AI-powered lead filtering from any data source.

1. Click Remix
2. Save your OPENAI_API_KEY as an environment variable
3. Customize PROMPT.txt. Don't removing anything from the it's structured response.
4. Get the val's HTTP endpoint from main.ts:
5. Forward any leads to the val's HTTP endpoint 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 second column for the AI evaluator's output_data.

• When a new lead comes in via POST, it is sent to the OpenAI agent in agent.ts.
• The agent uses web search and the lead data from your POST request to determine if it's a match for the criteria in PROMPT.txt. By default, the AI's response includes match (true or false), its reasoning, and a normalized name for the lead.
• The input_data and agent's output_data each get a column in the leads sqlite table.
• The main.ts dashboard shows a history of all lead assessments, successful matches first. Clicking any lead shows the full inbound and output data from the assessment.

• You can add any number of fields to the AI's output instructions in PROMPT.txt and they'll be added to output_data in the leads table and in the dashboard. This can add new dimensions to the AI's assessments or simply allow you to normalize data from different sources.
• You can also add query parameters when you POST to the val endpoint. (ex: leadval.val.run?source=singup-page). The parameters be bundled in the _query object of 'input_data.' This can be useful for tracking where a lead came from.