and review workflows using the API.

***

## Training data curation

**Problem:** You need labeled video clips for a fine-tuning or evaluation dataset, but manually reviewing hours of footage isn't practical.

**How Pureframe AI helps:** Describe the moments you need — `"person looking directly at the camera"`, `"close-up of hands on keyboard"`, `"outdoor scene with natural light"` — and extract matched clips with timestamps. Use `thumbnail_base64` to pass frames directly to a vision model for automated labeling.

***

## Meeting and lecture search

**Problem:** You record meetings or lectures and want attendees to find specific topics discussed without watching the whole recording.

**How Pureframe AI helps:** Upload recordings to a per-meeting or per-course collection. Expose a search box powered by `POST /v1/search` with `modes=transcript` — users search what was *said* and jump directly to the moment in the recording.