AI-powered lead qualifying from any data source.

• Send an HTTP POST request to main.ts
• Include any lead data you have
  • name
  • email
  • company
  • anything else

• main.ts forwards the request data to the AI agent in agent.ts
• The agent:
  • reads your qualification rules from PROMPT.txt
  • uses web search to gather more context if needed
  • decides whether the lead matches your criteria

• The agent returns a normalized, predictable JSON response:

{
  "name": "(normalized name)",
  "match": true,
  "reasoning": "(why this lead does or does not match)"
}

1. Click Remix
2. Save your OPENAI_API_KEY as an environment variable
3. Customize PROMPT.txt (Don't remove any fields from the structured response)
4. Get the val's HTTP endpoint from main.ts:
5. Start forwarding leads to this webhook from any source as a POST request.
6. That's it! You now have a growing leads SQLite table with one column for your original input_data and a second column for the AI evaluator's output_data. You also have a dashboard of this data hosted at the main.ts val endpoint.

• You can add any number of fields to the AI's output instructions in PROMPT.txt and they'll be added to output_data. You can use this to expand the AI's assessment, enrich your leads, or even just normalize lead data coming from different sources.
• You can also add query parameters when you POST to the val endpoint. (ex: your-val-endpoint.val.run?source=signup-page). The parameters will be bundled in the _query object of input_data. This can be useful for tracking where a lead was forwarded from.