Design blueprint for a knowledge graph-driven intelligence platform

A self-initiated knowledge graph design for a private equity intelligence platform, modelling the relationships between investors, fund managers, funds, deals, people, events, and editorial content, then showing what those relationships make possible in the product. Developed while commissioned to PEI Group as Senior Information Architect.

Overview

Intelligence platforms often contain vast amounts of information but struggle to reveal meaningful insight. The problem is rarely a lack of data. More often, it is that the relationships within the data remain implicit.

This project explored how a knowledge graph could restructure the underlying information model of a private equity intelligence platform. By explicitly modelling entities and relationships — firms, funds, investors, deals and strategies — the platform could move beyond static lists of content and begin to reveal the structure of the market itself.

The work demonstrates how information architecture, conceptual modelling and graph-based data design can enable richer discovery, exploration and AI-assisted analysis.

The structural problem

The platform already held a large amount of valuable information about the private equity ecosystem: general partners, limited partners, funds, deals, strategies, sectors and regions.

But this information existed largely as isolated records.

Firms appeared in one place, deals in another, investors in another again. Articles referenced these entities, but the connections between them were not consistently modelled.

As a result, the platform behaved more like a library of documents than an intelligence system.

Users could retrieve individual pieces of information, but understanding the relationships between them required manual effort.

Questions that investors naturally ask — such as:

• Which managers specialise in infrastructure strategies in Northern Europe?

• Which investors frequently co-invest together?

• Which firms have recently shifted strategic focus?

were difficult to answer because the relationships needed to surface those insights were not explicitly represented.

The information existed.
The platform simply could not connect it.

The design insight

In analysing the domain, a pattern emerged in how users think about investment markets.

They rarely begin with a single entity. Instead, they think in terms of intersecting dimensions:

• Strategy – what type of investment activity is being pursued

• Sector – the industries involved

• Region – the geographic focus

These three dimensions — strategy, sector and region — form a natural conceptual anchor for understanding private equity activity.

Instead of organising the platform primarily around documents or isolated entities, the system could instead organise knowledge around these intersecting dimensions of the market.

This insight provided the conceptual foundation for the knowledge graph.

The architectural move

A graph model allows entities and relationships to be represented explicitly.

Instead of storing information as disconnected records, the system represents the market as a network of connected objects.

Key entity types include:

• General partners (GPs)

• Limited partners (LPs)

• Funds

• Deals

• Companies

• People

• Articles and events

These entities are connected through named relationships such as:

• manages

• invests in

• participated in

• focuses on sector

• operates in region

The resulting structure is a network of explicit relationships rather than a collection of isolated entries.

In practical terms, this means the system can traverse connections between entities and reveal patterns that would otherwise remain hidden.

Instead of retrieving documents, the platform can begin to explore a market.

Key modelling decisions

Several modelling decisions were particularly important in shaping the graph.

Strategy drift and investment intent

Investment strategies are not static. Firms evolve, launch new funds and shift focus over time.

The model therefore allows strategies to exist as distinct entities connected to both funds and firms. This enables the system to detect patterns such as strategic expansion or drift across a firm’s portfolio of funds.

By representing these relationships explicitly, the platform can surface signals about how investment strategies are evolving within the market.

Editorial knowledge as structured relationships

Much of the platform’s insight originates in editorial content — articles, reports and commentary.

Traditionally, these pieces exist as text documents referencing firms and deals informally.

In the graph model, these references become structured relationships. Articles are connected directly to the entities they discuss.

This transforms editorial content from isolated narratives into part of the platform’s knowledge structure, allowing insights described in text to become navigable data.

Events and networks

The private equity industry is heavily relationship-driven. Conferences, panels and events reveal important networks between participants.

The graph allows events and participants to be represented as entities connected through participation relationships.

Over time, this structure makes it possible to explore professional networks and recurring collaborations across the industry.

Product capabilities unlocked

Once relationships are modelled explicitly, the platform can support entirely new forms of exploration.

Several potential product experiences emerged from the prototype.

GP intelligence dashboards

A firm profile can move beyond static descriptive information to show the network surrounding that firm:

• funds managed

• investors connected to those funds

• deals executed

• sectors and regions of activity

This creates a dynamic view of a firm’s activity within the broader market.

Market exploration

Instead of browsing lists of firms or deals, users can explore the market by traversing connections:

• from sector to active funds

• from funds to participating investors

• from investors to other firms they back

The graph structure makes these movements natural and discoverable.

Article intelligence

Because articles are connected directly to entities, users can move seamlessly between editorial insight and structured market data.

A piece of analysis discussing a firm’s strategy can become an entry point into the network of funds, investors and deals associated with that strategy.

Network discovery

The graph makes it possible to identify patterns across the ecosystem, such as:

• frequently collaborating investors

• emerging clusters of investment activity

• firms expanding into new sectors or regions

These patterns are difficult to detect when information is stored only as documents or tables.

What this work demonstrates

This exploration illustrates a broader design principle.

Intelligence platforms depend not only on the information they contain but on how relationships within that information are represented.

When relationships remain implicit, products tend to rely on lists, search and manual interpretation.

When relationships are made explicit, the same information can support discovery, exploration and analysis.

In this sense, the knowledge graph is not merely a technical implementation. It is a design decision about how knowledge is structured.

Instead of static lists of content, a graph-powered platform can show the structure of a market.

Reflection

This work sits at the intersection of information architecture, conceptual modelling and data product design.

It demonstrates how designing the structure of information — entities, relationships and vocabulary — can fundamentally change the capabilities of a digital product.

For complex domains such as private equity, where understanding relationships is central to analysis, the architecture of knowledge becomes as important as the interface through which users interact with it.