Organising Intelligence for Smarter Communication
All Family offices are unique but share the same common building blocks. Utilising data, both quantitative and qualitative, to communicate narratives that drive strategic decisions.
At Vesimi, we help integrate, into your communication strategy, an AI-powered engine that transforms fragmented, unstructured information into organised, decision-ready intelligence.
In this post, we walk through the architecture that powers this transformation, showing how we bring together multiple technologies into a single coherent process.
Breaking Down the AI Process
Our backend process integrates a series of modular components, each playing a specific role in the conversion of messy data into structured insight. Here’s how they fit together:
1. Natural Language Processing (NLP)
What It Does:
NLP interprets unstructured text, like investment reports, emails, meeting notes, and legal documents and extracts key entities (e.g. names of companies, asset classes, sectors, geographies).
(In plain terms: it reads documents and pulls out the important, contextual details.)
Why It Matters:
This step turns free-form language into recognizable data points. It lays the groundwork for connecting and contextualising information in later steps.
2. Retrieval-Augmented Generation (RAG)
What It Does:
RAG blends search and summarisation. It locates relevant sections across large documents and condenses them into concise, digestible outputs.
(Simply, it finds the right parts of your documents and rewrites them in plain language for quick understanding.)
Why It Matters:
RAG improves relevance and reduces noise. Rather than reading entire files, users or systems can work directly with what matters most, whether that’s a comment on a market trend or a clause in a shareholder agreement.
3. Knowledge Graphs
What They Do:
Knowledge Graphs establish structured relationships between entities, such as “invested in,” “correlated with,” or “was exited at.”
(Basically: they show how all your data points connect in a map-like structure.)
Why It Matters:
Without structure, extracted data remains flat. Graphs create dimensionality, enabling us to uncover patterns. For example, shared exposures across asset classes or overlapping counterparty relationships that wouldn’t be visible from isolated facts.
4. Data Pools
What They Are:
Data Pools are the organised, queryable repositories where enriched information, extracted, summarised, and connected is stored.
(Imagine a highly structured library that lets you search by concept, not just keywords.)
Why It Matters:
Data Pools serve as the foundation for real-time analysis, portfolio overviews, and strategic decision support. They enable a single source of truth across the Family Office, cutting through silos and manual digging.
5. Large Language Models (LLMs)
What They Do:
LLMs generate human-like narratives from structured data, helping make sense of complex relationships and trends.
(In other words, they explain your data in plain English.)
Why It Matters:
Once the backend is complete, LLMs allow decision-makers to engage with the data naturally, asking questions like “Where are our indirect risks in Southeast Asia?” and getting context-rich answers drawn directly from their internal knowledge base.
Final Thoughts
At Vesimi, we don’t just plug in AI, we design the structure that makes AI valuable. This means turning scattered notes, legacy documents, and siloed files into a connected system where every insight is traceable, contextual, and ready to inform action. By weaving together NLP, RAG, Knowledge Graphs, Data Pools, and LLMs, we give Family Offices a stable foundation that supports fast, accurate, and forward-looking decision-making.
