ai·rete·rag - Deterministic decisions with plain-language explanations
Show HN: AI·rete·RAG – a Rete rule engine decides, RAG explains why
ai·rete·rag combines a Rete rule engine with retrieval-augmented generation to deliver decisions that are both auditable and explainable. Rules determine the 'what' with precision and repeatability, while retrieval explains the 'why' using your own documents. It solves the problem of black-box AI by providing a full audit trail, conflict detection, and three flexible ways to wire rules and retrieval. Try it live with no signup and see how adjusting facts like credit score flips the verdict from approved to declined. Bring your own rules and documents to build custom decision domains.
Rules decide the what — auditable and repeatable. Retrieval explains the why — grounded in your own documents.
- awfm9
This is a really interesting concept. Could be quite useful for agents. I worked on something similar, but more abstract; you managed to take it to the next level.
- jimmySixDOF
I was expecting to see some Jev based influence on RETE/CEP rule handling ... the Typesafe.ai team have a lot more in the pipeline this use case is just getting started
- lunatuna
I've used rete based CEP tool in the past for utility operations event management. We could tag the rule used to the event or event aggregate, but it was on a single node basis and a basic aggregate. If there was more coverage in understanding it would be a big help to operators. Based on say a set of outage events, can it further understand if it weather event, equipment failure, scheduled maintenance, etc. Linking it to possible operating procedures based on a broader understanding of the state would be very useful.