Business activity monitoring, bottleneck detection, and case oversight — asked in plain English.
Your claims platform already logs every step of every case. Knodox reads that event store read-only and turns it into permission-aware answers: where cases are stuck, which are past SLA, and the full history of any single claim — every figure verified against the underlying rows, never invented.
For claims operations, SIU, compliance — and the platforms that serve them.
You log everything — but detailed logging isn't insight. The questions that matter get answered by exporting to spreadsheets, pinging adjusters, and hoping the numbers line up.
Which stage is slowing everything down — and by how much against target? The answer is spread across millions of event rows nobody has time to aggregate.
"Which cases are past SLA right now?" should take seconds. At scale it takes a report request, a queue, and a day — by which time the answer has changed.
Reconstructing one claim's journey — every handoff, pause, and decision — means reading the raw event log by hand. Fine for one case. Impossible for a book of them.
These are real Knodox answers on a realistic claims dataset. Every number is derived from the underlying event rows at query time, then checked back against the source before it's shown.
"Where are claims getting stuck this quarter?"
Legal Review is the bottleneck — average dwell of 14.8 days against a 7-day target (2.1× over), the slowest stage on every one of the four claims teams. Standard claims clear it in ~6 days; complex claims are what drag the average up.
Derived from ~11,800 event rows · reconciles with stage_metrics"Which cases are past SLA right now?"
The same question returns a different, correct answer per role — because permissions are enforced at the data layer, not the UI:
"Give me the full history of claim CF-2025-0052."
A complete 38-day timeline straight from the event store — every stage transition, handoff, and SLA pause, with who did it and when. No spreadsheet archaeology; the receipts are one question away.
Every event cited back to its source rowThe same three guarantees that run across all of Knodox — applied to claims data.
Adjusters see their cases, supervisors their team, ops managers their department. Roles map from your identity provider; every query is scoped at retrieval — and sensitive PII is masked for roles without clearance.
Every figure is checked back against your live data after the answer is written. Aggregates like average dwell or breach rate reconcile with the raw event rows — if a number can't be traced, it's flagged, not shown.
Pin any answer — bottleneck by stage, breaches by team, adjuster load — into a living dashboard that refreshes against your data. Ask once; monitor forever.
The example answers above run on a realistic claims dataset — the kind of volume where manual oversight breaks down.
Every headline number is computed from the underlying rows at load time — never hand-typed — so it reconciles with Knodox's open-the-receipts guarantee.
Point your teams at Knodox's Ask interface for the fastest path to value, or keep your own product UI and call Knodox's permission-aware retrieval API. Either way, the same role and PII rules apply per end-user.
Your users log in and ask questions directly. Nothing to build — connect a read-only source, map roles, and go.
Keep your own screens and call the retrieval API with the end-user's identity on every request. Knodox mints a scoped token and returns only what that user is allowed to see.
Phone numbers, national IDs, and policy details are masked before the model reads them — unless the caller's role is explicitly cleared to see them.
Thirty minutes. We connect a read-only copy of your event store, set up two roles, and let you ask your first real question. No demo data, no slideware.