We believe complex eligibility rules shouldn't live locked inside dense PDF documents. LexiRule makes them readable, verifiable, and interactive.
Regulatory documents — immigration guides, insurance policies, healthcare eligibility frameworks — are intentionally precise. But that precision creates impenetrable walls for the people who need to understand and act on them.
Organizations spend enormous resources manually translating these documents into software systems or spreadsheet checklists. The process is error-prone, expensive, and leaves a gap between the official policy and what gets implemented.
When the policy changes — which it does, often — the entire process repeats. Version drift accumulates. Decisions get made on outdated logic.
LexiRule's core principle: AI is used heavily during preparation, but execution is 100% deterministic. No AI at runtime means no hallucinations on consequential decisions.
Gemini and OpenAI models are used intensively during ingestion, extraction, and audit to achieve high accuracy from complex documents.
Every extracted rule must be reviewed and approved by a human admin before it can affect any user. AI is a clerk, not the judge.
Once approved, rules are stored as plain SQL data. The runtime engine uses only logic operators — AND, OR, >, <, == — never an API call.
Every decision traces back to a specific rule version and the exact paragraph in the source document that justified it.
Every eligibility decision is produced by deterministic logic. Same inputs, same outputs — always. No probabilistic AI in the critical path.
Every rule links back to the exact sentence in the policy document. Applicants and auditors can always see why a decision was made.
Built for immigration first, designed for any policy domain. Healthcare, finance, compliance — the architecture adapts to the rules.
AI accelerates, humans decide. We believe consequential rule systems must have an accountable human in the approval loop.