ai-rete-rag

ai·rete·rag is a hosted decision-intelligence platform that combines a Rete rule engine with retrieval-augmented generation (RAG). Rules handle the deterministic "what" — producing auditable, repeatable verdicts — while retrieval grounds the "why" in your own policy documents, contracts, and guidelines. The platform targets regulated industries such as financial services, healthcare, legal, insurance, and e-commerce where explainability and auditability are non-negotiable.

What It Is

ai·rete·rag is middleware that sits between your data and your users. It accepts structured facts via a REST API, evaluates them against YAML-authored domain rules using a pure-Python Rete network, retrieves the most relevant document chunks from a ChromaDB vector store, and synthesises a coherent verdict plus explanation using Claude. The result is a single API call that returns a decision, a confidence score, and a full audit trail — without requiring teams to write prompts or manage LLM logic directly.

Three-Layer Architecture

The platform is built around three independently configurable layers:

• Rete Rule Engine — A pure-Python Rete network with alpha nodes (fact filtering), beta nodes (fact joining), and terminal nodes (action firing). Rules are authored in YAML, support salience-based conflict resolution, and can be hot-reloaded without restarting the server. Every firing is recorded for replay.
• Retrieval-Augmented Generation — ChromaDB stores chunked embeddings (using the all-MiniLM-L6-v2 sentence-transformer model) of uploaded policy documents. At decision time, the top-k most relevant chunks are retrieved and passed to the language model, with relevance scores returned alongside each chunk.
• Orchestrator — Inspects each request and selects the optimal mode automatically: rules-only for speed, RAG-only when no rules exist, or hybrid when both are available. Supports three response modes: verdict, verdict_with_explanation, and full_audit.

Three Wiring Patterns

The platform supports three composable patterns for combining rules and retrieval, all running live:

• Rules → Retrieval — Rules narrow retrieval scope before documents are fetched, so a cardiac case only pulls cardiology sources, reducing hallucination risk.
• Retrieval → Rules — Documents are parsed into facts (entities, dates, obligations) and asserted into the working memory session; rules then fire on what was read.
• Decision → Narrative — The engine fires first and produces a decision trace; retrieval is invoked only to generate a human-readable explanation grounded in source documents.

Audit Trail and Conflict Detection

Every decision links back to the exact rules that fired and explains why every other rule did not — down to the specific value that missed a threshold. A built-in conflict detection feature runs static analysis to flag when two rules with different verdicts could both match the same case, surfacing the issue before it reaches production. The platform also includes a browsable policy rule catalog per domain showing conditions, salience, and verdicts.

Target Domains and Setup Path

The platform ships with eight built-in demo domains — loan underwriting, fraud screening, clinical vitals, blockchain/AML, insurance, legal/compliance, operations, and e-commerce — each runnable live in the workspace without signup. Bringing a custom domain requires uploading documents and authoring YAML rules; the API is unified across all verticals. Access is via a hosted API (no local setup required); an API key is created in the account settings and used as a Bearer token on POST /api/v1/decide. A self-hosted deployment option is available on the Enterprise plan.