Taylor Moore

Taylor Moore

Building Raptor Data (raptordata.dev).
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Taylor Mooreleft a comment
Hey Product Hunt, I'm Taylor. Former Australian fighter pilot, now software engineer. I built Raptor because I was tired of two things: burning money on redundant LLM calls and watching prompt injections slip through keyword filters. What Raptor does: It sits between your app and your LLM provider. Every request flows through and gets three things: 1. Semantic caching Similar queries hit the...
Raptor Data
Raptor DataProtect, cache and hot patch your LLM APIs. Built in Rust.
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Taylor MoorehuntedRaptor Data
Rust-powered AI gateway that actually slaps. Semantic caching: 500ms → 8ms. Semantic firewall: catches jailbreaks and malicious actors by intent, not keywords. Hot-patch: fix hallucinations without redeploying. One line change. Free tier. Your API bill will thank you.
Raptor Data
Raptor DataProtect, cache and hot patch your LLM APIs. Built in Rust.
Taylor Mooreleft a comment
Here from Australia 🦘 Let me know if you have any questions!
Raptor
RaptorHot patch, cache, protect your for LLM API. Built in Rust.
Taylor Mooreleft a comment
The Math That Made Us Build This I want to share the numbers that convinced us this problem was worth solving. We analyzed the embedding costs of a typical contract management system: The Scenario: 500 contracts Each contract averages 3 versions (negotiation rounds) ~1,000 chunks per contract Traditional RAG approach: 500 contracts × 3 versions × 1,000 chunks = 1.5M chunks embedded What...
Raptor
RaptorHot patch, cache, protect your for LLM API. Built in Rust.
Taylor Mooreleft a comment
We built the "Stripe for Ingestion" so you can stop writing parsers. Hey Product Hunt! I’m the maker of Raptor Data. We built Raptor Data because the "Day 2" of building RAG apps is miserable. You build the prototype, but then users start updating documents. Suddenly, you're writing complex Python scripts to parse PDFs, manage versions, and huge AWS bills to re-embed 500-page files just because...
Raptor
RaptorHot patch, cache, protect your for LLM API. Built in Rust.
Taylor MoorehuntedRaptor
Rust-powered AI gateway that actually slaps. Semantic caching: 500ms → 8ms. Semantic firewall: catches jailbreaks/malicious actors by intent, not keywords. Hot-patch: fix hallucinations without redeploying. One line change. Free tier. Your API bill will thank you.
Raptor
RaptorHot patch, cache, protect your for LLM API. Built in Rust.
Taylor Moorestarted a discussion

Stop re-embedding the whole world. Introducing Raptor Data: The "Git" layer for RAG.

We all know the feeling. You build a RAG prototype, it works beautifully, and you deploy it. Then the "Day 2" reality hits: The Bill: Your OpenAI/Pinecone costs start creeping up. The Maintenance: Users update documents. You have to write script after script to handle versions. The Inefficiency: You realize that when a user fixes a typo in a 500-page contract, your pipeline is re-embedding all...

Taylor Moorestarted a discussion

The "Day 2" Problem in RAG: Why don't we treat documents like code?

We’ve all built the "Hello World" RAG app. You upload a PDF, chunk it, embed it, and chat with it. It works great. But what happens on Day 2 when the user uploads Contract_v2.pdf with a single typo fix? In 90% of pipelines I see, the logic is: Delete all old vectors. Re-parse the file. Re-embed the entire 500-page document. This feels insane to me. We wouldn't re-compile an entire OS just to...