Everything below is interactive and built on realistic synthetic data for a fictional 475-person company. No login, no email gate — click through and judge the craft yourself.
A 475-person SaaS company "felt" turnover was high, but the data lived in three systems and the quarterly report was always six weeks stale. We built one dashboard: turnover cut by department, tenure and manager — the manager view surfaced one team that had lost 4 of 7 people — plus a transparent risk score per employee with the top driver named. Replacement cost aggregated for the first time: ~$5.0M trailing twelve months, 51% of exits regretted.
Ten hires a month, every decision made on anecdote. The ATS had every timestamp needed — nobody had assembled them. The result: a five-stage funnel over 15,000 applications, time-to-fill medians and p90s by role family, source conversion showing referrals converting ~5x better than job boards, and an aging view that catches reqs before they hit day 90 silently.
Dashboards answer questions for the people who open them — and usage always decays. This agent reads the attrition data model every Monday and writes leadership a three-part brief: what changed, why it matters, what to do this week. Below it, a plain-English Q&A box computes grounded answers with supporting charts. In production this runs server-side against your live semantic model with row-level security; the public demo pre-generates its briefs at build time — because putting a live LLM endpoint with an API key on the open internet is exactly the kind of shortcut we don't take.