Fragmented information
Portfolio data, MiFID profile, CRM, documentation, interaction history and market data live in separate systems. The advisor manually rebuilds a view that should already be available before each client conversation.
It turns the data your institution already has, portfolio, MiFID profile, market and internal rules, into priorities, opportunities, analysis, reports and explained proposals for the advisor. Read-only and with traceability.
Advisors work across fragmented systems, rising commercial pressure and growing regulatory demands. The problem is not a lack of data. It is turning data into useful context before each decision, meeting or proposal.
Portfolio data, MiFID profile, CRM, documentation, interaction history and market data live in separate systems. The advisor manually rebuilds a view that should already be available before each client conversation.
Meeting preparation, information gathering, analysis and reporting consume hours that the advisor cannot dedicate to the client relationship or commercial activity. Every operational hour has a measurable cost.
MiFID II, DORA and GDPR are not a future issue. They are an operational reality that requires every proposal to explain where the data came from, which limits were applied and which information supported it. The AI Act adds another layer for AI systems.
Financenova connects to your systems in read-only mode, unifies client information and gives the advisor a working environment with an AI agent system inside. Without replacing anything, without touching the core, within the institution’s perimeter.
360 view, automated brief, proposals, reporting
Data unification + Agentnova + native compliance
No alteration. Read-only integration.
Positions, products, documentation, interaction history and mandate. All client information unified into an actionable view for the advisor.
Specialised agents monitor portfolios and markets, generate briefs before each meeting, prioritise commercial opportunities and produce narrative reporting.
Reports generated instantly, in institutional language, explainable step by step and ready to present to the client or take to committee.
MiFID II, DORA and GDPR are integrated from the design stage. Every recommendation, interaction and decision is recorded with auditable evidence.
At the start of the day, the advisor does not find a tray full of data. They find prioritised clients, explained reasons and next steps prepared with traceable sources.
Agentnova is the agent system that operates inside Financenova. It analyses institution-authorised information, cites sources, prepares context and explains the reasoning. The advisor always keeps the final decision.
Works across portfolio, CRM, MiFID profile, documentation, market data and internal rules.
Every answer must show where the underlying information comes from.
Agentnova helps prepare, explain and document. It does not replace professional judgement.
Answers must rely on data, rules and traceability, not on model intuition.
Financenova is built with clear boundaries: it does not replace the advisor, it does not replace the core, and it does not produce conclusions without context, rules and verifiable sources.
LLMs are the engine. Financenova is the platform that makes them usable in banking environments.
It is not about whether to use ChatGPT, Copilot or another model. The numbers and calculations are never produced by the LLM: they come from Financenova’s business logic over real data, and the model only writes and explains that verified context. That means 0% invention in data and figures.
The difference is not the model. It is the platform that governs it.
Financenova is in a pre-commercial phase. These are the impact targets we designed to prove in a pilot, measured with your real data from day one.
Each target is agreed with a baseline and measured with your real data during the pilot, with full traceability of how it is calculated. What we prove, we report to management.
Less manual prep, more time with clients.
Liquidity, maturities and deviations caught in time.
Sources and reasoning documented in every proposal.
In an initial conversation, we can review the use case, available data sources, technical perimeter and a potential focused POC for private banking or wealth management.
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