YC Has It: Finding Startup Solutions Fast

Finding a specific tool in the ocean of Y Combinator alumni usually involves endless scrolling through the YC directory or guessing keywords in a search bar that doesn't always understand intent. "YC Has It" flips this by letting you describe a problem in plain English and mapping it to a startup that actually solves that specific pain point. It's essentially a semantic search layer over the YC ecosystem.

Why this matters for your AI workflow

If you're building an LLM agent or trying to optimize a deployment pipeline, you often hit a wall where you need a niche piece of infrastructure—like a specific vector database wrapper or a specialized observability tool. Instead of spending an hour on Google or Twitter, you can just describe the technical gap.

For example, if you're struggling with "managing long-term memory for autonomous agents without hitting token limits," a standard keyword search might give you generic documentation. A tool like this aims to point you directly to the YC company that built a dedicated solution for that exact problem.

Getting started with the tool

Since this is a discovery tool, the "deployment" is just visiting the site and inputting your query. To get the most out of it, I've found that using "Job-to-be-Done" (JTBD) phrasing works better than single keywords.

Bad Query: "AI CRM"
Good Query: "I need a way to automatically sync my LinkedIn leads into a database and trigger a personalized AI email sequence based on their latest post."

The more specific the scenario, the more accurate the match.

Practical use case: Solving the "Cold Start" problem

I used a similar discovery method recently to find a tool for synthetic data generation. Instead of browsing lists, I described the exact technical constraint:

I need a tool that can generate 1,000 high-fidelity synthetic user personas for testing a B2B SaaS onboarding flow, including realistic edge-case errors in their input data.

The result was a specific YC-backed startup that specializes in synthetic testing environments, which saved me from trying to write a custom Python script with Faker and a bunch of manual prompts that would have taken a full day to refine.

Is it worth it?

If you are a founder, a developer, or a product manager, yes. The YC portfolio is essentially a map of where the smartest people are betting on the future of tech. Being able to query that map using natural language is a huge time-saver.

The only downside is that it's limited to the YC ecosystem. If the solution is an open-source project on GitHub or a bootstrapped company in Europe, you won't find it here. But for high-growth, VC-backed AI tools, it's a shortcut.

For those interested in more AI discovery tools, check out promptcube3.com for more resources.