G-Nosis is a context graph that writes itself from your firm's own decisions: the reasoning, the rule in force, the outcome. It recycles every past call as precedent for the next, surfaced unasked. Retrieval tools make your people faster. G-Nosis makes the institution smarter, compounding with every decision.
The reasoning behind every decision your institution makes is scattered across trading systems, claims platforms, deal rooms and Slack threads. It vanishes the moment the person who made it leaves. No retrieval tool can read what nobody wrote down.
87% OF DECISION CONTEXT · NEVER QUERIED AGAING-Nosis binds every fragment to the decision it justified: every hop a typed edge, pinned to the rule in force and the moment it was known. Scattered files become an institutional mind, organised by domain.
150,000 NODES · 6 DOMAINS · BITEMPORALEvery decision leaves a trace. When a live call meets facts the book has seen before, the precedent surfaces unasked, recycled as context. The graph compounds with every call. That loop is the moat.
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.
An index change forces a rebalance, and the desk has to choose how to trade it.
The desk sees the constraint, the tracking error it is defending, and the last comparable action.
Trade in full at the close. Stage it over three days. Seek an exemption from the constraint.
Compliance blocks the full size pre-trade. The desk records why it wanted the size, not just that it was stopped.
Staged over three days, with the pre-trade override and its reason captured alongside the order.
Implementation shortfall is measured against the reasoning, not only against the benchmark.
Pre-trade override is the one place asset management already captures divergence. It is almost never kept in a form anyone can query a year later.
A water damage claim, £9,000 reserved, loss dated in December.
The brief arrives with the policy terms, the reserve, and the nearest past claims already attached.
Settle at £8,400 on three matching precedents. Decline on the exclusion. Refer for inspection.
The handler pushes back on settling: a suspected gradual leak, which the policy excludes, and this book keeps producing them in winter.
Referred, not settled. The reason is recorded against the exclusion, attributed, and sealed as it is made.
Inspection confirms the leak. The decline holds, and the three precedents that argued for paying are scored down for claims like this one.
The FCA’s review of home and travel claims handling found committee minutes that lacked the detail to prove meaningful discussion, challenge or decision-making had happened at all.
Renew the treaty, restructure it, or take it to market.
Last year’s terms arrive with the reasoning that produced them, not just the slip.
Renew as expiring. Lift the retention. Restructure the layer and re-broke it.
The actuary challenges the retention on two development years the original pricing never saw.
Retention lifted and the layer restructured, with the trade-off recorded as it was argued.
Judged when the development is in, against the case that was actually made at renewal.
Treaties renew annually and the reasoning resets annually. The fourth renewal should not start from a blank page.
A borrower trips a covenant, and the committee has to decide what follows.
The memo assembles the facility terms, the breach itself, and how comparable breaches went.
Waive it. Waive with a fee and tighter reporting. Call the default.
Credit pushes back on a clean waiver, citing two comparable waivers that ran on into a restructuring.
Waived with a fee and monthly reporting, the conditions attributed and dated.
What the borrower did next, tied back to the waiver that allowed it.
Waivers set precedent whether or not anyone writes them down. The next committee inherits the precedent without the reasoning.
Cut the property allocation, or hold it.
The papers, the exposure and the last three related calls reach the room before the meeting does.
Hold at twelve percent. Cut to seven. Cut to zero and re-enter later.
Risk pushes back on the full exit: liquidity in the vehicle is worse than the paper assumes, so the terms change.
Cut to seven, conditional on a six month liquidity review, attributed to the committee and to its dissent.
Reviewed twelve months on against what the room expected, not against what everyone now remembers.
Five years later someone asks why. Today that answer is a search through minutes and mailboxes, and the person who knew has left.
A risk comes in outside appetite, and someone has to price it or decline it.
The underwriter gets the guide, the exposure, and how the book handled the nearest matches.
Decline. Write it at the guide rate. Write it with a loading and a warranty.
The referral pushes for the loading: the two nearest precedents both ran hot, and the record says so.
Written with a loading and a warranty, cited to the precedents and to the authority it was written under.
The loss ratio lands against the assumption that justified the price, not against a memory of it.
An appetite that drifts a little each quarter is invisible until the year it is not.
A model recommends a decline, and a reviewer has to decide whether to take it.
The reviewer sees the model version, the inputs it used, and the override history for models like it.
Accept the output. Override it. Send the case back for a second run on fuller data.
The reviewer overrides, citing a data gap the model cannot see. It is the eleventh override on this model this quarter.
Overridden, with a structured reason, against a named model version and a dated policy.
Outcomes are scored for accepts and overrides alike, so the honest question, who turned out right, has an answer.
SS1/23 expects override tracking and parallel outcome analysis. Of the eight model risk platforms most commonly shortlisted in 2026, none sells the record that would evidence it.
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.