Portfolio intelligence solution for a well-established UK private equity firm
We built a natural-language intelligence layer for private equity portfolio monitoring that securely connects portfolio data with external context.
This gives investment teams a single place to investigate performance, understand why changes are happening, and identify next actions - cutting time-to-insight, reducing reliance on data teams, and increasing trust through fully inspectable analysis.
Challenge:
Our client wanted to move from "what" is happening in their portfolio to "why", but the path to an answer was too slow.
Non-technical users couldn't easily self-serve. Understanding performance required manual analysis across disconnected spreadsheets and systems, often involving a data specialist.
For any portfolio team, speed is essential. Because context and prior analyses were scattered across emails and decks, root-cause investigations took longer than they should.
Without a way to centralise this logic, insight generation remained bottlenecked and teams were forced to repeatedly rebuild the same work.
Solution:
We created a natural language intelligence layer that bridges the gap between raw data and decision-making. Instead of navigating complex systems, users ask questions and receive immediate, data-grounded answers.
The system is built on complete transparency. It reveals the step-by-step logic used to reach an answer, allowing users to verify the data and export results directly to charts or Excel.
By connecting structured portfolio data with context from news, filings, and internal documents, the platform doesn't just show what changed - it explains the "why." This allows teams to move faster, reducing manual overhead and ensuring every decision is backed by the full picture.
Core impact:
Faster insights: Transformed portfolio monitoring into an interactive investigation, significantly reducing time-to-insight for underperformance and variance analysis.
Reduced bottlenecks & greater trust: Enabled self-serve analysis for non-technical users while maintaining transparency, with inspectable queries and results that build confidence in conclusions.
Better reuse & continuity: Captures context and analytical patterns within the workflow, making investigations repeatable and preventing knowledge from being lost across tools and documents.