Intelligence you can audit.

One assistant across every module. Replies come back as structured blocks — text, KPI tiles, charts, tables, record cards, record lists, and an in-line document viewer — each stitched with provenance chips and suggested follow-ups. It's page-context aware, takes voice input via server-side speech recognition, and can act on the page only with your consent.

Every record is dual-indexed: a 3072-dimension vector index built on Azure OpenAI text-embedding-3-large, written beside a classic keyword index — so meaning-based questions and exact-term lookups both hit. A change-data-capture indexer keeps both indexes fresh as the archive changes, and purged records drop out of search.

A deterministic CDS rules engine does the safety-critical work: allergies, drug cross-reactivity, critical labs, renal function, care gaps, and medication reconciliation, ranked by severity — same inputs, same flags, every time. On top of that, a cited AI clinical impression summarizes the chart, PHI-minimized, with every statement linked to clickable evidence.

Ask for any metric in plain English. A default-deny validator inspects the generated SQL and runs only statements it can prove safe — over de-identified, k-anonymized views, scoped to your division and tenant. You see the chart, the table, and exactly what executed. Anything it can't prove safe simply doesn't run.

Most AI waits to be asked. The Attention Digest doesn't: it scans finance, payroll, clinical, and compliance signals and tells administrators what actually needs their attention today — surfaced as a daily, cross-module briefing instead of another dashboard to remember to open.

Duplicate patient records are scored by a weighted probabilistic matcher and sorted into high, medium, and low confidence tiers. High-confidence matches are obvious; low-confidence ones aren't worth your time — so your HIM team reviews only the gray zone, working from grouped human-review clusters. No merge happens without a person approving it.