Calling a model API is one part of building an AI app. Getting the right information from your own data into that call is another project. That’s the work I built Jylus to handle. You send your application’s records, ask for the context a question needs, and get back a compact evidence pack with references to the source records. You pass that into your existing model. For example, a support assistant might need an account’s current plan, relevant support history and the policy that applies. The backend has to bring those together before the model writes its response. If you’re building a RAG pipeline or wiring retrieval into an AI app, this is the part of the stack I’d like you to try Jylus against. The public playground accepts text or JSON. Paste a small sample, ask a question you know the answer to, and inspect what it selects. It returns evidence for your model; it doesn’t generate the final answer. Try it here: https://jylus.ai/try — no account required. I’m the founder. My goal is to let developers spend more time on their application and less on context plumbing. What would you need to see before trusting a managed API with this part of your app?   submitted by   /u/jylusdev [link]   [comments]