Merger Intelligence
Role and responsibilities
Project context
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UX design: domain knowledge-based ideation, UX planning.
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Front end development: connecting APIs via Retool, creating Retool front-end components.
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February 2025 to May 2026
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Worked with team of 2 back-end engineers & 2 researchers.
Tools used
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Retool
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Claude
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Figma
Backstory
Merger Intelligence was developed to bring greater clarity to the unpredictable world of mergers and acquisitions (M&A). By aggregating global transaction data and analysing the factors influencing deal outcomes, the platform helps investors assess the likelihood of a merger closing and predict its completion date, transforming complex financial information into actionable intelligence.
At its core, Merger Intelligence is about making the unpredictable more understandable.
Problem
M&A transactions are inherently complex, shaped by regulatory approvals, market conditions, and shifting deal dynamics. Investors often rely on fragmented information, time-consuming research, and subjective judgement to evaluate deal outcomes. The challenge was to consolidate these insights into an intuitive, trustworthy experience that enables users to quickly understand a deal's status, assess its risks, and make informed decisions with confidence.
How might we transform complex, fragmented M&A data into clear, actionable insights that help investors navigate uncertainty and make more confident decisions?
Solution
Main Dashboard
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Global Deal Overview: Centralised view of M&A transactions, deal status, conviction scores, and predicted closing dates.
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Advanced Search & Filtering: Quickly discover and segment transactions using customisable filters.
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Deal Watchlists: Organise and monitor transactions relevant to investment strategies.
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News & Milestone Tracking: Stay informed through integrated news, regulatory updates, and upcoming deal events.
Deal Intelligence
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Comprehensive Deal Profiles: Access transaction details, financial terms, and key deal information in one place.
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Regulatory Approval Tracking: Monitor approval progress, outstanding conditions, and potential deal risks.
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Interactive Deal Timeline: Explore historical events, upcoming milestones, and critical deadlines.
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Financial & Market Insights: Analyse deal spreads, market data, and supporting documentation to evaluate opportunities.
AI Research Assistant
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Conversational Research: Ask complex M&A questions using natural language.
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Context-Aware Insights: Retrieve relevant information from deal data and supporting documents.
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Cross-Deal Comparison: Compare transactions, regulatory conditions, and timelines through AI-powered queries.
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Actionable Responses: Receive structured summaries with direct references to relevant transactions.
Refinery
Role and responsibilities
Project context
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UX design: domain knowledge-based ideation, UX planning.
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UX research: user research, usability testing.
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February 2024 to February 2025
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Worked with team of 5 back-end and 1 front-end engineer
Tools used
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Figma
Backstory
As Intropic's data ecosystem grew, so did the complexity of managing the data that powered its business units. Teams relied on a growing network of data pipelines, services and infrastructure to build and deliver data products, but fragmented workflows and technical dependencies made it increasingly difficult to work efficiently and independently.
Refinery was established as Intropic's internal data infrastructure team, with a mission to transform how business units create, maintain and consume data products. By building a more unified, reliable and scalable data infrastructure, Refinery aimed to simplify complex technical processes, empower teams with greater autonomy and make high-quality data more accessible across the organisation.
Problem
As data infrastructure became more complex, business units faced challenges in understanding, monitoring and managing their data products. Limited visibility into data pipelines, fragmented tooling and reliance on engineering expertise created operational bottlenecks, making it difficult to identify issues, troubleshoot failures and maintain reliable data flows.
How might we make complex data infrastructure more accessible, transparent and intuitive, empowering business units to independently create, manage and consume reliable data products?
Features Designed
1. Message Queue
2. Secrets Management
3. Product Catalogue
4. Data Product Analytics
5. Service Page
