What we'll unpack
What lies beneath the model
Models are the visible part. What sits beneath them decides whether they are any use.
Models are only the visible part of the AI wave. Their usefulness depends on the infrastructure beneath them: search, retrieval, knowledge graphs, vector stores, metadata, institutional memory and the systems connecting it all.
We will examine which architectural approaches are proving credible inside real banks — and where the elegant diagrams begin to meet operational reality.
Whose knowledge, and who can access it
One answer can cross four departments, each governed by different permissions.
A single answer may draw from client records, internal policies, investment research and decisions made across several departments — each governed by different permissions.
We will explore how identity, entitlements, data ownership, provenance and access controls can follow every request, retrieval and response. Without those foundations, the context layer does not become shared intelligence. It becomes a new way to expose information.
What breaks at scale
A prototype that works is not yet institutional infrastructure.
A successful prototype is not yet institutional infrastructure.
What happens when thousands of employees and agents query the same context layer? How does the bank keep knowledge current, answers traceable, permissions consistent and costs sustainable? And which failures only become visible once the system moves beyond a controlled pilot?
This is a discussion about the problems that rarely appear in AI demonstrations — or in published case studies.
Speakers
Tim O'Hear
Opening talkHead of AI Solutions, Switzerland
Vector8
View bio
Marc Maret
ModeratorCo-founder & CEO
Vector8
View bio
Arthur Orts
PanellistHead of Agentic AI
Pictet Asset Management
View bio
Pierre-Olivier Trabichet
PanellistChief Data Officer / Head of AI, Data & Innovation
Lombard Odier
View bio
Fabien Saint-Roch
PanellistManaging Director, Head of Digital, Innovation & Data
Union Bancaire Privée
View bioHow the night unfolds
Doors open & networking
We let AI agents build a web search engine — inside Vector8's open source web index: designed for agents, developed by agents (Tim O'Hear, Vector8)
Panel — building one context layer across the silos of a bank, moderated by Marc Maret, Vector8 (50 min)
Open floor — questions and best practices from the room (20 min)
Apéro & networking — at La Maison Rouge, Rue des Noirettes 17, 1227 Carouge
Convened & moderated by
Vector8
Apéro sponsored by
Vector8
La Maison Rouge, Rue des Noirettes 17, 1227 Carouge
About this event
Vector8 let AI agents build a web search engine. The evening opens on that open source index — designed for agents, developed by agents — and then turns to the harder question it raises for a bank: the architecture that determines what an AI agent knows, where that knowledge comes from, and what it is allowed to reveal.
Every answer an agent gives is a decision about who was allowed to know what.
This is the architecture of knowing: a trusted context layer connecting the bank’s documents, decisions, client history and institutional memory — while applying the right permissions to every question and every answer. Building it means confronting difficult choices around technology, governance, ownership, traceability and scale.
This session brings together senior banking and AI leaders to explore what works in practice. Which technologies hold up inside a real bank? How can agents work across organisational silos without crossing access boundaries? And what begins to break when the system expands across the institution?
Because giving AI more context also gives it more power. The objective is not simply to help agents know more — it is to ensure they know the right things, for the right reasons, on behalf of the right people.
The evening belongs to a continuing series on the transformation of banking, following AI-DLC and The unbearable lightness of banking.
Join the conversation
November 10, 2026 · 6:30 PM CET
Auditoire Pictet de Rochemont, Route des Acacias 66, Carouge
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