2025
·
Finance
Designed a GenAI-powered knowledge mining platform for a venture capital and private equity firm to improve how internal knowledge, research, and insights are captured, synthesised, and used across investment workflows.
The work spanned product design, UX strategy, interaction design, and early-stage AI-assisted experience design within a fast-moving MVP environment.
Investment teams operate in information-dense environments — constantly working with fragmented data across reports, decks, conversations, and external sources. Much of this knowledge remains unstructured and difficult to reuse, making it hard to connect insights across deals, track evolving narratives, or move efficiently from research to decision-making.
The platform was built to bring structure to this process — enabling teams to extract insights, synthesise information, and generate structured outputs without losing context. It combined conversational AI interactions with document generation workflows, supporting both exploratory thinking and more structured, output-driven use cases.
The product was designed around how analysts and partners actually work. This meant supporting two key modes of behaviour — deep, focused exploration and side-by-side comparison — allowing users to move fluidly between analysing a single narrative and evaluating multiple perspectives.
Features like multi-threaded interactions, comparison views, and iterative document building were introduced to support these workflows.
A key part of the exploration involved understanding how generative AI could fit into real investment workflows — not by replacing judgement, but by helping users structure, refine, and articulate their thinking.
The system allowed users to move from scattered notes to more coherent narratives, generate working drafts of reports, and collaborate on them within the platform.
We also explored early multi-agent interaction patterns, where different AI agents could support different modes of thinking — from analysis and synthesis to simulation and scenario exploration. These were intentionally kept lightweight and flexible, allowing users to manually switch contexts while validating how such patterns might evolve in more mature systems.
The focus throughout was on speed, clarity, and usability within a high-stakes environment — designing a system that could handle complexity while still feeling intuitive, responsive, and aligned with how teams already think and operate.
Working on something complex, ambitious, or hard to get right?
I’d love to hear what you’re building.
Most of my work sits with teams solving complex problems — where design needs to hold up as products evolve and companies scale. If that’s what you’re working on, we’ll likely get along well.
