Every claim, quote and renewal is a decision. G-Nosis remembers the reasoning behind each one — turning an insurer's everyday calls into faster, more consistent, more defensible ones. Value that compounds with every file.
Trading systems, claims platforms, deal rooms, risk models, Slack threads, board memos. The reasoning behind every decision your institution has made is scattered across systems no two people read the same way — and gone the moment the person who made it leaves.
87% OF DECISION CONTEXT · NEVER QUERIED AGAING-Nosis binds every fragment to the decision it justified. Proposals, rules, approvals, exceptions, precedents, outcomes — first-class events on an append-only, bitemporal log. Scattered files become an institutional mind your people can reason over.
SEMANTIC ACCESS · 27 EDGE TYPES · BITEMPORALA customer has dropped and shattered a scheduled luxury watch. The examiner must decide quantum and coverage — and deciding it cold risks diverging from how the firm settled the same facts last year.
The engine binds the in-force WORDING-V5 §accidental-damage and the claims-handling guideline that applies. Each step is pinned to wording version, transaction time and valid time — not reconstructed later from an adjuster's file note.
Fourteen months ago, another team settled a comparable valuables loss on the same coverage trigger — same wording version, same quantum band. The trace surfaces it before the examiner asks, and flags the moment this decision would diverge from it.
Every precedent is fed back in as context for the next decision — and every decision becomes the next precedent. The graph compounds with each call, not each user. That loop is the moat no read-path tool can copy.
We stopped re-deciding things we had already decided. The trace surfaces our own precedent before anyone asks — and every call we make is defensible the moment it’s made.
The market is full of tools that index your documents and answer questions about them. They get smarter when your people ask better queries — the institution itself learns nothing. G-Nosis is the opposite kind of system.
Your manuscripts, models and precedent stay in your tenant. The data that makes your decisions defensible is the same data that makes you competitive — it never leaves, and never trains anyone's model.
G-Nosis is the substrate, not the model. Your decision infrastructure is not locked to any single frontier model's pricing, deprecation schedule or roadmap. Swap the engine; keep the graph.
Every event already carries transaction time, valid time, schema version, actor and source. You are not bolting on explainability — the trace is the system. Defensible by construction.
G-Nosis is the decision-intelligence layer for institutions — on infrastructure you own, independent of any single model. We work with firms that want to design the decision schema they preserve, not buy a black box that forgets.