Questions & Answers

A classic SaaS executes pre-programmed actions: if X, then Y. An AI agent, on the other hand, receives an objective, understands the context, plans its steps and executes a sequence of actions, adapting to the data it encounters. This autonomy is both its interest and its risk.
In a fund, the relevant use cases for an agent are well-defined: processing supplier emails, enriching a deal sheet, automatically preparing a committee pack from a data room, monitoring signals on target companies. Ill-framed cases (making an investment decision, sending a communication to an LP without proofreading) are not cases for agents, they are cases for assisted humans.
A good agent has four characteristics: a clear perimeter, human supervision at every sensitive stage, complete traceability, and reversibility in the event of unexpected behavior. Bodic develops its agents according to these principles as part of its customized developments.

Hallucinations are the main risk when using AI agents in a financial context. They are not the result of random errors, but of the structural behavior of models in the absence of reliable or sufficiently constrained information.
Three levels of control can greatly reduce this risk.
The first is strict anchoring on a certified source of truth. The agent must not rely on general knowledge or implicit data, but only on a controlled internal repository. Each answer must be associated with an identifiable, accessible and verifiable source. An unsourced answer must be considered invalid by default.
The second level is the restriction of the functional and informational perimeter. An agent must intervene in a precise domain, with a limited and controlled set of data. The wider the scope, the greater the risk of approximate interpretation. In practice, an agent who specializes in a subset of financial data, such as a shareholding's KPIs or Middle Office flows, is significantly more reliable than a generalist agent.
The third level is the implementation of systematic controls on outputs. All numerical information intended for external use, in particular for investors, must be validated by a human being. This validation must be based on complete traceability: initial request, sources used, transformations applied and response generated.
Beyond these principles, technical architecture plays a decisive role. A structured approach consists in isolating the AI layer from raw data, using a centralized repository, then exposing only validated data to agents. This allows precise control over what the agent can see and use.
Finally, it's important to treat hallucinations as a governance issue, not just a technical problem. This means defining rules of use, levels of responsibility and supervisory mechanisms adapted to the financial stakes involved.
A reliable agent is not one that "responds well", but one whose every response can be explained, traced and verified.

Structuring a robust data chain in a management company involves making the circulation of information explicit, controlled and reliable, from its production to its final use.
In concrete terms, this involves formalizing several key stages: identifying data sources (emails, files, portals, APIs), defining storage spaces (internal databases, data warehouse, business tools), organizing transformations (cleansing, enrichment, consolidation), then structuring distribution to end-users (reporting, committees, investor communication, regulatory obligations).
A robust data chain is based on a few fundamental principles.
First and foremost, each piece of critical data must be clearly defined: an identified source, a reference format, an update frequency and a person in charge. Without this discipline, gaps quickly appear between teams, tools and deliverables.
Next, it's essential to limit redundancy. The multiplication of Excel files, local extractions or parallel versions creates inconsistencies and undermines confidence in the figures. The aim is to converge towards a shared, accessible and controlled "source of truth".
Traceability is also central. Every figure used in a report or committee must be traceable back to its origin, with a history of transformations. This becomes critical as LP and regulatory requirements increase.
Finally, a robust chain includes control mechanisms: validation rules, alerts in the event of anomalies, and human supervision of sensitive points. This framework ensures quality without slowing down operations.
The challenge goes far beyond the technical. A well-structured data chain improves the quality of reporting, facilitates collaboration between teams (investment, IR, middle office, compliance), strengthens credibility with investors and accelerates decision-making.
It's also a prerequisite for the effective deployment of AI tools. Without structured, reliable and governed data, AI amplifies existing shortcomings instead of creating value.